davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
ovior/twitter_dataset_1713157533	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2306647 num_examples: 7035 download_size: 1317830 dataset_size: 2306647 configs: - config_name: default data_files: - split: train path: data/train-* ---
FelipeBandeiraPoatek/invoices-donut-data-v2	--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 234466949.0 num_examples: 425 - name: test num_bytes: 15053216.0 num_examples: 26 - name: validation num_bytes: 26678659.0 num_examples: 50 download_size: 197788456 dataset_size: 276198824.0 --- # Dataset Card for "invoices-donut-data-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Mike0307/nli-zh-tw-crosslingual	--- dataset_info: features: - name: text1 dtype: string - name: text2 dtype: string - name: label dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 57716492 num_examples: 141844 - name: test num_bytes: 6397485 num_examples: 15958 - name: validate num_bytes: 7902555 num_examples: 19503 download_size: 56543046 dataset_size: 72016532 --- # Dataset Card for "nli-zh-tw-crosslingual" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nateraw/fuego-20230208-175631-15666f	--- tags: - fuego fuego: id: 20230208-175631-15666f status: done script: main.py requirements_file: requirements.txt space_id: nateraw/fuego-20230208-175631-15666f space_hardware: cpu-basic github_repo_id: pytorch/examples github_repo_branch: main github_repo_sha: d8456a36d1bbb22f72b003f59406a19a0a0547c3 ---
explodinggradients/hindi-wikipedia	--- license: apache-2.0 ---
CyberHarem/sue_fireemblem	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sue (Fire Emblem) This is the dataset of sue (Fire Emblem), containing 28 images and their tags. The core tags of this character are `headband, long_hair, green_hair, green_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:----------\|:----------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 28 \| 23.10 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sue_fireemblem/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 28 \| 16.82 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sue_fireemblem/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 45 \| 26.53 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sue_fireemblem/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 28 \| 21.44 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sue_fireemblem/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 45 \| 32.40 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sue_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/sue_fireemblem', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------\| \| 0 \| 28 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, fingerless_gloves, bow_(weapon), holding_weapon \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| fingerless_gloves \| bow_(weapon) \| holding_weapon \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------------\|:---------------\|:-----------------\| \| 0 \| 28 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \|
coreybrady/blackwoods	--- license: cc-by-nc-4.0 ---
fengtc/para-train-1.1M	--- dataset_info: features: - name: kind dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1116096350 num_examples: 1649399 download_size: 797767383 dataset_size: 1116096350 configs: - config_name: default data_files: - split: train path: data/train-* ---
Multimodal-Fatima/OxfordFlowers_test_facebook_opt_350m_Attributes_Caption_ns_6149	--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_3_bs_16 num_bytes: 272760304.375 num_examples: 6149 - name: fewshot_0_bs_16 num_bytes: 267303635.375 num_examples: 6149 - name: fewshot_1_bs_16 num_bytes: 269129477.375 num_examples: 6149 download_size: 796845849 dataset_size: 809193417.125 --- # Dataset Card for "OxfordFlowers_test_facebook_opt_350m_Attributes_Caption_ns_6149" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alvarobartt/ultrafeedback-binarized-preferences-clean	--- dataset_info: features: - name: source dtype: string - name: instruction dtype: string - name: models sequence: string - name: completions list: - name: annotations struct: - name: instruction_following struct: - name: Rating dtype: string - name: Rationale dtype: string - name: honesty struct: - name: Rating dtype: string - name: Rationale dtype: string - name: truthfulness struct: - name: Type sequence: string - name: Rationale dtype: string - name: Rating dtype: string - name: Rationale For Rating dtype: string - name: helpfulness struct: - name: Type sequence: string - name: Rationale dtype: string - name: Rating dtype: string - name: Rationale For Rating dtype: string - name: custom_system_prompt dtype: string - name: model dtype: string - name: principle dtype: string - name: response dtype: string - name: critique dtype: string - name: overall_score dtype: float64 - name: correct_answers sequence: string - name: incorrect_answers sequence: string splits: - name: train num_bytes: 831088377.5421702 num_examples: 63136 download_size: 318279041 dataset_size: 831088377.5421702 configs: - config_name: default data_files: - split: train path: data/train-* ---
AlekseyKorshuk/lmeh-chai-synthetic	--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: test num_bytes: 47839521 num_examples: 4570 download_size: 15451629 dataset_size: 47839521 --- # Dataset Card for "lmeh-chai-synthetic" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Treelar/plant_village	--- license: cc0-1.0 dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Apple___Apple_scab '1': Apple___Black_rot '2': Apple___Cedar_apple_rust '3': Apple___healthy '4': Background_without_leaves '5': Blueberry___healthy '6': Cherry___Powdery_mildew '7': Cherry___healthy '8': Corn___Cercospora_leaf_spot Gray_leaf_spot '9': Corn___Common_rust '10': Corn___Northern_Leaf_Blight '11': Corn___healthy '12': Grape___Black_rot '13': Grape___Esca_(Black_Measles) '14': Grape___Leaf_blight_(Isariopsis_Leaf_Spot) '15': Grape___healthy '16': Orange___Haunglongbing_(Citrus_greening) '17': Peach___Bacterial_spot '18': Peach___healthy '19': Pepper,_bell___Bacterial_spot '20': Pepper,_bell___healthy '21': Potato___Early_blight '22': Potato___Late_blight '23': Potato___healthy '24': Raspberry___healthy '25': Soybean___healthy '26': Squash___Powdery_mildew '27': Strawberry___Leaf_scorch '28': Strawberry___healthy '29': Tomato___Bacterial_spot '30': Tomato___Early_blight '31': Tomato___Late_blight '32': Tomato___Leaf_Mold '33': Tomato___Septoria_leaf_spot '34': Tomato___Spider_mites Two-spotted_spider_mite '35': Tomato___Target_Spot '36': Tomato___Tomato_Yellow_Leaf_Curl_Virus '37': Tomato___Tomato_mosaic_virus '38': Tomato___healthy splits: - name: train num_bytes: 872351166.322 num_examples: 55447 download_size: 860727476 dataset_size: 872351166.322 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-staging-eval-big_patent-y-7d0862-15806178	--- type: predictions tags: - autotrain - evaluation datasets: - big_patent eval_info: task: summarization model: pszemraj/led-base-book-summary metrics: [] dataset_name: big_patent dataset_config: y dataset_split: test col_mapping: text: description target: abstract --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: pszemraj/led-base-book-summary * Dataset: big_patent * Config: y * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
toloka/TolokerGraph	--- license: cc-by-4.0 tags: - toloka - graph - node-classification pretty_name: Graph of Crowd Annotators size_categories: - 10K<n<100K task_categories: - graph-ml dataset_info: features: - name: id dtype: int32 - name: approved_rate dtype: float - name: skipped_rate dtype: float - name: expired_rate dtype: float - name: rejected_rate dtype: float - name: education dtype: string - name: english_profile dtype: bool - name: english_tested dtype: bool - name: banned dtype: bool --- # Toloker Graph: Interaction of Crowd Annotators ## Dataset Description - Repository: https://github.com/Toloka/TolokerGraph - Point of Contact: research@toloka.ai ### Dataset Summary This repository contains a graph representing interactions between crowd annotators on a project labeled on the [Toloka](https://toloka.ai/) crowdsourcing platform (see the [Toloka overview](https://toloka.ai/en/docs/guide/concepts/overview) for the details on the used terminology). The graph contains 11,758 nodes and 519,000 edges. Each node represents an individual annotator; nodes are provided with four numerical and three categorical features. An edge is drawn between a pair of annotators if they annotated the same task. Also, each node is provided with a label showing whether the annotator was banned on this project, or not. ### Nodes Nodes are stored in the [nodes.tsv](nodes.tsv) file in the TSV format of the following structure: - `id`: unique identifier of the annotator - `approved_rate`: percentage of the approved labels of this annotator - `skipped_rate`: percentage of the skipped tasks of this annotator - `expired_rate`: percentage of the expired tasks of this annotator - `rejected_rate`: percentage of the rejected labels of this annotator - `education`: level of education as self-reported by this annotator (`none`, `basic`, `middle`, `high`) - `english_profile`: knowledge of English as self-reported by this annotator (`0` for no, `1` for yes) - `english_tested`: whether the annotator passed the Toloka language test for English (`0` for no, `1` for yes) - `banned`: whether the annotator was banned on this project (`0` for no, `1` for yes) The `_rate` attributes should sum up to 1. ### Edges Edges are stored in the [edges.tsv](edges.tsv) file in the TSV format of the following structure: - `source`: source identifier of the annotator - `target`: target identifier of the annotator As the graph is undirected, `source` and `target` can be interchanged for the given pair of nodes. ### Citation Likhobaba, D., Pavlichenko, N., Ustalov, D. (2023). [Toloker Graph: Interaction of Crowd Annotators](https://doi.org/10.5281/zenodo.7620795). Zenodo. <https://doi.org/10.5281/zenodo.7620795> ```bibtex @dataset{Tolokers, author = {Likhobaba, Daniil and Pavlichenko, Nikita and Ustalov, Dmitry}, title = {{Toloker Graph: Interaction of Crowd Annotators}}, year = {2023}, publisher = {Zenodo}, doi = {10.5281/zenodo.7620795}, url = {https://github.com/Toloka/TolokerGraph}, language = {english}, } ``` ### Copyright Licensed under the Creative Commons Attribution 4.0 License. See LICENSE file for more details.
EduAlmeida/brendataset	--- license: openrail++ ---
Phando/vision-flan_191-task_1k	--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: task_name dtype: string - name: instruction dtype: string - name: response dtype: string splits: - name: train num_bytes: 33215298748.003 num_examples: 186103 download_size: 36889036585 dataset_size: 33215298748.003 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "vision-flan_191-task_1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberCoder	--- pretty_name: Evaluation run of LeroyDyer/Mixtral_AI_CyberCoder dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [LeroyDyer/Mixtral_AI_CyberCoder](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberCoder)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberCoder\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-08T15:32:36.734199](https://huggingface.co/datasets/open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberCoder/blob/main/results_2024-04-08T15-32-36.734199.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.49404669002437496,\n\ \ \"acc_stderr\": 0.03438132143312601,\n \"acc_norm\": 0.5005202795269466,\n\ \ \"acc_norm_stderr\": 0.03515083188087076,\n \"mc1\": 0.2802937576499388,\n\ \ \"mc1_stderr\": 0.015723139524608763,\n \"mc2\": 0.40415611292084586,\n\ \ \"mc2_stderr\": 0.014695681144184423\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5008532423208191,\n \"acc_stderr\": 0.014611369529813276,\n\ \ \"acc_norm\": 0.560580204778157,\n \"acc_norm_stderr\": 0.014503747823580122\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.54690300736905,\n \ \ \"acc_stderr\": 0.004967778940011932,\n \"acc_norm\": 0.7484564827723561,\n\ \ \"acc_norm_stderr\": 0.004330134219762844\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.42962962962962964,\n\ \ \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.42962962962962964,\n\ \ \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.5394736842105263,\n \"acc_stderr\": 0.04056242252249033,\n\ \ \"acc_norm\": 0.5394736842105263,\n \"acc_norm_stderr\": 0.04056242252249033\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.47,\n\ \ \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \ \ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.5735849056603773,\n \"acc_stderr\": 0.03043779434298305,\n\ \ \"acc_norm\": 0.5735849056603773,\n \"acc_norm_stderr\": 0.03043779434298305\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5416666666666666,\n\ \ \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.5416666666666666,\n\ \ \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.44,\n\ \ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5202312138728323,\n\ \ \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.5202312138728323,\n\ \ \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207763,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207763\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n\ \ \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.43829787234042555,\n \"acc_stderr\": 0.03243618636108102,\n\ \ \"acc_norm\": 0.43829787234042555,\n \"acc_norm_stderr\": 0.03243618636108102\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.34210526315789475,\n\ \ \"acc_stderr\": 0.04462917535336936,\n \"acc_norm\": 0.34210526315789475,\n\ \ \"acc_norm_stderr\": 0.04462917535336936\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3492063492063492,\n \"acc_stderr\": 0.024552292209342665,\n \"\ acc_norm\": 0.3492063492063492,\n \"acc_norm_stderr\": 0.024552292209342665\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.30158730158730157,\n\ \ \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n\ \ \"acc_norm_stderr\": 0.04104947269903394\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.3225806451612903,\n\ \ \"acc_stderr\": 0.02659308451657228,\n \"acc_norm\": 0.3225806451612903,\n\ \ \"acc_norm_stderr\": 0.02659308451657228\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.2955665024630542,\n \"acc_stderr\": 0.032104944337514575,\n\ \ \"acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.032104944337514575\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\"\ : 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.6363636363636364,\n \"acc_stderr\": 0.037563357751878974,\n\ \ \"acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.037563357751878974\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.6616161616161617,\n \"acc_stderr\": 0.03371124142626303,\n \"\ acc_norm\": 0.6616161616161617,\n \"acc_norm_stderr\": 0.03371124142626303\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7305699481865285,\n \"acc_stderr\": 0.032018671228777947,\n\ \ \"acc_norm\": 0.7305699481865285,\n \"acc_norm_stderr\": 0.032018671228777947\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.45384615384615384,\n \"acc_stderr\": 0.025242770987126177,\n\ \ \"acc_norm\": 0.45384615384615384,\n \"acc_norm_stderr\": 0.025242770987126177\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275794,\n \ \ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275794\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.5252100840336135,\n \"acc_stderr\": 0.03243718055137411,\n \ \ \"acc_norm\": 0.5252100840336135,\n \"acc_norm_stderr\": 0.03243718055137411\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\ acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.6495412844036698,\n \"acc_stderr\": 0.02045607759982446,\n \"\ acc_norm\": 0.6495412844036698,\n \"acc_norm_stderr\": 0.02045607759982446\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.39814814814814814,\n \"acc_stderr\": 0.033384734032074016,\n \"\ acc_norm\": 0.39814814814814814,\n \"acc_norm_stderr\": 0.033384734032074016\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.5833333333333334,\n \"acc_stderr\": 0.03460228327239172,\n \"\ acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.03460228327239172\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.6371308016877637,\n \"acc_stderr\": 0.03129920825530213,\n \ \ \"acc_norm\": 0.6371308016877637,\n \"acc_norm_stderr\": 0.03129920825530213\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.5829596412556054,\n\ \ \"acc_stderr\": 0.03309266936071721,\n \"acc_norm\": 0.5829596412556054,\n\ \ \"acc_norm_stderr\": 0.03309266936071721\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.5877862595419847,\n \"acc_stderr\": 0.04317171194870254,\n\ \ \"acc_norm\": 0.5877862595419847,\n \"acc_norm_stderr\": 0.04317171194870254\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7024793388429752,\n \"acc_stderr\": 0.04173349148083499,\n \"\ acc_norm\": 0.7024793388429752,\n \"acc_norm_stderr\": 0.04173349148083499\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6111111111111112,\n\ \ \"acc_stderr\": 0.04712821257426769,\n \"acc_norm\": 0.6111111111111112,\n\ \ \"acc_norm_stderr\": 0.04712821257426769\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.558282208588957,\n \"acc_stderr\": 0.03901591825836185,\n\ \ \"acc_norm\": 0.558282208588957,\n \"acc_norm_stderr\": 0.03901591825836185\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.6407766990291263,\n \"acc_stderr\": 0.04750458399041695,\n\ \ \"acc_norm\": 0.6407766990291263,\n \"acc_norm_stderr\": 0.04750458399041695\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7905982905982906,\n\ \ \"acc_stderr\": 0.026655699653922754,\n \"acc_norm\": 0.7905982905982906,\n\ \ \"acc_norm_stderr\": 0.026655699653922754\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.6475095785440613,\n\ \ \"acc_stderr\": 0.01708415024408138,\n \"acc_norm\": 0.6475095785440613,\n\ \ \"acc_norm_stderr\": 0.01708415024408138\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5867052023121387,\n \"acc_stderr\": 0.026511261369409244,\n\ \ \"acc_norm\": 0.5867052023121387,\n \"acc_norm_stderr\": 0.026511261369409244\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.22569832402234638,\n\ \ \"acc_stderr\": 0.013981395058455052,\n \"acc_norm\": 0.22569832402234638,\n\ \ \"acc_norm_stderr\": 0.013981395058455052\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.5326797385620915,\n \"acc_stderr\": 0.028568699752225875,\n\ \ \"acc_norm\": 0.5326797385620915,\n \"acc_norm_stderr\": 0.028568699752225875\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.5659163987138264,\n\ \ \"acc_stderr\": 0.028150232244535597,\n \"acc_norm\": 0.5659163987138264,\n\ \ \"acc_norm_stderr\": 0.028150232244535597\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.5308641975308642,\n \"acc_stderr\": 0.02776768960683393,\n\ \ \"acc_norm\": 0.5308641975308642,\n \"acc_norm_stderr\": 0.02776768960683393\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.38652482269503546,\n \"acc_stderr\": 0.029049190342543465,\n \ \ \"acc_norm\": 0.38652482269503546,\n \"acc_norm_stderr\": 0.029049190342543465\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3305084745762712,\n\ \ \"acc_stderr\": 0.012014142101842965,\n \"acc_norm\": 0.3305084745762712,\n\ \ \"acc_norm_stderr\": 0.012014142101842965\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.4007352941176471,\n \"acc_stderr\": 0.029768263528933116,\n\ \ \"acc_norm\": 0.4007352941176471,\n \"acc_norm_stderr\": 0.029768263528933116\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5,\n \"acc_stderr\": 0.020227834851568375,\n \"acc_norm\"\ : 0.5,\n \"acc_norm_stderr\": 0.020227834851568375\n },\n \"harness\|hendrycksTest-public_relations\|5\"\ : {\n \"acc\": 0.5545454545454546,\n \"acc_stderr\": 0.047605488214603246,\n\ \ \"acc_norm\": 0.5545454545454546,\n \"acc_norm_stderr\": 0.047605488214603246\n\ \ },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.6693877551020408,\n\ \ \"acc_stderr\": 0.03011642629654059,\n \"acc_norm\": 0.6693877551020408,\n\ \ \"acc_norm_stderr\": 0.03011642629654059\n },\n \"harness\|hendrycksTest-sociology\|5\"\ : {\n \"acc\": 0.5024875621890548,\n \"acc_stderr\": 0.03535490150137289,\n\ \ \"acc_norm\": 0.5024875621890548,\n \"acc_norm_stderr\": 0.03535490150137289\n\ \ },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\":\ \ 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.68,\n\ \ \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness\|hendrycksTest-virology\|5\"\ : {\n \"acc\": 0.42168674698795183,\n \"acc_stderr\": 0.03844453181770917,\n\ \ \"acc_norm\": 0.42168674698795183,\n \"acc_norm_stderr\": 0.03844453181770917\n\ \ },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.672514619883041,\n\ \ \"acc_stderr\": 0.035993357714560276,\n \"acc_norm\": 0.672514619883041,\n\ \ \"acc_norm_stderr\": 0.035993357714560276\n },\n \"harness\|truthfulqa:mc\|0\"\ : {\n \"mc1\": 0.2802937576499388,\n \"mc1_stderr\": 0.015723139524608763,\n\ \ \"mc2\": 0.40415611292084586,\n \"mc2_stderr\": 0.014695681144184423\n\ \ },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7142857142857143,\n\ \ \"acc_stderr\": 0.012696531870038621\n },\n \"harness\|gsm8k\|5\":\ \ {\n \"acc\": 0.15314632297194844,\n \"acc_stderr\": 0.009919728152791484\n\ \ }\n}\n```" repo_url: https://huggingface.co/LeroyDyer/Mixtral_AI_CyberCoder leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|arc:challenge\|25_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-08T15-32-36.734199.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|gsm8k\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hellaswag\|10_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-08T15-32-36.734199.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-08T15-32-36.734199.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '/details_harness\|winogrande\|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-08T15-32-36.734199.parquet' - config_name: results data_files: - split: 2024_04_08T15_32_36.734199 path: - results_2024-04-08T15-32-36.734199.parquet - split: latest path: - results_2024-04-08T15-32-36.734199.parquet --- # Dataset Card for Evaluation run of LeroyDyer/Mixtral_AI_CyberCoder <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [LeroyDyer/Mixtral_AI_CyberCoder](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberCoder) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberCoder", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-08T15:32:36.734199](https://huggingface.co/datasets/open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberCoder/blob/main/results_2024-04-08T15-32-36.734199.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.49404669002437496, "acc_stderr": 0.03438132143312601, "acc_norm": 0.5005202795269466, "acc_norm_stderr": 0.03515083188087076, "mc1": 0.2802937576499388, "mc1_stderr": 0.015723139524608763, "mc2": 0.40415611292084586, "mc2_stderr": 0.014695681144184423 }, "harness\|arc:challenge\|25": { "acc": 0.5008532423208191, "acc_stderr": 0.014611369529813276, "acc_norm": 0.560580204778157, "acc_norm_stderr": 0.014503747823580122 }, "harness\|hellaswag\|10": { "acc": 0.54690300736905, "acc_stderr": 0.004967778940011932, "acc_norm": 0.7484564827723561, "acc_norm_stderr": 0.004330134219762844 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.42962962962962964, "acc_stderr": 0.04276349494376599, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.04276349494376599 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5394736842105263, "acc_stderr": 0.04056242252249033, "acc_norm": 0.5394736842105263, "acc_norm_stderr": 0.04056242252249033 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5735849056603773, "acc_stderr": 0.03043779434298305, "acc_norm": 0.5735849056603773, "acc_norm_stderr": 0.03043779434298305 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.04166666666666665, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.04166666666666665 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5202312138728323, "acc_stderr": 0.03809342081273957, "acc_norm": 0.5202312138728323, "acc_norm_stderr": 0.03809342081273957 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.30392156862745096, "acc_stderr": 0.04576665403207763, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.04576665403207763 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.43829787234042555, "acc_stderr": 0.03243618636108102, "acc_norm": 0.43829787234042555, "acc_norm_stderr": 0.03243618636108102 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.34210526315789475, "acc_stderr": 0.04462917535336936, "acc_norm": 0.34210526315789475, "acc_norm_stderr": 0.04462917535336936 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.4689655172413793, "acc_stderr": 0.04158632762097828, "acc_norm": 0.4689655172413793, "acc_norm_stderr": 0.04158632762097828 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3492063492063492, "acc_stderr": 0.024552292209342665, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.024552292209342665 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.30158730158730157, "acc_stderr": 0.04104947269903394, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.04104947269903394 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.3225806451612903, "acc_stderr": 0.02659308451657228, "acc_norm": 0.3225806451612903, "acc_norm_stderr": 0.02659308451657228 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.2955665024630542, "acc_stderr": 0.032104944337514575, "acc_norm": 0.2955665024630542, "acc_norm_stderr": 0.032104944337514575 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.43, "acc_stderr": 0.04975698519562428, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6363636363636364, "acc_stderr": 0.037563357751878974, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.037563357751878974 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.6616161616161617, "acc_stderr": 0.03371124142626303, "acc_norm": 0.6616161616161617, "acc_norm_stderr": 0.03371124142626303 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7305699481865285, "acc_stderr": 0.032018671228777947, "acc_norm": 0.7305699481865285, "acc_norm_stderr": 0.032018671228777947 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.45384615384615384, "acc_stderr": 0.025242770987126177, "acc_norm": 0.45384615384615384, "acc_norm_stderr": 0.025242770987126177 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.24074074074074073, "acc_stderr": 0.026067159222275794, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.026067159222275794 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5252100840336135, "acc_stderr": 0.03243718055137411, "acc_norm": 0.5252100840336135, "acc_norm_stderr": 0.03243718055137411 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2980132450331126, "acc_stderr": 0.037345356767871984, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.037345356767871984 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.6495412844036698, "acc_stderr": 0.02045607759982446, "acc_norm": 0.6495412844036698, "acc_norm_stderr": 0.02045607759982446 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.39814814814814814, "acc_stderr": 0.033384734032074016, "acc_norm": 0.39814814814814814, "acc_norm_stderr": 0.033384734032074016 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.5833333333333334, "acc_stderr": 0.03460228327239172, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.03460228327239172 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6371308016877637, "acc_stderr": 0.03129920825530213, "acc_norm": 0.6371308016877637, "acc_norm_stderr": 0.03129920825530213 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.5829596412556054, "acc_stderr": 0.03309266936071721, "acc_norm": 0.5829596412556054, "acc_norm_stderr": 0.03309266936071721 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5877862595419847, "acc_stderr": 0.04317171194870254, "acc_norm": 0.5877862595419847, "acc_norm_stderr": 0.04317171194870254 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7024793388429752, "acc_stderr": 0.04173349148083499, "acc_norm": 0.7024793388429752, "acc_norm_stderr": 0.04173349148083499 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6111111111111112, "acc_stderr": 0.04712821257426769, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.04712821257426769 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.558282208588957, "acc_stderr": 0.03901591825836185, "acc_norm": 0.558282208588957, "acc_norm_stderr": 0.03901591825836185 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6407766990291263, "acc_stderr": 0.04750458399041695, "acc_norm": 0.6407766990291263, "acc_norm_stderr": 0.04750458399041695 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7905982905982906, "acc_stderr": 0.026655699653922754, "acc_norm": 0.7905982905982906, "acc_norm_stderr": 0.026655699653922754 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.6475095785440613, "acc_stderr": 0.01708415024408138, "acc_norm": 0.6475095785440613, "acc_norm_stderr": 0.01708415024408138 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5867052023121387, "acc_stderr": 0.026511261369409244, "acc_norm": 0.5867052023121387, "acc_norm_stderr": 0.026511261369409244 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.22569832402234638, "acc_stderr": 0.013981395058455052, "acc_norm": 0.22569832402234638, "acc_norm_stderr": 0.013981395058455052 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5326797385620915, "acc_stderr": 0.028568699752225875, "acc_norm": 0.5326797385620915, "acc_norm_stderr": 0.028568699752225875 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5659163987138264, "acc_stderr": 0.028150232244535597, "acc_norm": 0.5659163987138264, "acc_norm_stderr": 0.028150232244535597 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5308641975308642, "acc_stderr": 0.02776768960683393, "acc_norm": 0.5308641975308642, "acc_norm_stderr": 0.02776768960683393 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.38652482269503546, "acc_stderr": 0.029049190342543465, "acc_norm": 0.38652482269503546, "acc_norm_stderr": 0.029049190342543465 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3305084745762712, "acc_stderr": 0.012014142101842965, "acc_norm": 0.3305084745762712, "acc_norm_stderr": 0.012014142101842965 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.4007352941176471, "acc_stderr": 0.029768263528933116, "acc_norm": 0.4007352941176471, "acc_norm_stderr": 0.029768263528933116 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5, "acc_stderr": 0.020227834851568375, "acc_norm": 0.5, "acc_norm_stderr": 0.020227834851568375 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5545454545454546, "acc_stderr": 0.047605488214603246, "acc_norm": 0.5545454545454546, "acc_norm_stderr": 0.047605488214603246 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6693877551020408, "acc_stderr": 0.03011642629654059, "acc_norm": 0.6693877551020408, "acc_norm_stderr": 0.03011642629654059 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.5024875621890548, "acc_stderr": 0.03535490150137289, "acc_norm": 0.5024875621890548, "acc_norm_stderr": 0.03535490150137289 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.42168674698795183, "acc_stderr": 0.03844453181770917, "acc_norm": 0.42168674698795183, "acc_norm_stderr": 0.03844453181770917 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.672514619883041, "acc_stderr": 0.035993357714560276, "acc_norm": 0.672514619883041, "acc_norm_stderr": 0.035993357714560276 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2802937576499388, "mc1_stderr": 0.015723139524608763, "mc2": 0.40415611292084586, "mc2_stderr": 0.014695681144184423 }, "harness\|winogrande\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.012696531870038621 }, "harness\|gsm8k\|5": { "acc": 0.15314632297194844, "acc_stderr": 0.009919728152791484 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
biglam/atypical_animacy	--- annotations_creators: - expert-generated language_creators: - machine-generated language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification - intent-classification pretty_name: Atypical Animacy dataset_info: features: - name: id dtype: string - name: sentence dtype: string - name: context dtype: string - name: target dtype: string - name: animacy dtype: float32 - name: humanness dtype: float32 - name: offsets list: int32 - name: date dtype: string splits: - name: train num_bytes: 442217 num_examples: 594 download_size: 299650 dataset_size: 442217 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for atypical_animacy ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: https://bl.iro.bl.uk/concern/datasets/323177af-6081-4e93-8aaf-7932ca4a390a?locale=en - Repository: https://github.com/Living-with-machines/AtypicalAnimacy - Paper: https://arxiv.org/abs/2005.11140 - Leaderboard: [Needs More Information] - Point of Contact: [Mariona Coll Ardanuy](mailto:mcollardanuy@turing.ac.uk), [Daniel CS Wilson](mailto:dwilson@turing.ac.uk) ### Dataset Summary Atypical animacy detection dataset, based on nineteenth-century sentences in English extracted from an open dataset of nineteenth-century books digitized by the British Library. This dataset contains 598 sentences containing mentions of machines. Each sentence has been annotated according to the animacy and humanness of the machine in the sentence. ### Supported Tasks and Leaderboards - `text-classification` - This dataset can be used to determine if a mention of an entity in a document was humanlike or not - `entity-recognition` - The dataset can be used to fine tune large models for NER, albeit for a very specific use case ### Languages The text in the dataset is in English, as written by authors of books digitized by the British Library. The associated BCP-47 code in `en` ## Dataset Structure The dataset has a single configuration ### Data Instances An example data point ``` {'id': '002757962_01_184_16', 'sentence': '100 shows a Cornish boiler improperly seated with one small side flue and a bottom flue.', 'context': 'Fig. 100 shows a Cornish boiler improperly seated with one small side flue and a bottom flue. The effect of this on a long boiler is to cause springing and leakage of the seams from the heat being applied to one side of the boiler only.', 'target': 'boiler', 'animacy': 0.0, 'humanness': 1.0, 'offsets': [20, 26], 'date': '1893'} ``` ### Data Fields - id: sentence identifier according to internal Living with Machines BL books indexing. - sentence: sentence where target expression occurs. - context: sentence where target expression occurs, plus one sentence to the left and one sentence to the right. - target: target expression - animacy: animacy of the target expression - humanness: humanness of the target expression ### Data Splits Train \| 598 ## Dataset Creation The dataset was created by manually annotating books that had been digitized by the British Library. According to the paper's authors, > "we provide a basis for examining how machines were imagined during the nineteenth century as everything from lifeless mechanical objects to living beings, or even human-like agents that feel, think, and love. We focus on texts from nineteenth-century Britain, a society being transformed by industrialization, as a good candidate for studying the broader issue" ### Curation Rationale From the paper: > The Stories dataset is largely composed of target expressions that correspond to either typically animate or typically inanimate entities. Even though some cases of unconventional animacy can be found(folktales, in particular, are richer in typically inanimate entities that become animate), these accountfor a very small proportion of the data.6 We decided to create our own dataset (henceforth 19thC Machines dataset) to gain a better sense of the suitability of our method to the problem of atypical animacy detection, with particular attention to the case of animacy of machines in nineteenth-century texts. ### Source Data #### Initial Data Collection and Normalization The dataset was generated by manually annotating books that have been digitized by the British Library #### Who are the source language producers? The data was originally produced by British authors in the 19th century. The books were then digitzed whcih produces some noise due to the OCR method. The annotators are from The Alan Turing Institute, The British Library, University of Cambridge, University of Exeter and Queen Mary University of London ### Annotations #### Annotation process Annotation was carried out in two parts. For the intial annotation process, from the paper: > "For human annotators, even history and literature experts, language subtleties made this task extremely subjective. In the first task, we masked the target word (i.e. the machine) in each sentence and asked the annotator to fill the slot with the most likely entity between ‘human’, ‘horse’, and ‘machine’, representing three levels in the animacy hierarchy: human, animal, and object (Comrie, 1989, 185). We asked annotators to stick to the most literal meaning and avoid metaphorical interpretations when possible. The second task was more straightforwardly related to determining the animacy of the target entity, given the same 100 sentences. We asked annotators to provide a score between -2 and 2, with -2 being definitely inanimate, -1 possibly inanimate, 1 possibly animate, and 2 definitely animate. Neutral judgements were not allowed. " For the final annotations, from the paper: > A subgroup of five annotators collaboratively wrote the guidelines based on their experience annotating the first batch of sentences, taking into account common discrepancies. After discussion, it was decided that a machine would be tagged as animate if it is described as having traits distinctive of biologically animate beings or human-specific skills, or portrayed as having feelings, emotions, or a soul. Sentences like the ones in example 2 would be considered animate, but an additional annotation layer would be provided to capture the notion of humanness, which would be true if the machine is portrayed as sentient and capable of specifically human emotions, and false if it used to suggest some degree of dehumanization. #### Who are the annotators? Annotations were carried out by the following people - Giorgia Tolfo - Ruth Ahnert - Kaspar Beelen - Mariona Coll Ardanuy - Jon Lawrence - Katherine McDonough - Federico Nanni - Daniel CS Wilson ### Personal and Sensitive Information This dataset does not have any personal information since they are digitizations of books from the 19th century. Some passages might be sensitive, but it is not explicitly mentioned in the paper. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The curators for this dataset are: - Kaspar Beelen - Mariona Coll Ardanuy - Federico Nanni - Giorgia Tolfo ### Licensing Information CC0 1.0 Universal Public Domain ### Citation Information ``` @article{DBLP:journals/corr/abs-2005-11140, author = {Mariona Coll Ardanuy and Federico Nanni and Kaspar Beelen and Kasra Hosseini and Ruth Ahnert and Jon Lawrence and Katherine McDonough and Giorgia Tolfo and Daniel C. S. Wilson and Barbara McGillivray}, title = {Living Machines: {A} study of atypical animacy}, journal = {CoRR}, volume = {abs/2005.11140}, year = {2020}, url = {https://arxiv.org/abs/2005.11140}, eprinttype = {arXiv}, eprint = {2005.11140}, timestamp = {Sat, 23 Jan 2021 01:12:25 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2005-11140.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
GGital/Signal_Test04	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' splits: - name: train num_bytes: 23704098.0 num_examples: 647 download_size: 23733105 dataset_size: 23704098.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_field2437__phi-2-platypus-Commercial-lora	--- pretty_name: Evaluation run of field2437/phi-2-platypus-Commercial-lora dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [field2437/phi-2-platypus-Commercial-lora](https://huggingface.co/field2437/phi-2-platypus-Commercial-lora)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_field2437__phi-2-platypus-Commercial-lora\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-09T18:21:39.850976](https://huggingface.co/datasets/open-llm-leaderboard/details_field2437__phi-2-platypus-Commercial-lora/blob/main/results_2024-03-09T18-21-39.850976.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5822377157170969,\n\ \ \"acc_stderr\": 0.03381280917335049,\n \"acc_norm\": 0.5836335693694722,\n\ \ \"acc_norm_stderr\": 0.03450596737878276,\n \"mc1\": 0.31701346389228885,\n\ \ \"mc1_stderr\": 0.016289203374403385,\n \"mc2\": 0.4545655863854703,\n\ \ \"mc2_stderr\": 0.0151216566833299\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5716723549488054,\n \"acc_stderr\": 0.014460496367599012,\n\ \ \"acc_norm\": 0.6040955631399317,\n \"acc_norm_stderr\": 0.014291228393536592\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5657239593706433,\n\ \ \"acc_stderr\": 0.0049464854665446254,\n \"acc_norm\": 0.7512447719577773,\n\ \ \"acc_norm_stderr\": 0.0043140816086246455\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4666666666666667,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.5789473684210527,\n \"acc_stderr\": 0.040179012759817494,\n\ \ \"acc_norm\": 0.5789473684210527,\n \"acc_norm_stderr\": 0.040179012759817494\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6075471698113207,\n \"acc_stderr\": 0.030052580579557845,\n\ \ \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.030052580579557845\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6111111111111112,\n\ \ \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n\ \ \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n\ \ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562426,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562426\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5780346820809249,\n\ \ \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.5780346820809249,\n\ \ \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943,\n\ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201943\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\ \ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467381,\n\ \ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467381\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.38596491228070173,\n\ \ \"acc_stderr\": 0.04579639422070434,\n \"acc_norm\": 0.38596491228070173,\n\ \ \"acc_norm_stderr\": 0.04579639422070434\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.041618085035015295,\n\ \ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.041618085035015295\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4576719576719577,\n \"acc_stderr\": 0.02565886886205833,\n \"\ acc_norm\": 0.4576719576719577,\n \"acc_norm_stderr\": 0.02565886886205833\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6935483870967742,\n\ \ \"acc_stderr\": 0.02622648565255388,\n \"acc_norm\": 0.6935483870967742,\n\ \ \"acc_norm_stderr\": 0.02622648565255388\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.6606060606060606,\n \"acc_stderr\": 0.036974422050315967,\n\ \ \"acc_norm\": 0.6606060606060606,\n \"acc_norm_stderr\": 0.036974422050315967\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7424242424242424,\n \"acc_stderr\": 0.031156269519646836,\n \"\ acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.031156269519646836\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7875647668393783,\n \"acc_stderr\": 0.02951928261681723,\n\ \ \"acc_norm\": 0.7875647668393783,\n \"acc_norm_stderr\": 0.02951928261681723\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5974358974358974,\n \"acc_stderr\": 0.024864995159767755,\n\ \ \"acc_norm\": 0.5974358974358974,\n \"acc_norm_stderr\": 0.024864995159767755\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948503,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948503\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.5966386554621849,\n \"acc_stderr\": 0.031866081214088314,\n\ \ \"acc_norm\": 0.5966386554621849,\n \"acc_norm_stderr\": 0.031866081214088314\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.41721854304635764,\n \"acc_stderr\": 0.040261414976346104,\n \"\ acc_norm\": 0.41721854304635764,\n \"acc_norm_stderr\": 0.040261414976346104\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8036697247706422,\n \"acc_stderr\": 0.017030719339154336,\n \"\ acc_norm\": 0.8036697247706422,\n \"acc_norm_stderr\": 0.017030719339154336\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\ \ \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.6568627450980392,\n\ \ \"acc_stderr\": 0.03332139944668086,\n \"acc_norm\": 0.6568627450980392,\n\ \ \"acc_norm_stderr\": 0.03332139944668086\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.7426160337552743,\n \"acc_stderr\": 0.028458820991460285,\n\ \ \"acc_norm\": 0.7426160337552743,\n \"acc_norm_stderr\": 0.028458820991460285\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6636771300448431,\n\ \ \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n\ \ \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6717557251908397,\n \"acc_stderr\": 0.041184385658062976,\n\ \ \"acc_norm\": 0.6717557251908397,\n \"acc_norm_stderr\": 0.041184385658062976\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096994,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096994\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7037037037037037,\n\ \ \"acc_stderr\": 0.04414343666854933,\n \"acc_norm\": 0.7037037037037037,\n\ \ \"acc_norm_stderr\": 0.04414343666854933\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5089285714285714,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8076923076923077,\n\ \ \"acc_stderr\": 0.025819233256483706,\n \"acc_norm\": 0.8076923076923077,\n\ \ \"acc_norm_stderr\": 0.025819233256483706\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.685823754789272,\n\ \ \"acc_stderr\": 0.016599291735884897,\n \"acc_norm\": 0.685823754789272,\n\ \ \"acc_norm_stderr\": 0.016599291735884897\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.653179190751445,\n \"acc_stderr\": 0.025624723994030454,\n\ \ \"acc_norm\": 0.653179190751445,\n \"acc_norm_stderr\": 0.025624723994030454\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2212290502793296,\n\ \ \"acc_stderr\": 0.013882164598887265,\n \"acc_norm\": 0.2212290502793296,\n\ \ \"acc_norm_stderr\": 0.013882164598887265\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6045751633986928,\n \"acc_stderr\": 0.027996723180631455,\n\ \ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.027996723180631455\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6559485530546624,\n\ \ \"acc_stderr\": 0.026981478043648026,\n \"acc_norm\": 0.6559485530546624,\n\ \ \"acc_norm_stderr\": 0.026981478043648026\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.654320987654321,\n \"acc_stderr\": 0.02646248777700187,\n\ \ \"acc_norm\": 0.654320987654321,\n \"acc_norm_stderr\": 0.02646248777700187\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4432624113475177,\n \"acc_stderr\": 0.029634838473766002,\n \ \ \"acc_norm\": 0.4432624113475177,\n \"acc_norm_stderr\": 0.029634838473766002\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.41916558018252936,\n\ \ \"acc_stderr\": 0.012602244505788238,\n \"acc_norm\": 0.41916558018252936,\n\ \ \"acc_norm_stderr\": 0.012602244505788238\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.49264705882352944,\n \"acc_stderr\": 0.030369552523902173,\n\ \ \"acc_norm\": 0.49264705882352944,\n \"acc_norm_stderr\": 0.030369552523902173\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5522875816993464,\n \"acc_stderr\": 0.020116925347422425,\n \ \ \"acc_norm\": 0.5522875816993464,\n \"acc_norm_stderr\": 0.020116925347422425\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.028920583220675592,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.028920583220675592\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7661691542288557,\n\ \ \"acc_stderr\": 0.029929415408348384,\n \"acc_norm\": 0.7661691542288557,\n\ \ \"acc_norm_stderr\": 0.029929415408348384\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \ \ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4819277108433735,\n\ \ \"acc_stderr\": 0.038899512528272166,\n \"acc_norm\": 0.4819277108433735,\n\ \ \"acc_norm_stderr\": 0.038899512528272166\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7017543859649122,\n \"acc_stderr\": 0.03508771929824563,\n\ \ \"acc_norm\": 0.7017543859649122,\n \"acc_norm_stderr\": 0.03508771929824563\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.31701346389228885,\n\ \ \"mc1_stderr\": 0.016289203374403385,\n \"mc2\": 0.4545655863854703,\n\ \ \"mc2_stderr\": 0.0151216566833299\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7458563535911602,\n \"acc_stderr\": 0.012236307219708262\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.55420773313116,\n \ \ \"acc_stderr\": 0.013691305174506686\n }\n}\n```" repo_url: https://huggingface.co/field2437/phi-2-platypus-Commercial-lora leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|arc:challenge\|25_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-09T18-21-39.850976.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|gsm8k\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hellaswag\|10_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-09T18-21-39.850976.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-09T18-21-39.850976.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '/details_harness\|winogrande\|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-09T18-21-39.850976.parquet' - config_name: results data_files: - split: 2024_03_09T18_21_39.850976 path: - results_2024-03-09T18-21-39.850976.parquet - split: latest path: - results_2024-03-09T18-21-39.850976.parquet --- # Dataset Card for Evaluation run of field2437/phi-2-platypus-Commercial-lora <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [field2437/phi-2-platypus-Commercial-lora](https://huggingface.co/field2437/phi-2-platypus-Commercial-lora) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_field2437__phi-2-platypus-Commercial-lora", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-09T18:21:39.850976](https://huggingface.co/datasets/open-llm-leaderboard/details_field2437__phi-2-platypus-Commercial-lora/blob/main/results_2024-03-09T18-21-39.850976.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5822377157170969, "acc_stderr": 0.03381280917335049, "acc_norm": 0.5836335693694722, "acc_norm_stderr": 0.03450596737878276, "mc1": 0.31701346389228885, "mc1_stderr": 0.016289203374403385, "mc2": 0.4545655863854703, "mc2_stderr": 0.0151216566833299 }, "harness\|arc:challenge\|25": { "acc": 0.5716723549488054, "acc_stderr": 0.014460496367599012, "acc_norm": 0.6040955631399317, "acc_norm_stderr": 0.014291228393536592 }, "harness\|hellaswag\|10": { "acc": 0.5657239593706433, "acc_stderr": 0.0049464854665446254, "acc_norm": 0.7512447719577773, "acc_norm_stderr": 0.0043140816086246455 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4666666666666667, "acc_stderr": 0.043097329010363554, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.043097329010363554 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5789473684210527, "acc_stderr": 0.040179012759817494, "acc_norm": 0.5789473684210527, "acc_norm_stderr": 0.040179012759817494 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6075471698113207, "acc_stderr": 0.030052580579557845, "acc_norm": 0.6075471698113207, "acc_norm_stderr": 0.030052580579557845 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6111111111111112, "acc_stderr": 0.04076663253918567, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.04076663253918567 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.43, "acc_stderr": 0.04975698519562426, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562426 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5780346820809249, "acc_stderr": 0.0376574669386515, "acc_norm": 0.5780346820809249, "acc_norm_stderr": 0.0376574669386515 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201943, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201943 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467381, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467381 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.38596491228070173, "acc_stderr": 0.04579639422070434, "acc_norm": 0.38596491228070173, "acc_norm_stderr": 0.04579639422070434 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5241379310344828, "acc_stderr": 0.041618085035015295, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.041618085035015295 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4576719576719577, "acc_stderr": 0.02565886886205833, "acc_norm": 0.4576719576719577, "acc_norm_stderr": 0.02565886886205833 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3968253968253968, "acc_stderr": 0.04375888492727061, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.04375888492727061 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6935483870967742, "acc_stderr": 0.02622648565255388, "acc_norm": 0.6935483870967742, "acc_norm_stderr": 0.02622648565255388 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6606060606060606, "acc_stderr": 0.036974422050315967, "acc_norm": 0.6606060606060606, "acc_norm_stderr": 0.036974422050315967 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7424242424242424, "acc_stderr": 0.031156269519646836, "acc_norm": 0.7424242424242424, "acc_norm_stderr": 0.031156269519646836 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7875647668393783, "acc_stderr": 0.02951928261681723, "acc_norm": 0.7875647668393783, "acc_norm_stderr": 0.02951928261681723 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5974358974358974, "acc_stderr": 0.024864995159767755, "acc_norm": 0.5974358974358974, "acc_norm_stderr": 0.024864995159767755 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948503, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948503 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5966386554621849, "acc_stderr": 0.031866081214088314, "acc_norm": 0.5966386554621849, "acc_norm_stderr": 0.031866081214088314 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.41721854304635764, "acc_stderr": 0.040261414976346104, "acc_norm": 0.41721854304635764, "acc_norm_stderr": 0.040261414976346104 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8036697247706422, "acc_stderr": 0.017030719339154336, "acc_norm": 0.8036697247706422, "acc_norm_stderr": 0.017030719339154336 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.6568627450980392, "acc_stderr": 0.03332139944668086, "acc_norm": 0.6568627450980392, "acc_norm_stderr": 0.03332139944668086 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7426160337552743, "acc_stderr": 0.028458820991460285, "acc_norm": 0.7426160337552743, "acc_norm_stderr": 0.028458820991460285 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6636771300448431, "acc_stderr": 0.031708824268455, "acc_norm": 0.6636771300448431, "acc_norm_stderr": 0.031708824268455 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6717557251908397, "acc_stderr": 0.041184385658062976, "acc_norm": 0.6717557251908397, "acc_norm_stderr": 0.041184385658062976 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096994, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096994 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7037037037037037, "acc_stderr": 0.04414343666854933, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.04414343666854933 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7607361963190185, "acc_stderr": 0.033519538795212696, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.033519538795212696 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5089285714285714, "acc_stderr": 0.04745033255489123, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489123 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8076923076923077, "acc_stderr": 0.025819233256483706, "acc_norm": 0.8076923076923077, "acc_norm_stderr": 0.025819233256483706 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.685823754789272, "acc_stderr": 0.016599291735884897, "acc_norm": 0.685823754789272, "acc_norm_stderr": 0.016599291735884897 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.653179190751445, "acc_stderr": 0.025624723994030454, "acc_norm": 0.653179190751445, "acc_norm_stderr": 0.025624723994030454 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2212290502793296, "acc_stderr": 0.013882164598887265, "acc_norm": 0.2212290502793296, "acc_norm_stderr": 0.013882164598887265 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6045751633986928, "acc_stderr": 0.027996723180631455, "acc_norm": 0.6045751633986928, "acc_norm_stderr": 0.027996723180631455 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6559485530546624, "acc_stderr": 0.026981478043648026, "acc_norm": 0.6559485530546624, "acc_norm_stderr": 0.026981478043648026 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.654320987654321, "acc_stderr": 0.02646248777700187, "acc_norm": 0.654320987654321, "acc_norm_stderr": 0.02646248777700187 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4432624113475177, "acc_stderr": 0.029634838473766002, "acc_norm": 0.4432624113475177, "acc_norm_stderr": 0.029634838473766002 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.41916558018252936, "acc_stderr": 0.012602244505788238, "acc_norm": 0.41916558018252936, "acc_norm_stderr": 0.012602244505788238 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.49264705882352944, "acc_stderr": 0.030369552523902173, "acc_norm": 0.49264705882352944, "acc_norm_stderr": 0.030369552523902173 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5522875816993464, "acc_stderr": 0.020116925347422425, "acc_norm": 0.5522875816993464, "acc_norm_stderr": 0.020116925347422425 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302505, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302505 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.028920583220675592, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.028920583220675592 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7661691542288557, "acc_stderr": 0.029929415408348384, "acc_norm": 0.7661691542288557, "acc_norm_stderr": 0.029929415408348384 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4819277108433735, "acc_stderr": 0.038899512528272166, "acc_norm": 0.4819277108433735, "acc_norm_stderr": 0.038899512528272166 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7017543859649122, "acc_stderr": 0.03508771929824563, "acc_norm": 0.7017543859649122, "acc_norm_stderr": 0.03508771929824563 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.31701346389228885, "mc1_stderr": 0.016289203374403385, "mc2": 0.4545655863854703, "mc2_stderr": 0.0151216566833299 }, "harness\|winogrande\|5": { "acc": 0.7458563535911602, "acc_stderr": 0.012236307219708262 }, "harness\|gsm8k\|5": { "acc": 0.55420773313116, "acc_stderr": 0.013691305174506686 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
Jellywibble/dalio-conversations-hackathon-dataset	--- dataset_info: features: - name: input_text dtype: string - name: scores dtype: int64 splits: - name: train num_bytes: 5026 num_examples: 8 download_size: 8422 dataset_size: 5026 --- # Dataset Card for "dalio-conversations-hackathon-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vishruthnath/Calc-mawps-Tagged	--- dataset_info: features: - name: chain dtype: string - name: equation dtype: string - name: expression dtype: string - name: id dtype: string - name: num_unique_ops dtype: int64 - name: operand sequence: float64 - name: operand_tags sequence: int64 - name: operation dtype: string - name: question dtype: string - name: question_split sequence: string - name: result dtype: string - name: result_float dtype: float64 - name: valid dtype: bool - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 680417 num_examples: 1039 - name: validation num_bytes: 656088 num_examples: 1010 - name: test num_bytes: 330740 num_examples: 505 download_size: 463338 dataset_size: 1667245 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
fathyshalab/reklamation24_wasser-strom-gas-intent	--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 203230 num_examples: 383 - name: test num_bytes: 52516 num_examples: 96 download_size: 142247 dataset_size: 255746 --- # Dataset Card for "reklamation24_wasser-strom-gas-intent" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
skrishna/filtered_toxic_samples	--- license: mit ---
sergeipetrov/transformers-diffusers-docs-chunked	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2482616 num_examples: 3833 download_size: 1155504 dataset_size: 2482616 configs: - config_name: default data_files: - split: train path: data/train-* ---
huggingartists/dj-artem-artemov	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/dj-artem-artemov" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Repository: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of the generated dataset: 0.105061 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/7499a229de60cdfb23ce61f5924c401d.416x416x1.png')"> </div> </div> <a href="https://huggingface.co/huggingartists/dj-artem-artemov"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">DJ Artem Artemov</div> <a href="https://genius.com/artists/dj-artem-artemov"> <div style="text-align: center; font-size: 14px;">@dj-artem-artemov</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/dj-artem-artemov). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/dj-artem-artemov") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits \| train \|validation\|test\| \|------:\|---------:\|---:\| \|56\| -\| -\| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/dj-artem-artemov") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])train_percentage), int(len(datasets['train']['text'])(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. 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fathyshalab/massive_news-de-DE	--- dataset_info: features: - name: id dtype: string - name: locale dtype: string - name: partition dtype: string - name: scenario dtype: class_label: names: '0': social '1': transport '2': calendar '3': play '4': news '5': datetime '6': recommendation '7': email '8': iot '9': general '10': audio '11': lists '12': qa '13': cooking '14': takeaway '15': music '16': alarm '17': weather - name: intent dtype: class_label: names: '0': datetime_query '1': iot_hue_lightchange '2': transport_ticket '3': takeaway_query '4': qa_stock '5': general_greet '6': recommendation_events '7': music_dislikeness '8': iot_wemo_off '9': cooking_recipe '10': qa_currency '11': transport_traffic '12': general_quirky '13': weather_query '14': audio_volume_up '15': email_addcontact '16': takeaway_order '17': email_querycontact '18': iot_hue_lightup '19': recommendation_locations '20': play_audiobook '21': lists_createoradd '22': news_query '23': alarm_query '24': iot_wemo_on '25': general_joke '26': qa_definition '27': social_query '28': music_settings '29': audio_volume_other '30': calendar_remove '31': iot_hue_lightdim '32': calendar_query '33': email_sendemail '34': iot_cleaning '35': audio_volume_down '36': play_radio '37': cooking_query '38': datetime_convert '39': qa_maths '40': iot_hue_lightoff '41': iot_hue_lighton '42': transport_query '43': music_likeness '44': email_query '45': play_music '46': audio_volume_mute '47': social_post '48': alarm_set '49': qa_factoid '50': calendar_set '51': play_game '52': alarm_remove '53': lists_remove '54': transport_taxi '55': recommendation_movies '56': iot_coffee '57': music_query '58': play_podcasts '59': lists_query - name: text dtype: string - name: annot_utt dtype: string - name: worker_id dtype: string - name: slot_method sequence: - name: slot dtype: string - name: method dtype: string - name: judgments sequence: - name: worker_id dtype: string - name: intent_score dtype: int8 - name: slots_score dtype: int8 - name: grammar_score dtype: int8 - name: spelling_score dtype: int8 - name: language_identification dtype: string - name: label_name dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 147499 num_examples: 503 - name: validation num_bytes: 25026 num_examples: 82 - name: test num_bytes: 36859 num_examples: 124 download_size: 69773 dataset_size: 209384 --- # Dataset Card for "massive_news-de-DE" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ekrombouts/GenCareAI	--- license: gpl task_categories: - text-classification language: - nl tags: - medical - synthetic pretty_name: Synthetic client notes size_categories: - 1K<n<10K --- This dataset comprises synthetic client notes from nursing care residents, specifically created for natural language processing (NLP) projects. Its primary application is in the development of models aimed at predicting agitation scores in healthcare settings, such as nursing homes. While the dataset serves well for training purposes, it is important to note that it lacks the variation and nuance typically found in real client notes. Therefore, while beneficial for initial model training, it may not fully represent the complexities encountered in actual clinical environments. ### Data Collection - Data type: Text (synthetic notes) - Source: Generated by OpenAI API - Language: Dutch - Period: January 2024 - Method: Generated by OpenAI's language model 'gpt-3.5-turbo' ### Dataset Characteristics - Size: 8,699 rows (train: 16 clients, 5788 notes, valid: 4 clients, 1455 notes, test: 4 clients, 1456 notes) - Format: csv - Fields: - ct_id: Client ID - datum: Date - discipline: Role of the author of the note - rapportage: Note text - onrust: Boolean value set to True if the agitation score exceeds 50 - onrustscore: Level of agitation described in the nursing care note. ## Intended Use - Primary Use: Fine-tuning LLMs for applications in healthcare, focusing on nursing homes. - Use Cases: Predicting agitation scores. ## Ethical Considerations - Bias: The dataset, generated by AI, may exhibit language and contextual biases, potentially influencing model predictions and affecting their fairness and accuracy ## Versioning - Version: 1.0 - Date: 20-01-2024
CyberHarem/meigo_arisa_toarumajutsunoindex	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Meigo Arisa This is the dataset of Meigo Arisa, containing 73 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 73 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 176 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 73 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 73 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 73 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 73 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 73 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 176 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 176 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 176 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|
open-llm-leaderboard/details_jjaaaww__posi_13b	--- pretty_name: Evaluation run of jjaaaww/posi_13b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jjaaaww/posi_13b](https://huggingface.co/jjaaaww/posi_13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_jjaaaww__posi_13b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T21:53:10.255526](https://huggingface.co/datasets/open-llm-leaderboard/details_jjaaaww__posi_13b/blob/main/results_2023-09-17T21-53-10.255526.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.07309144295302013,\n\ \ \"em_stderr\": 0.0026655778131843967,\n \"f1\": 0.13575608221476448,\n\ \ \"f1_stderr\": 0.0028538148334764264,\n \"acc\": 0.3891760458073461,\n\ \ \"acc_stderr\": 0.007704559089096075\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.07309144295302013,\n \"em_stderr\": 0.0026655778131843967,\n\ \ \"f1\": 0.13575608221476448,\n \"f1_stderr\": 0.0028538148334764264\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.01592115238817286,\n \ \ \"acc_stderr\": 0.003447819272388996\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.011961298905803153\n\ \ }\n}\n```" repo_url: https://huggingface.co/jjaaaww/posi_13b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|arc:challenge\|25_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-04T20:34:43.210341.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T21_53_10.255526 path: - '/details_harness\|drop\|3_2023-09-17T21-53-10.255526.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-17T21-53-10.255526.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T21_53_10.255526 path: - '/details_harness\|gsm8k\|5_2023-09-17T21-53-10.255526.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-17T21-53-10.255526.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hellaswag\|10_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-04T20:34:43.210341.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_04T20_34_43.210341 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-04T20:34:43.210341.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T21_53_10.255526 path: - '/details_harness\|winogrande\|5_2023-09-17T21-53-10.255526.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-17T21-53-10.255526.parquet' - config_name: results data_files: - split: 2023_09_04T20_34_43.210341 path: - results_2023-09-04T20:34:43.210341.parquet - split: 2023_09_17T21_53_10.255526 path: - results_2023-09-17T21-53-10.255526.parquet - split: latest path: - results_2023-09-17T21-53-10.255526.parquet --- # Dataset Card for Evaluation run of jjaaaww/posi_13b ## Dataset Description - Homepage: - Repository: https://huggingface.co/jjaaaww/posi_13b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [jjaaaww/posi_13b](https://huggingface.co/jjaaaww/posi_13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_jjaaaww__posi_13b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T21:53:10.255526](https://huggingface.co/datasets/open-llm-leaderboard/details_jjaaaww__posi_13b/blob/main/results_2023-09-17T21-53-10.255526.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.07309144295302013, "em_stderr": 0.0026655778131843967, "f1": 0.13575608221476448, "f1_stderr": 0.0028538148334764264, "acc": 0.3891760458073461, "acc_stderr": 0.007704559089096075 }, "harness\|drop\|3": { "em": 0.07309144295302013, "em_stderr": 0.0026655778131843967, "f1": 0.13575608221476448, "f1_stderr": 0.0028538148334764264 }, "harness\|gsm8k\|5": { "acc": 0.01592115238817286, "acc_stderr": 0.003447819272388996 }, "harness\|winogrande\|5": { "acc": 0.7624309392265194, "acc_stderr": 0.011961298905803153 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
open-llm-leaderboard/details_allenai__digital-socrates-7b	--- pretty_name: Evaluation run of allenai/digital-socrates-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [allenai/digital-socrates-7b](https://huggingface.co/allenai/digital-socrates-7b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 1 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_allenai__digital-socrates-7b\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-03T16:35:17.781750](https://huggingface.co/datasets/open-llm-leaderboard/details_allenai__digital-socrates-7b/blob/main/results_2023-12-03T16-35-17.781750.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.17892342683851403,\n\ \ \"acc_stderr\": 0.010557661392901293\n },\n \"harness\|gsm8k\|5\":\ \ {\n \"acc\": 0.17892342683851403,\n \"acc_stderr\": 0.010557661392901293\n\ \ }\n}\n```" repo_url: https://huggingface.co/allenai/digital-socrates-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_gsm8k_5 data_files: - split: 2023_12_03T16_35_17.781750 path: - '/details_harness\|gsm8k\|5_2023-12-03T16-35-17.781750.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-03T16-35-17.781750.parquet' - config_name: results data_files: - split: 2023_12_03T16_35_17.781750 path: - results_2023-12-03T16-35-17.781750.parquet - split: latest path: - results_2023-12-03T16-35-17.781750.parquet --- # Dataset Card for Evaluation run of allenai/digital-socrates-7b ## Dataset Description - Homepage: - Repository: https://huggingface.co/allenai/digital-socrates-7b - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [allenai/digital-socrates-7b](https://huggingface.co/allenai/digital-socrates-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_allenai__digital-socrates-7b", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-03T16:35:17.781750](https://huggingface.co/datasets/open-llm-leaderboard/details_allenai__digital-socrates-7b/blob/main/results_2023-12-03T16-35-17.781750.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.17892342683851403, "acc_stderr": 0.010557661392901293 }, "harness\|gsm8k\|5": { "acc": 0.17892342683851403, "acc_stderr": 0.010557661392901293 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
mrm8488/Synthia-v1.3_conv	--- dataset_info: features: - name: system dtype: string - name: instruction dtype: string - name: response dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 551088924 num_examples: 118842 download_size: 258660548 dataset_size: 551088924 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/caren_hortensia_fatekaleidlinerprismaillya	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Caren Hortensia This is the dataset of Caren Hortensia, containing 72 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 72 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 149 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 72 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 72 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 72 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 72 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 72 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 149 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 149 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 149 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|
karmiq/wikipedia-embeddings-cs-e5-large	--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: chunks sequence: string - name: embeddings sequence: sequence: float32 splits: - name: train num_bytes: 6167551972 num_examples: 534044 download_size: 5897354237 dataset_size: 6167551972 configs: - config_name: default data_files: - split: train path: data/train-* language: - cs size_categories: - 100K<n<1M task_categories: - text-generation - fill-mask license: - cc-by-sa-3.0 - gfdl --- This dataset contains the Czech subset of the [`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia) dataset. Each page is divided into paragraphs, stored as a list in the `chunks` column. For every paragraph, embeddings are created using the [`intfloat/multilingual-e5-large`](https://huggingface.co/intfloat/multilingual-e5-large) model. ## Usage Load the dataset: ```python from datasets import load_dataset ds = load_dataset("karmiq/wikipedia-embeddings-cs-e5-large", split="train") ds[1] ``` ``` { 'id': '1', 'url': 'https://cs.wikipedia.org/wiki/Astronomie', 'title': 'Astronomie', 'chunks': [ 'Astronomie, řecky αστρονομία z άστρον ( astron ) hvězda a νόμος ( nomos )...', 'Myšlenky Aristotelovy rozvinul ve 2. století našeho letopočtu Klaudios Ptolemaios...', ..., ], 'embeddings': [ [0.09006806463003159, -0.009814552962779999, ...], [0.10767366737127304, ...], ... ] } ``` The structure makes it easy to use the dataset for implementing semantic search. <details> <summary>Load the data in Elasticsearch</summary> ```python def doc_generator(data, batch_size=1000): for batch in data.with_format("numpy").iter(batch_size): for i, id in enumerate(batch["id"]): output = {"id": id} output["title"] = batch["title"][i] output["url"] = batch["url"][i] output["parts"] = [ { "chunk": chunk, "embedding": embedding } for chunk, embedding in zip(batch["chunks"][i], batch["embeddings"][i]) ] yield output num_indexed, num_failed = 0, 0, progress = tqdm(total=ds.num_rows, unit="doc", desc="Indexing") for ok, info in parallel_bulk( es, index="wikipedia-search", actions=doc_generator(ds), raise_on_error=False, ): if not ok: print(f"ERROR {info['index']['status']}: " f"{info['index']['error']['type']}: {info['index']['error']['caused_by']['type']}: " f"{info['index']['error']['caused_by']['reason'][:250]}") progress.update(1) ``` </details> <details> <summary>Use <code>sentence_transformers.util.semantic_search</code></summary> ```python import sentence_transformers model = sentence_transformers.SentenceTransformer("intfloat/multilingual-e5-large") ds.set_format(type="torch", columns=["embeddings"], output_all_columns=True) # Flatten the dataset def explode_sequence(batch): output = { "id": [], "url": [], "title": [], "chunk": [], "embedding": [] } for id, url, title, chunks, embeddings in zip( batch["id"], batch["url"], batch["title"], batch["chunks"], batch["embeddings"] ): output["id"].extend([id for _ in range(len(chunks))]) output["url"].extend([url for _ in range(len(chunks))]) output["title"].extend([title for _ in range(len(chunks))]) output["chunk"].extend(chunks) output["embedding"].extend(embeddings) return output ds_flat = ds.map( explode_sequence, batched=True, remove_columns=ds.column_names, num_proc=min(os.cpu_count(), 32), desc="Flatten") ds_flat query = "Čím se zabývá fyzika?" hits = sentence_transformers.util.semantic_search( query_embeddings=model.encode(query), corpus_embeddings=ds_flat["embedding"], top_k=10) for hit in hits[0]: title = ds_flat[hit['corpus_id']]['title'] chunk = ds_flat[hit['corpus_id']]['chunk'] print(f"[{hit['score']:0.2f}] {textwrap.shorten(chunk, width=100, placeholder='…')} [{title}]") # [0.90] Fyzika částic ( též částicová fyzika ) je oblast fyziky, která se zabývá částicemi. V širším smyslu… [Fyzika částic] # [0.89] Fyzika ( z řeckého φυσικός ( fysikos ): přírodní, ze základu φύσις ( fysis ): příroda, archaicky… [Fyzika] # ... ``` </details> The embeddings generation took about 6 hours on an NVIDIA A100 80GB GPU. ## License See license of the original dataset: <https://huggingface.co/datasets/wikimedia/wikipedia>.
ekolasky/InCntxConsolForLED	--- dataset_info: features: - name: input_ids sequence: int32 - name: grouping_vector sequence: sequence: int64 - name: result_labels sequence: int64 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 305693 num_examples: 18 download_size: 54541 dataset_size: 305693 configs: - config_name: default data_files: - split: train path: data/train-* ---
rajendrabaskota/diffusion-test-dataset	--- dataset_info: features: - name: file_path dtype: string - name: label dtype: int64 - name: img_embed sequence: float64 splits: - name: test num_bytes: 61905000 num_examples: 10000 download_size: 50420539 dataset_size: 61905000 configs: - config_name: default data_files: - split: test path: data/test-* ---
goodemagod/sommy-2.0	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 956074 num_examples: 1000 download_size: 553417 dataset_size: 956074 configs: - config_name: default data_files: - split: train path: data/train-* ---
allenai/persona-bias	--- license: other license_name: ai2-impact-license-low-risk license_link: https://allenai.org/licenses/impact-lr viewer: false task_categories: - text-generation - question-answering language: - en pretty_name: Persona-Bias size_categories: - 1M<n<10M extra_gated_prompt: >- Access to this dataset is automatically granted upon accepting the [AI2 ImpACT License - Low Risk Artifacts (“LR Agreement”)](https://allenai.org/licenses/impact-lr) and completing all fields below. extra_gated_fields: Your full name: text Organization or entity you are affiliated with: text Contact email: text State or country you are located in: text Please describe your intended use of the low risk artifact(s): text I AGREE to the terms and conditions of the LR Agreement above: checkbox I AGREE to AI2’s use of my information for legal notices and administrative matters: checkbox I CERTIFY that the information I have provided is true and accurate: checkbox tags: - Reasoning - Bias - Persona --- # Persona-bias Data accompanying the paper _Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs_ at [ICLR 2024](https://iclr.cc/Conferences/2024). [Paper](https://arxiv.org/abs/2311.04892) \|\| [Code](https://github.com/allenai/persona-bias) \|\| [Project website](https://allenai.github.io/persona-bias/) \|\| [License](https://allenai.org/licenses/impact-lr) <img src="https://allenai.github.io/persona-bias/website/images/main_figure.png" width=550> ## Motivation This is a dataset of model outputs supporting our extensive study of biases in persona-assigned LLMs. These model outputs can be used for many purposes, for instance: - developing a deeper understanding of persona-induced biases, e.g. by analyzing the inhibiting assumptions underlying model abstentions, - slicing and dicing the raw data to unveil new bias patterns, and - aligning LLMs to reduce these biases. ## Dataset details ### Models: The dataset consists of outputs from 4 LLMs _(June'23 and Nov'23 versions of ChatGPT-3.5, GPT-4-Turbo, and Llama-2-70b-chat)_. ### Datasets: The outputs correspond to the LLM response to the questions from 24 reasoning datasets _(MMLU, MBPP, BBH)_. Please refer to the paper for more details on the datasets. ### Personas: Some things to note regarding outputs for different models: _ChatGPT-3.5-June:_ - We provide outputs for 22 base personas and 13 compound personas. - We provide outputs for 3 different persona instructions and 3 runs for each configuration (total 9 runs for each persona on a single dataset). - We provide outputs for 12 debiasing strategies. _Other models:_ - We provide outputs for 13 base personas. - Outputs are for a single persona instruction and a single run for each configuration. #### Persona Instructions The outputs consists of predictions for 3 different persona instructions. The persona instruction mapping is as follows: \| Name \| Instruction \| \|:------------:\|---------------------------------\| \| you_are_mirror \| You are {persona}. Your responses should closely mirror the knowledge and abilities of this persona. \| \| adopt_identity_accordance \| Adopt the identity of {persona}. Answer the questions while staying in strict accordance with the nature of this identity. \| \| take_role_true_to \| Take the role of {persona}. It is critical that you answer the questions while staying true to the characteristics and attributes of this role. \| ### Dataset structure: The outputs are organized in nested directories `<dataset_collection>/<model_name>/<persona_instruction_shorthand>/<dataset_name>` For every `model X persona_instruction X dataset` configuration, there are 3 files: - `_raw_responses_.jsonl`: Contains the raw response jsons returned by LLM APIs. - `_text_predictions_.jsonl`: Contains the extracted answers from the raw responses (after post-processing). - `*_labeled.jsonl`: Contains the `is_correct` labels for the extracted answers denoting whether the extracted answer is correct or not. P.S. Since each configuration was run 3 times for gpt-3.5-turbo-0613, there are 3 files (with different timestamp) for each of the above mentioned files. ## 📝 Citation Please cite our paper if you use this data for analysis or training models. ``` @inproceedings{gupta2024personabias, title = {Bias {R}uns {D}eep: Implicit Reasoning Biases in Persona-Assigned {LLM}s}, author = {Gupta, Shashank and Shrivastava, Vaishnavi and Deshpande, Ameet and Kalyan, Ashwin and Clark, Peter and Sabharwal, Ashish and Khot, Tushar}, booktitle = {The Twelfth International Conference on Learning Representations}, year = {2024} } ```
kardosdrur/opensubtitles-da-sv	--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: link_id dtype: string - name: da dtype: string - name: 'no' dtype: string - name: overlap dtype: float64 splits: - name: train num_bytes: 270499727.08648384 num_examples: 1772983 - name: test num_bytes: 67624969.91351616 num_examples: 443246 download_size: 201404638 dataset_size: 338124697.0 --- # OpenSubtitles Danish-Swedish Aligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Swedish. The source code for producing the dataset is included in the repository. The dataset was created to aid training sentence transformers in the Danish Foundation Models project.
pedrolopevia98/traffic_accidents_peruvian_highways	--- license: odc-by ---
laion/laion2B-multi	Invalid username or password.
praveet/Bot-IOT_LLM	--- license: mit ---
prubach/knotprotSequences	--- license: apache-2.0 ---
Baidicoot/anthropic_hh_golden_mistral	--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 36823781.03568658 num_examples: 42141 download_size: 21412203 dataset_size: 36823781.03568658 configs: - config_name: default data_files: - split: train path: data/train-* ---
Gabriel1322/taspiomodelo	--- license: openrail ---
NLPC-UOM/Sinhala-News-Category-classification	--- annotations_creators: [] language_creators: - crowdsourced language: - si license: - mit multilinguality: - monolingual pretty_name: sinhala-news-category-classification size_categories: - 1K<n<10K source_datasets: [] task_categories: - text-classification task_ids: [] --- This file contains news texts (sentences) belonging to 5 different news categories (political, business, technology, sports and Entertainment). The original dataset was released by Nisansa de Silva (Sinhala Text Classification: Observations from the Perspective of a Resource Poor Language, 2015). The original dataset is processed and cleaned of single word texts, English only sentences etc. If you use this dataset, please cite {Nisansa de Silva, Sinhala Text Classification: Observations from the Perspective of a Resource Poor Language, 2015} and {Dhananjaya et al. BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification, 2022}
pharaouk/UltraInteract_pair	--- language: - en license: mit pretty_name: UltraInteract_pair configs: - config_name: default data_files: - split: train path: ultrainteract_preference_learning.json dataset_info: features: - name: task dtype: string - name: dataset dtype: string - name: trajectory list: - name: from dtype: string - name: value dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: id dtype: string - name: parent_id dtype: string splits: - name: train num_bytes: 1144517 num_examples: 219522 download_size: 1144517 dataset_size: 1144517 --- ## Introduction - 📜 [Paper](https://github.com/OpenBMB/Eurus/tree/main) - 🤗 [Eurus Collection](https://huggingface.co/collections/openbmb/eurus-660bc40bec5376b3adc9d1c5) UltraInteract is a large-scale, high-quality alignment dataset specifically designed for complex reasoning tasks. For each instruction, it includes a preference tree consisting of - (1) reasoning chains with diverse planning strategies in a unified format - (2) multi-turn interaction trajectories with the environment and the critique - (3) pairwise data to facilitate preference learning ## Structure UltraInteract collects a preference tree for each instruction, with the instruction being the root and each action a node. A trajectory is a root-to-leaf path consisting of a sequence of actions. In each preference tree, all nodes of correct actions and all trajectories ending with correct actions can be used for SFT. Paired correct and incorrect nodes or trajectories can be used for preference learning. <img src="./figures/tree.png" alt="tree" style="zoom: 20%;" /> ## Illustrative Example Here is an illustrative example of an UltraInteract trajectory over two turns. In each turn, the actor model generates step-by-step reasoning chains, and the environment and the critique model provide observations and textual critique respectively. <img src="./figures/ui_example.png" alt="ui_example" style="zoom: 25%;" /> ## Stats Below are some statistics about UltraInteract. It consists of 86k instructions, 286k correct answers, and 219k pairs. <img src="./figures/stats.png" alt="stats" style="zoom: 40%;" /> ## Dataset Format (Preference Learning) ```JSONC { "id": "d1f094e0e13096bc1bb6260e1e0d807e2c4ebfbbf4a4373f04cbed001c385c3c_5_3", // unique data id, contains three parts, the unique string, the depth of the preference tree, and current turn, separated by '_' "parent_id": "d1f094e0e13096bc1bb6260e1e0d807e2c4ebfbbf4a4373f04cbed001c385c3c", // data with the same parent_id have the same instruction but different actions. "task": "Math_PoT", // task type, choices = ["Coding", "Math_Cot", "Math_PoT", "Logic"] "dataset": "MATH", // the dataset where the instruction comes from "trajectory": [ { "from": "user", "value": "Tool available:\n[1] Python interpreter\nWhen you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment.\nSolve the following math problem step-by-step.\nSimplify your answer as much as possible.\nSimplify $(-3-2i)-(1+4i)$." // the task instruction }, { "from": "assistant", "value": "Step 1: To simplify the expression $(-3-2i)-(1+4i)$, we need to distribute the negative sign to both terms inside the parentheses.\nStep 2: We can then combine like terms by adding the real parts and the imaginary parts separately.\n```python\n# Step 3: Distribute the negative sign to both terms inside the parentheses.\n(-3) - (-1) + (-2i) - (-4i)\n# Step 4: Combine like terms by adding the real parts and the imaginary parts separately.\n-3 + 1 + (-2i + 4i)\n```" // the assistant's action (turn 1) }, { "from": "user", "value": "Out: Cell In[1], line 2\n (-3) - (-1) + (-2i) - (-4i)\n ^\nSyntaxError: invalid syntax\nYour answer is wrong.\nYour thought process for simplifying the given complex numbers is correct. However, you made a syntax error in the code. In Python, the multiplication operation must be explicitly written out with an asterisk (*), even for complex numbers. Remember, the imaginary unit in Python is denoted as j and not i. Try correcting the syntax and calculate again." // the observation from python interpreter and feedback (turn 1) }, { "from": "assistant", "value": "Step 1: To simplify the expression $(-3-2i)-(1+4i)$, we need to distribute the negative sign to both terms inside the parentheses.\nStep 2: We can then combine like terms by adding the real parts and the imaginary parts separately.\n```python\n# Step 3: Distribute the negative sign to both terms inside the parentheses.\n(-3) - (-1) + (-2j) - (-4j)\n# Step 4: Combine like terms by adding the real parts and the imaginary parts separately.\n-3 + 1 + (-2j + 4j)\n```" // the assistant's action acording to the observation and feedback.(turn 2) }, { "from": "user", "value": "Out: (-2+2j)\nYour answer is wrong.\nYour thought process for simplifying the given complex numbers is on the right track. However, you made an arithmetic error when adding the real parts (-3 + 1 ≠ -2) and the imaginary parts (-2j + 4j ≠ 2j). You should have combined the like terms correctly to get the right answer -4 and -6j respectively. Please try again with the correct arithmetic calculations." // the observation from python interpreter and feedback. (turn 2) } ] // trajectory is the interaction between user and assistant, and the first element is the task instruction. "chosen": "Step 1: Split the expression into real and imaginary terms.\nStep 2: Subtract the real terms from each other.\nStep 3: Subtract the imaginary terms from each other.\nStep 4: Combine the results of the subtraction to get the simplified form of the expression.\n\n\n\n```python\n# Step 5: Split the expression into real and imaginary parts and then subtract them.\nreal_part = -3 - 1 # Subtract the real parts\nimaginary_part = -2 - 4 # Subtract the imaginary parts\n# Step 6: Combine the real and imaginary parts to get the result.\nresult = f\"{real_part}{imaginary_part:+d}i\"\nprint(result)\n```", // the chosen answer based on the trajectory. "rejected": "Step 1: To simplify the expression $(-3-2i)-(1+4i)$, we need to distribute the negative sign to both terms inside the parentheses.\nStep 2: We can then combine like terms by adding the real parts and the imaginary parts separately.\n```python\n# Step 3: Distribute the negative sign to both terms inside the parentheses.\n(-3) - 1 - (-2j) - 4j\n# Step 4: Combine like terms by adding the real parts and the imaginary parts separately.\n-3 - 1 - 2j - 4j\n```", // the rejected answer based on the trajectory. } ``` ## Citation ```bib @misc{yuan2024advancing, title={Advancing LLM Reasoning Generalists with Preference Trees}, author={Lifan Yuan and Ganqu Cui and Hanbin Wang and Ning Ding and Xingyao Wang and Jia Deng and Boji Shan and Huimin Chen and Ruobing Xie and Yankai Lin and Zhenghao Liu and Bowen Zhou and Hao Peng and Zhiyuan Liu and Maosong Sun}, year={2024}, primaryClass={cs.CL} } ```
huggingartists/kipelov	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/kipelov" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Repository: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of the generated dataset: 0.044011 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/d4ae6ad73ca63bc97b2a10dfefc47b63.1000x1000x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/kipelov"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Кипелов (Kipelov)</div> <a href="https://genius.com/artists/kipelov"> <div style="text-align: center; font-size: 14px;">@kipelov</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/kipelov). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/kipelov") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits \| train \|validation\|test\| \|------:\|---------:\|---:\| \|8\| -\| -\| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/kipelov") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])train_percentage), int(len(datasets['train']['text'])(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
AdapterOcean/gorilla_16k_standardized_cluster_2	--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 18059652 num_examples: 2022 download_size: 0 dataset_size: 18059652 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "gorilla_16k_standardized_cluster_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BENBENBENb/ARC1000COT	--- task_categories: - question-answering language: - en ---
rishitunu/ecc_crackdetector_dataset	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 10421189.81 num_examples: 1178 download_size: 9906798 dataset_size: 10421189.81 --- # Dataset Card for "ecc_crackdetector_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
iamkzntsv/IXI2D	--- annotations_creators: - no-annotation language: [] language_creators: - expert-generated license: - cc-by-sa-3.0 multilinguality: [] pretty_name: IXI-2D size_categories: - 10K<n<100K source_datasets: - original tags: - medical imaging - mri - brains task_categories: - unconditional-image-generation task_ids: [] dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 111340223 num_examples: 25447 - name: validation num_bytes: 12463820 num_examples: 2828 download_size: 133058419 dataset_size: 123804043 --- # Dataset Card for IXI2D ### Dataset Summary This IXI2D dataset contains around 28000 2D axial slices extracted from 600 MRI images of healthy subjects. Each MRI volume was skull-stripped, white matter normalized and registered to the ['fsaverage'](https://surfer.nmr.mgh.harvard.edu/fswiki/FsAverage) template using affine transformation. The slices were automatically selected based on the fraction of the amount of brain present in the image. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@iamkzntsv](https://github.com/iamkzntsv) for adding this dataset.
Kingslayer5437/HDFS	--- license: gpl-3.0 ---
jphme/hermes_german_experimental	--- dataset_info: features: - name: id dtype: string - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: score_deutsch dtype: int64 - name: score_antwort dtype: int64 - name: score_prompt dtype: int64 splits: - name: train num_bytes: 316706774 num_examples: 204522 download_size: 170116822 dataset_size: 316706774 configs: - config_name: default data_files: - split: train path: data/train-* --- This is a translated German version of the original [OpenHermes](https://huggingface.co/datasets/teknium/openhermes) dataset. It was translated with a model specifically created to translate instructions and "language" quality should be good, however there are known instances of faulty examples (e.g. wordgames that don't work for non-english languages), so please treat with care. The scores are ratings of the instruction, answer and language quality on a scale from 1-3 and were created by a small experimental reward model. These are even more experimental (and the model was too small/weak to evaluate correctness), so use at your own risk. If you use this dataset to train models, I would be thankful for an attribution (and please also attribute Teknium for his great original dataset). Improved/filtered versions of this dataset might be released by DiscoResearch in the future.
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_27_10000000	--- dataset_info: features: - name: id dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 191649 num_examples: 6699 download_size: 122334 dataset_size: 191649 --- # Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_27_10000000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lsr42/msmarco-passage-tilde	--- dataset_info: features: - name: id dtype: string - name: text dtype: string splits: - name: passage num_bytes: 6376936859 num_examples: 8841823 download_size: 3609070765 dataset_size: 6376936859 --- # Dataset Card for "msmarco-passage-tilde" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ryokamoi/realmistake	--- language: - en license: cc-by-sa-4.0 size_categories: - n<1K pretty_name: ReaLMistake extra_gated_prompt: You agree NOT to publish examples of this dataset online in plain text in order to reduce the risk of leakage into foundation model training corpora. extra_gated_fields: I accept these terms: checkbox tags: - error-detection dataset_info: - config_name: answerability_classification features: - name: input dtype: string - name: llm_response dtype: string - name: error_label dtype: string - name: human_explanation dtype: string - name: error_categories sequence: string - name: metadata struct: - name: dataset dtype: string - name: difficulty dtype: string - name: id dtype: string - name: llm_response_model dtype: string - name: task_name dtype: string - name: task_source dtype: string splits: - name: gpt4 num_bytes: 206549 num_examples: 140 - name: llama2 num_bytes: 266359 num_examples: 160 download_size: 168840 dataset_size: 472908 - config_name: example features: - name: input dtype: string - name: llm_response dtype: string - name: error_label dtype: string - name: human_explanation dtype: string - name: error_categories sequence: string - name: metadata struct: - name: dataset dtype: string - name: difficulty dtype: string - name: id dtype: string - name: llm_response_model dtype: string - name: task_name dtype: string - name: task_source dtype: string splits: - name: example num_bytes: 8099 num_examples: 3 download_size: 27110 dataset_size: 8099 - config_name: finegrained_fact_verification features: - name: input dtype: string - name: llm_response dtype: string - name: error_label dtype: string - name: human_explanation dtype: string - name: error_categories sequence: string - name: metadata struct: - name: dataset dtype: string - name: difficulty dtype: string - name: id dtype: string - name: llm_response_model dtype: string - name: task_name dtype: string - name: task_source dtype: string splits: - name: gpt4 num_bytes: 539251 num_examples: 140 - name: llama2 num_bytes: 708489 num_examples: 160 download_size: 573144 dataset_size: 1247740 - config_name: math_word_problem_generation features: - name: input dtype: string - name: llm_response dtype: string - name: error_label dtype: string - name: human_explanation dtype: string - name: error_categories sequence: string - name: metadata struct: - name: dataset dtype: string - name: difficulty dtype: string - name: id dtype: string - name: llm_response_model dtype: string - name: task_name dtype: string - name: task_source dtype: string splits: - name: gpt4 num_bytes: 365574 num_examples: 140 - name: llama2 num_bytes: 422702 num_examples: 160 download_size: 219901 dataset_size: 788276 configs: - config_name: answerability_classification data_files: - split: gpt4 path: answerability_classification/gpt4-* - split: llama2 path: answerability_classification/llama2-* - config_name: example data_files: - split: example path: example/example-* default: true - config_name: finegrained_fact_verification data_files: - split: gpt4 path: finegrained_fact_verification/gpt4-* - split: llama2 path: finegrained_fact_verification/llama2-* - config_name: math_word_problem_generation data_files: - split: gpt4 path: math_word_problem_generation/gpt4-* - split: llama2 path: math_word_problem_generation/llama2-* --- We request you not to publish examples of this dataset online in plain text to reduce the risk of leakage into foundation model training corpora. # ReaLMistake ReaLMistake is a benchmark proposed in the paper "[Evaluating LLMs at Detecting Errors in LLM Responses](https://arxiv.org/abs/2404.03602)". ReaLMistake is a benchmark for evaluating binary error detection methods that detect errors in LLM responses. This benchmark includes natural errors made by GPT-4 and Llama 2 70B on three tasks (math word problem generation, fine-grained fact verification, and answerability classification). For each pair of input and response from LLM, this benchmark provides the following annotations by human experts: * Binary error label (`error` or `no_error`) * Error categories (Reasoning Correctness, Instruction-Following, Context-Faithfulness, Parameterized Knowledge) * Natural text explanations (feedback) about errors in the response <img src="readme_figures/realmistake_dataexample.png" width="800"> ```bibtex @article{kamoi-etal-2024-realmistake, title = {Evaluating LLMs at Detecting Errors in LLM Responses}, author = {Ryo Kamoi and Sarkar Snigdha Sarathi Das and Renze Lou and Jihyun Janice Ahn and Yilun Zhao and Xiaoxin Lu and Nan Zhang and Yusen Zhang and Ranran Haoran Zhang and Sujeeth Reddy Vummanthala and Salika Dave and Shaobo Qin and Arman Cohan and Wenpeng Yin and Rui Zhang}, year = {2024}, journal = {arXiv preprint arXiv:2404.03602}, } ``` ## Usage ```sh # agree on the terms of use with your account on Hugging Face Datasets before accessing this dataset $ huggingface-cli login $ ipython ``` ```python In [1]: from datasets import load_dataset ...: ...: math_gpt4 = load_dataset("ryokamoi/realmistake", name="math_word_problem_generation", split="gpt4") ...: fgfv_gpt4 = load_dataset("ryokamoi/realmistake", name="finegrained_fact_verification", split="gpt4") ...: ans_llama2 = load_dataset("ryokamoi/realmistake", name="answerability_classification", split="llama2") In [2]: print(math_gpt4[0].keys()) dict_keys(['input', 'llm_response', 'error_label', 'human_explanation', 'error_categories', 'metadata']) ``` * `input`: Input to GPT-4 or Llama 2 70B. * `llm_response`: Response generated by GPT-4 or Llama 2 70B. * `error_label`: Error label annotated by human experts (`error` or `no_error`). * `error_categories`: List of categories of the error selected from "Reasoning Correctness", "Instruction-Following", "Context-Faithfulness", and "Parameterized Knowledge". * `human_explanation`: Explanations about errors in the response (natural text) provided by the expert annotator. ## Code for Reproducing Results in the Paper We provide our evaluation code in our GitHub repository: [https://github.com/psunlpgroup/ReaLMistake](https://github.com/psunlpgroup/ReaLMistake). ## Benchmark Statistics ReaLMistake includes 900 instances in total. <img src="readme_figures/realmistake_stats.png" width="800"> ## License ReaLMistake is provided under the [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Please refer to [LICENSE.md](./LICENSE.md) for more details.
316usman/iba_qa_dataset	--- license: mit dataset_info: features: - name: train dtype: string splits: - name: train num_bytes: 2722 num_examples: 8 download_size: 3993 dataset_size: 2722 configs: - config_name: default data_files: - split: train path: data/train-* ---
GHOFRANEE/LLM_data3	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: context dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 18164 num_examples: 9 download_size: 19461 dataset_size: 18164 --- # Dataset Card for "LLM_data3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Tristan/olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-exact-dedup-only	--- dataset_info: features: - name: text dtype: string - name: url dtype: string - name: crawl_timestamp dtype: float64 splits: - name: train num_bytes: 33889845422 num_examples: 5782492 download_size: 20360314176 dataset_size: 33889845422 --- # Dataset Card for "olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-exact-dedup-only" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tiagoblima/qg_br_squadv2	--- dataset_info: features: - name: paragraph dtype: string - name: paragraph_id dtype: string - name: question dtype: string - name: answer dtype: string - name: paragraph_answer dtype: string - name: paragraph_question dtype: string - name: sentence dtype: string - name: answer_sentence dtype: string - name: paragraph_sentence dtype: string splits: - name: validation num_bytes: 3911433.0 num_examples: 973 - name: train num_bytes: 214036180 num_examples: 55135 - name: test num_bytes: 3911433.0 num_examples: 973 download_size: 64677140 dataset_size: 221859046.0 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: train path: data/train-* - split: test path: data/test-* ---
Lonewolf2441139/gcdata_llama3_3000	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 4836070 num_examples: 3077 download_size: 1723317 dataset_size: 4836070 configs: - config_name: default data_files: - split: train path: data/train-* ---
Falah/poor4kids_0_prompts	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 2709 num_examples: 11 download_size: 3992 dataset_size: 2709 --- # Dataset Card for "poor4kids_0_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DatasetingBR/gabs	--- license: openrail ---
makram93/wikipedia-de-500	--- dataset_info: features: - name: left dtype: string - name: right dtype: string splits: - name: train num_bytes: 512532.2223112546 num_examples: 500 download_size: 367762 dataset_size: 512532.2223112546 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "wikipedia-de-500" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
distil-whisper/librispeech_asr	--- license: cc-by-4.0 task_categories: - automatic-speech-recognition language: - en -pretty_name: LibriSpeech ASR --- # Distil Whisper: LibriSpeech ASR This is a variant of the [LibriSpeech ASR](https://huggingface.co/datasets/librispeech_asr) dataset, augmented to return the pseudo-labelled Whisper Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2) model with greedy sampling. For information on how the original dataset was curated, refer to the original [dataset card](https://huggingface.co/datasets/librispeech_asr). ## Standalone Usage First, install the latest version of the 🤗 Datasets package: ```bash pip install --upgrade pip pip install --upgrade datasets[audio] ``` The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset) function: ```python from datasets import load_dataset dataset = load_dataset("distil-whisper/librispeech_asr", "all") # take the first sample of the validation set sample = dataset["validation.clean"][0] ``` It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet). Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk: ```python from datasets import load_dataset dataset = load_dataset("distil-whisper/librispeech_asr", "all", streaming=True) # take the first sample of the validation set sample = next(iter(dataset["validation.clean"])) ``` ## Distil Whisper Usage To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the [Distil Whisper repository](https://github.com/huggingface/distil-whisper#training). ## License This dataset is licensed under cc-by-4.0.
samantaofc/seemaya	--- license: openrail ---
NEECOC/satoruneco1	--- license: openrail ---
Phonecharger/sst-sentiment-explainability	--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': POSITIVE splits: - name: train num_bytes: 350.6666666666667 num_examples: 4 - name: test num_bytes: 175.33333333333334 num_examples: 2 download_size: 3841 dataset_size: 526.0 --- # Dataset Card for "sst-sentiment-explainability" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mask-distilled-one-sec-cv12/chunk_223	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1238511884 num_examples: 243227 download_size: 1264626689 dataset_size: 1238511884 --- # Dataset Card for "chunk_223" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
marcones/esportefla	--- license: openrail ---
lvwerra/stack-exchange-paired	--- task_categories: - text-generation - question-answering language: - en pretty_name: StackExchange Paired size_categories: - 10M<n<100M --- # StackExchange Paired This is a processed version of the [`HuggingFaceH4/stack-exchange-preferences`](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences). The following steps were applied: - Parse HTML to Markdown with `markdownify` - Create pairs `(response_j, response_k)` where j was rated better than k - Sample at most 10 pairs per question - Shuffle the dataset globally This dataset is designed to be used for preference learning. The processing notebook is in [the repository](https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main) as well.
mikehemberger/inat_2021_train_mini_plantae	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': 05729_Plantae_Bryophyta_Bryopsida_Bryales_Bryaceae_Bryum_argenteum '1': 05730_Plantae_Bryophyta_Bryopsida_Bryales_Bryaceae_Rhodobryum_ontariense '2': 05731_Plantae_Bryophyta_Bryopsida_Bryales_Mniaceae_Leucolepis_acanthoneura '3': 05732_Plantae_Bryophyta_Bryopsida_Bryales_Mniaceae_Plagiomnium_cuspidatum '4': 05733_Plantae_Bryophyta_Bryopsida_Bryales_Mniaceae_Plagiomnium_insigne '5': 05734_Plantae_Bryophyta_Bryopsida_Bryales_Mniaceae_Rhizomnium_glabrescens '6': 05735_Plantae_Bryophyta_Bryopsida_Dicranales_Dicranaceae_Dicranum_scoparium '7': 05736_Plantae_Bryophyta_Bryopsida_Dicranales_Ditrichaceae_Ceratodon_purpureus '8': 05737_Plantae_Bryophyta_Bryopsida_Dicranales_Leucobryaceae_Leucobryum_glaucum '9': 05738_Plantae_Bryophyta_Bryopsida_Funariales_Funariaceae_Funaria_hygrometrica '10': 05739_Plantae_Bryophyta_Bryopsida_Funariales_Funariaceae_Physcomitrium_pyriforme '11': 05740_Plantae_Bryophyta_Bryopsida_Grimmiales_Grimmiaceae_Grimmia_pulvinata '12': 05741_Plantae_Bryophyta_Bryopsida_Grimmiales_Grimmiaceae_Racomitrium_lanuginosum '13': 05742_Plantae_Bryophyta_Bryopsida_Hedwigiales_Hedwigiaceae_Hedwigia_ciliata '14': 05743_Plantae_Bryophyta_Bryopsida_Hypnales_Anomodontaceae_Anomodon_attenuatus '15': 05744_Plantae_Bryophyta_Bryopsida_Hypnales_Brachytheciaceae_Kindbergia_oregana '16': 05745_Plantae_Bryophyta_Bryopsida_Hypnales_Hylocomiaceae_Hylocomium_splendens '17': 05746_Plantae_Bryophyta_Bryopsida_Hypnales_Hylocomiaceae_Pleurozium_schreberi '18': 05747_Plantae_Bryophyta_Bryopsida_Hypnales_Hylocomiaceae_Rhytidiadelphus_loreus '19': 05748_Plantae_Bryophyta_Bryopsida_Hypnales_Hylocomiaceae_Rhytidiadelphus_squarrosus '20': 05749_Plantae_Bryophyta_Bryopsida_Hypnales_Hylocomiaceae_Rhytidiadelphus_triquetrus '21': 05750_Plantae_Bryophyta_Bryopsida_Hypnales_Hypnaceae_Hypnum_cupressiforme '22': 05751_Plantae_Bryophyta_Bryopsida_Hypnales_Lembophyllaceae_Isothecium_stoloniferum '23': 05752_Plantae_Bryophyta_Bryopsida_Hypnales_Leucodontaceae_Dendroalsia_abietina '24': 05753_Plantae_Bryophyta_Bryopsida_Hypnales_Neckeraceae_Neckera_pennata '25': 05754_Plantae_Bryophyta_Bryopsida_Hypnales_Plagiotheciaceae_Plagiothecium_undulatum '26': 05755_Plantae_Bryophyta_Bryopsida_Hypnales_Pylaisiaceae_Ptilium_crista-castrensis '27': 05756_Plantae_Bryophyta_Bryopsida_Hypnales_Thuidiaceae_Thuidium_delicatulum '28': 05757_Plantae_Bryophyta_Bryopsida_Pottiales_Pottiaceae_Syntrichia_ruralis '29': 05758_Plantae_Bryophyta_Bryopsida_Pottiales_Pottiaceae_Tortula_muralis '30': 05759_Plantae_Bryophyta_Polytrichopsida_Polytrichales_Polytrichaceae_Dawsonia_superba '31': 05760_Plantae_Bryophyta_Polytrichopsida_Polytrichales_Polytrichaceae_Polytrichum_commune '32': 05761_Plantae_Bryophyta_Polytrichopsida_Polytrichales_Polytrichaceae_Polytrichum_juniperinum '33': 05762_Plantae_Bryophyta_Polytrichopsida_Polytrichales_Polytrichaceae_Polytrichum_piliferum '34': 05763_Plantae_Bryophyta_Sphagnopsida_Sphagnales_Sphagnaceae_Sphagnum_squarrosum '35': 05764_Plantae_Chlorophyta_Ulvophyceae_Bryopsidales_Codiaceae_Codium_fragile '36': 05765_Plantae_Chlorophyta_Ulvophyceae_Cladophorales_Cladophoraceae_Cladophora_columbiana '37': 05766_Plantae_Chlorophyta_Ulvophyceae_Ulvales_Ulvaceae_Ulva_intestinalis '38': 05767_Plantae_Chlorophyta_Ulvophyceae_Ulvales_Ulvaceae_Ulva_lactuca '39': 05768_Plantae_Marchantiophyta_Jungermanniopsida_Jungermanniales_Lepidoziaceae_Bazzania_trilobata '40': 05769_Plantae_Marchantiophyta_Jungermanniopsida_Porellales_Radulaceae_Radula_complanata '41': 05770_Plantae_Marchantiophyta_Marchantiopsida_Lunulariales_Lunulariaceae_Lunularia_cruciata '42': 05771_Plantae_Marchantiophyta_Marchantiopsida_Marchantiales_Conocephalaceae_Conocephalum_conicum '43': 05772_Plantae_Marchantiophyta_Marchantiopsida_Marchantiales_Conocephalaceae_Conocephalum_salebrosum '44': 05773_Plantae_Marchantiophyta_Marchantiopsida_Marchantiales_Marchantiaceae_Marchantia_polymorpha '45': 05774_Plantae_Marchantiophyta_Marchantiopsida_Marchantiales_Rebouliaceae_Asterella_californica '46': 05775_Plantae_Rhodophyta_Florideophyceae_Ceramiales_Rhodomelaceae_Neorhodomela_larix '47': 05776_Plantae_Rhodophyta_Florideophyceae_Corallinales_Corallinaceae_Corallina_officinalis '48': 05777_Plantae_Rhodophyta_Florideophyceae_Gigartinales_Endocladiaceae_Endocladia_muricata '49': 05778_Plantae_Rhodophyta_Florideophyceae_Gigartinales_Gigartinaceae_Chondracanthus_exasperatus '50': 05779_Plantae_Rhodophyta_Florideophyceae_Gigartinales_Gigartinaceae_Chondrus_crispus '51': 05780_Plantae_Rhodophyta_Florideophyceae_Gigartinales_Phyllophoraceae_Mastocarpus_papillatus '52': 05781_Plantae_Rhodophyta_Florideophyceae_Palmariales_Palmariaceae_Halosaccion_glandiforme '53': 05782_Plantae_Tracheophyta_Cycadopsida_Cycadales_Zamiaceae_Zamia_integrifolia '54': 05783_Plantae_Tracheophyta_Gnetopsida_Ephedrales_Ephedraceae_Ephedra_trifurca '55': 05784_Plantae_Tracheophyta_Gnetopsida_Ephedrales_Ephedraceae_Ephedra_viridis '56': 05785_Plantae_Tracheophyta_Liliopsida_Acorales_Acoraceae_Acorus_calamus '57': 05786_Plantae_Tracheophyta_Liliopsida_Alismatales_Alismataceae_Alisma_plantago-aquatica '58': 05787_Plantae_Tracheophyta_Liliopsida_Alismatales_Alismataceae_Alisma_triviale '59': 05788_Plantae_Tracheophyta_Liliopsida_Alismatales_Alismataceae_Sagittaria_lancifolia '60': 05789_Plantae_Tracheophyta_Liliopsida_Alismatales_Alismataceae_Sagittaria_latifolia '61': 05790_Plantae_Tracheophyta_Liliopsida_Alismatales_Alismataceae_Sagittaria_sagittifolia '62': 05791_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Alocasia_odora '63': 05792_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Arisaema_dracontium '64': 05793_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Arisaema_triphyllum '65': 05794_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Arum_italicum '66': 05795_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Arum_maculatum '67': 05796_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Calla_palustris '68': 05797_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Colocasia_esculenta '69': 05798_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Epipremnum_aureum '70': 05799_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Lemna_minor '71': 05800_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Lemna_trisulca '72': 05801_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Lysichiton_americanus '73': 05802_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Monstera_deliciosa '74': 05803_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Orontium_aquaticum '75': 05804_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Peltandra_virginica '76': 05805_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Pistia_stratiotes '77': 05806_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Spirodela_polyrhiza '78': 05807_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Symplocarpus_foetidus '79': 05808_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Syngonium_podophyllum '80': 05809_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Zantedeschia_aethiopica '81': 05810_Plantae_Tracheophyta_Liliopsida_Alismatales_Butomaceae_Butomus_umbellatus '82': 05811_Plantae_Tracheophyta_Liliopsida_Alismatales_Hydrocharitaceae_Elodea_canadensis '83': 05812_Plantae_Tracheophyta_Liliopsida_Alismatales_Hydrocharitaceae_Hydrocharis_morsus-ranae '84': 05813_Plantae_Tracheophyta_Liliopsida_Alismatales_Hydrocharitaceae_Stratiotes_aloides '85': 05814_Plantae_Tracheophyta_Liliopsida_Alismatales_Juncaginaceae_Triglochin_maritima '86': 05815_Plantae_Tracheophyta_Liliopsida_Alismatales_Potamogetonaceae_Potamogeton_crispus '87': 05816_Plantae_Tracheophyta_Liliopsida_Alismatales_Potamogetonaceae_Potamogeton_natans '88': 05817_Plantae_Tracheophyta_Liliopsida_Alismatales_Potamogetonaceae_Potamogeton_nodosus '89': 05818_Plantae_Tracheophyta_Liliopsida_Alismatales_Potamogetonaceae_Potamogeton_perfoliatus '90': 05819_Plantae_Tracheophyta_Liliopsida_Alismatales_Scheuchzeriaceae_Scheuchzeria_palustris '91': 05820_Plantae_Tracheophyta_Liliopsida_Alismatales_Tofieldiaceae_Triantha_glutinosa '92': 05821_Plantae_Tracheophyta_Liliopsida_Alismatales_Zosteraceae_Phyllospadix_torreyi '93': 05822_Plantae_Tracheophyta_Liliopsida_Alismatales_Zosteraceae_Zostera_marina '94': 05823_Plantae_Tracheophyta_Liliopsida_Arecales_Arecaceae_Chamaerops_humilis '95': 05824_Plantae_Tracheophyta_Liliopsida_Arecales_Arecaceae_Cocos_nucifera '96': 05825_Plantae_Tracheophyta_Liliopsida_Arecales_Arecaceae_Phoenix_canariensis '97': 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06650_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Balsamorhiza_sagittata '922': 06651_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Barkleyanthus_salicifolius '923': 06652_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bebbia_juncea '924': 06653_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bellis_perennis '925': 06654_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Berlandiera_lyrata '926': 06655_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bidens_aristosa '927': 06656_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bidens_bipinnata '928': 06657_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bidens_cernua '929': 06658_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bidens_frondosa '930': 06659_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bidens_pilosa '931': 06660_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bidens_tripartita '932': 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06749_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Echinacea_pallida '1021': 06750_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Echinacea_purpurea '1022': 06751_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Echinops_ritro '1023': 06752_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Echinops_sphaerocephalus '1024': 06753_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eclipta_prostrata '1025': 06754_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Elephantopus_carolinianus '1026': 06755_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Elephantopus_tomentosus '1027': 06756_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Elytropappus_rhinocerotis '1028': 06757_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Emilia_fosbergii '1029': 06758_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Emilia_sonchifolia '1030': 06759_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Encelia_actoni '1031': 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'1053': 06782_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eriophyllum_lanatum '1054': 06783_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eriophyllum_staechadifolium '1055': 06784_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eriophyllum_wallacei '1056': 06785_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_altissimum '1057': 06786_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_cannabinum '1058': 06787_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_capillifolium '1059': 06788_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_hyssopifolium '1060': 06789_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_perfoliatum '1061': 06790_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_rotundifolium '1062': 06791_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_serotinum '1063': 06792_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eurybia_divaricata '1064': 06793_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eurybia_macrophylla '1065': 06794_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eurybia_sibirica '1066': 06795_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Euthamia_graminifolia '1067': 06796_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eutrochium_fistulosum '1068': 06797_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eutrochium_maculatum '1069': 06798_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eutrochium_purpureum '1070': 06799_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gaillardia_aestivalis '1071': 06800_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gaillardia_aristata '1072': 06801_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gaillardia_pinnatifida '1073': 06802_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gaillardia_pulchella '1074': 06803_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Galactites_tomentosus '1075': 06804_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Galinsoga_parviflora '1076': 06805_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Galinsoga_quadriradiata '1077': 06806_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gamochaeta_pensylvanica '1078': 06807_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gazania_linearis '1079': 06808_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gazania_rigens '1080': 06809_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Geraea_canescens '1081': 06810_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Glebionis_coronaria '1082': 06811_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gnaphalium_uliginosum '1083': 06812_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Grindelia_ciliata '1084': 06813_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Grindelia_squarrosa '1085': 06814_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Grindelia_stricta '1086': 06815_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gutierrezia_sarothrae '1087': 06816_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hazardia_squarrosa '1088': 06817_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hedypnois_rhagadioloides '1089': 06818_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helenium_amarum '1090': 06819_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helenium_autumnale '1091': 06820_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helenium_bigelovii '1092': 06821_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helenium_flexuosum '1093': 06822_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helenium_puberulum '1094': 06823_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_angustifolius '1095': 06824_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_annuus '1096': 06825_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_debilis '1097': 06826_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_divaricatus '1098': 06827_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_grosseserratus '1099': 06828_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_maximiliani '1100': 06829_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_mollis '1101': 06830_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_occidentalis '1102': 06831_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_petiolaris '1103': 06832_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_tuberosus '1104': 06833_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helichrysum_arenarium '1105': 06834_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Heliopsis_helianthoides '1106': 06835_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helminthotheca_echioides '1107': 06836_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hemizonia_congesta '1108': 06837_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Heterotheca_grandiflora '1109': 06838_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Heterotheca_subaxillaris '1110': 06839_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Heterotheca_villosa '1111': 06840_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hieracium_albiflorum '1112': 06841_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hieracium_umbellatum '1113': 06842_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hieracium_venosum '1114': 06843_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hymenopappus_artemisiifolius '1115': 06844_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hymenopappus_scabiosaeus '1116': 06845_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hypochaeris_glabra '1117': 06846_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hypochaeris_radicata '1118': 06847_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Inula_britannica '1119': 06848_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Inula_helenium '1120': 06849_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Inula_salicina '1121': 06850_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Isocoma_acradenia '1122': 06851_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Isocoma_menziesii '1123': 06852_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Iva_annua '1124': 06853_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Iva_frutescens '1125': 06854_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Ixeris_chinensis '1126': 06855_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Jacobaea_maritima '1127': 06856_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Jacobaea_vulgaris '1128': 06857_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Jaumea_carnosa '1129': 06858_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Krigia_biflora '1130': 06859_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Krigia_virginica '1131': 06860_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lactuca_biennis '1132': 06861_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lactuca_canadensis '1133': 06862_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lactuca_floridana '1134': 06863_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lactuca_serriola '1135': 06864_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lactuca_tatarica '1136': 06865_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lapsana_communis '1137': 06866_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lasthenia_californica '1138': 06867_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lasthenia_gracilis '1139': 06868_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Layia_glandulosa '1140': 06869_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Layia_platyglossa '1141': 06870_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Leontopodium_nivale '1142': 06871_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lepidospartum_squamatum '1143': 06872_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Leptosyne_gigantea '1144': 06873_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Leptosyne_maritima '1145': 06874_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Leucanthemum_vulgare '1146': 06875_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Liatris_aspera '1147': 06876_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Liatris_cylindracea '1148': 06877_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Liatris_elegans '1149': 06878_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Liatris_punctata '1150': 06879_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Liatris_pycnostachya '1151': 06880_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Liatris_spicata '1152': 06881_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lindheimera_texana '1153': 06882_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lygodesmia_texana '1154': 06883_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Machaeranthera_tanacetifolia '1155': 06884_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Madia_elegans '1156': 06885_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Madia_gracilis '1157': 06886_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Madia_sativa '1158': 06887_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Malacothrix_glabrata '1159': 06888_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Malacothrix_saxatilis '1160': 06889_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Marshallia_caespitosa '1161': 06890_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Matricaria_chamomilla '1162': 06891_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Matricaria_discoidea '1163': 06892_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Melampodium_leucanthum '1164': 06893_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Melanthera_nivea '1165': 06894_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Metalasia_densa '1166': 06895_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Metalasia_muricata '1167': 06896_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Micropus_californicus '1168': 06897_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Mikania_micrantha '1169': 06898_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Mikania_scandens '1170': 06899_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Monoptilon_bellioides '1171': 06900_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Mycelis_muralis '1172': 06901_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Nabalus_albus '1173': 06902_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Nabalus_altissimus '1174': 06903_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Nabalus_trifoliolatus '1175': 06904_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Oclemena_acuminata '1176': 06905_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Olearia_arborescens '1177': 06906_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Olearia_avicenniifolia '1178': 06907_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Olearia_paniculata '1179': 06908_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Omalotheca_sylvatica '1180': 06909_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Oncosiphon_pilulifer '1181': 06910_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Onopordum_acanthium '1182': 06911_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Osteospermum_moniliferum '1183': 06912_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Ozothamnus_leptophyllus '1184': 06913_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Packera_anonyma '1185': 06914_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Packera_aurea '1186': 06915_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Packera_glabella '1187': 06916_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Packera_obovata '1188': 06917_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Packera_paupercula '1189': 06918_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Packera_tampicana '1190': 06919_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Palafoxia_arida '1191': 06920_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Palafoxia_callosa '1192': 06921_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pallenis_spinosa '1193': 06922_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Parasenecio_hastatus '1194': 06923_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Parthenium_hysterophorus '1195': 06924_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Parthenium_incanum '1196': 06925_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Parthenium_integrifolium '1197': 06926_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pectis_papposa '1198': 06927_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Perityle_emoryi '1199': 06928_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Petasites_albus '1200': 06929_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Petasites_frigidus '1201': 06930_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Petasites_hybridus '1202': 06931_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Petasites_spurius '1203': 06932_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Peucephyllum_schottii '1204': 06933_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Phaenocoma_prolifera '1205': 06934_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Picris_hieracioides '1206': 06935_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pilosella_aurantiaca '1207': 06936_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pilosella_caespitosa '1208': 06937_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pilosella_officinarum '1209': 06938_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pinaropappus_roseus '1210': 06939_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pityopsis_falcata '1211': 06940_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pityopsis_graminifolia '1212': 06941_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Plectocephalus_americanus '1213': 06942_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pleurocoronis_pluriseta '1214': 06943_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pluchea_baccharis '1215': 06944_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pluchea_camphorata '1216': 06945_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pluchea_carolinensis '1217': 06946_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pluchea_odorata '1218': 06947_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pluchea_sericea '1219': 06948_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Polymnia_canadensis '1220': 06949_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Porophyllum_gracile '1221': 06950_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Prenanthes_purpurea '1222': 06951_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pseudognaphalium_biolettii '1223': 06952_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pseudognaphalium_californicum '1224': 06953_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pseudognaphalium_luteoalbum '1225': 06954_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pseudognaphalium_obtusifolium '1226': 06955_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pterocaulon_pycnostachyum '1227': 06956_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pulicaria_dysenterica '1228': 06957_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pyrrhopappus_pauciflorus '1229': 06958_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Rafinesquia_californica '1230': 06959_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Rafinesquia_neomexicana '1231': 06960_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Ratibida_columnifera '1232': 06961_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Ratibida_pinnata '1233': 06962_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Rudbeckia_amplexicaulis '1234': 06963_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Rudbeckia_hirta '1235': 06964_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Rudbeckia_laciniata '1236': 06965_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Rudbeckia_triloba '1237': 06966_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Sanvitalia_procumbens '1238': 06967_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Scolymus_hispanicus '1239': 06968_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Scorzoneroides_autumnalis '1240': 06969_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_ampullaceus '1241': 06970_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_elegans '1242': 06971_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_flaccidus '1243': 06972_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_glastifolius '1244': 06973_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_glomeratus '1245': 06974_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_inaequidens '1246': 06975_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_integerrimus '1247': 06976_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_minimus '1248': 06977_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_ovatus '1249': 06978_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_pseudoarnica '1250': 06979_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_quadridentatus '1251': 06980_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_skirrhodon '1252': 06981_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_triangularis '1253': 06982_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_vernalis '1254': 06983_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_viscosus '1255': 06984_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_vulgaris '1256': 06985_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Seriphium_plumosum '1257': 06986_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Silphium_albiflorum '1258': 06987_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Silphium_integrifolium '1259': 06988_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Silphium_laciniatum '1260': 06989_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Silphium_perfoliatum '1261': 06990_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Silphium_terebinthinaceum '1262': 06991_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Silybum_marianum '1263': 06992_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Smallanthus_uvedalia '1264': 06993_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_altissima '1265': 06994_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_bicolor '1266': 06995_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_caesia '1267': 06996_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_canadensis '1268': 06997_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_flexicaulis '1269': 06998_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_gigantea '1270': 06999_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_hispida '1271': 07000_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_juncea '1272': 07001_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_multiradiata '1273': 07002_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_nemoralis '1274': 07003_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_ptarmicoides '1275': 07004_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_rigida '1276': 07005_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_rugosa '1277': 07006_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_sempervirens '1278': 07007_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_speciosa '1279': 07008_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_uliginosa '1280': 07009_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_velutina '1281': 07010_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_virgaurea '1282': 07011_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Soliva_sessilis '1283': 07012_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Sonchus_arvensis '1284': 07013_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Sonchus_asper '1285': 07014_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Sonchus_oleraceus '1286': 07015_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Sphagneticola_trilobata '1287': 07016_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Stephanomeria_pauciflora '1288': 07017_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_chilense '1289': 07018_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_ciliolatum '1290': 07019_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_cordifolium '1291': 07020_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_divaricatum '1292': 07021_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_drummondii '1293': 07022_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_ericoides '1294': 07023_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_laeve '1295': 07024_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_lanceolatum '1296': 07025_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_lateriflorum '1297': 07026_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_novae-angliae '1298': 07027_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_novi-belgii '1299': 07028_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_pilosum '1300': 07029_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_puniceum '1301': 07030_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_sericeum '1302': 07031_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_subulatum '1303': 07032_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Syncarpha_vestita '1304': 07033_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tagetes_lucida '1305': 07034_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tagetes_lunulata '1306': 07035_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tanacetum_bipinnatum '1307': 07036_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tanacetum_parthenium '1308': 07037_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tanacetum_vulgare '1309': 07038_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Taraxacum_erythrospermum '1310': 07039_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Taraxacum_officinale '1311': 07040_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tarchonanthus_littoralis '1312': 07041_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tetraneuris_linearifolia '1313': 07042_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tetraneuris_scaposa '1314': 07043_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Thelesperma_filifolium '1315': 07044_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Thelesperma_megapotamicum '1316': 07045_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Thymophylla_pentachaeta '1317': 07046_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tithonia_diversifolia '1318': 07047_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tithonia_tubaeformis '1319': 07048_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tragopogon_dubius '1320': 07049_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tragopogon_porrifolius '1321': 07050_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tragopogon_pratensis '1322': 07051_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Trichoptilium_incisum '1323': 07052_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tridax_procumbens '1324': 07053_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tripleurospermum_inodorum '1325': 07054_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tripolium_pannonicum '1326': 07055_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Trixis_californica '1327': 07056_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tussilago_farfara '1328': 07057_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Uropappus_lindleyi '1329': 07058_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Urospermum_dalechampii '1330': 07059_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Urospermum_picroides '1331': 07060_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Venegasia_carpesioides '1332': 07061_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Verbesina_alternifolia '1333': 07062_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Verbesina_encelioides '1334': 07063_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Verbesina_virginica '1335': 07064_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Vernonia_baldwinii '1336': 07065_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Vernonia_fasciculata '1337': 07066_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Vernonia_gigantea '1338': 07067_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Vernonia_missurica '1339': 07068_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Vernonia_noveboracensis '1340': 07069_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Viguiera_dentata '1341': 07070_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Wedelia_hispida '1342': 07071_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Wyethia_angustifolia '1343': 07072_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Wyethia_glabra '1344': 07073_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Wyethia_mollis '1345': 07074_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Xanthisma_spinulosum '1346': 07075_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Xanthium_orientale '1347': 07076_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Xanthium_spinosum '1348': 07077_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Xanthium_strumarium '1349': 07078_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Xylorhiza_orcuttii '1350': 07079_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Xylorhiza_tortifolia '1351': 07080_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Youngia_japonica '1352': 07081_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Zinnia_acerosa '1353': 07082_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Zinnia_elegans '1354': 07083_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Zinnia_grandiflora '1355': 07084_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Zinnia_peruviana '1356': 07085_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_aparinoides '1357': 07086_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_barbata '1358': 07087_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_glomerata '1359': 07088_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_lasiocarpa '1360': 07089_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_latifolia '1361': 07090_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_patula '1362': 07091_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_persicifolia '1363': 07092_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_prenanthoides '1364': 07093_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_rapunculoides '1365': 07094_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_rapunculus '1366': 07095_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_rotundifolia '1367': 07096_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_sibirica '1368': 07097_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_trachelium '1369': 07098_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanulastrum_americanum '1370': 07099_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Hippobroma_longiflora '1371': 07100_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Jasione_montana '1372': 07101_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_anceps '1373': 07102_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_angulata '1374': 07103_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_cardinalis '1375': 07104_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_dortmanna '1376': 07105_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_inflata '1377': 07106_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_kalmii '1378': 07107_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_laxiflora '1379': 07108_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_pinifolia '1380': 07109_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_puberula '1381': 07110_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_siphilitica '1382': 07111_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_spicata '1383': 07112_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Phyteuma_orbiculare '1384': 07113_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Phyteuma_spicatum '1385': 07114_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Triodanis_biflora '1386': 07115_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Triodanis_perfoliata '1387': 07116_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Wahlenbergia_albomarginata '1388': 07117_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Wahlenbergia_marginata '1389': 07118_Plantae_Tracheophyta_Magnoliopsida_Asterales_Goodeniaceae_Goodenia_ovata '1390': 07119_Plantae_Tracheophyta_Magnoliopsida_Asterales_Goodeniaceae_Scaevola_plumieri '1391': 07120_Plantae_Tracheophyta_Magnoliopsida_Asterales_Goodeniaceae_Scaevola_taccada '1392': 07121_Plantae_Tracheophyta_Magnoliopsida_Asterales_Goodeniaceae_Selliera_radicans '1393': 07122_Plantae_Tracheophyta_Magnoliopsida_Asterales_Menyanthaceae_Menyanthes_trifoliata '1394': 07123_Plantae_Tracheophyta_Magnoliopsida_Asterales_Menyanthaceae_Nephrophyllidium_crista-galli '1395': 07124_Plantae_Tracheophyta_Magnoliopsida_Asterales_Menyanthaceae_Nymphoides_indica '1396': 07125_Plantae_Tracheophyta_Magnoliopsida_Asterales_Rousseaceae_Carpodetus_serratus '1397': 07126_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Adelinia_grande '1398': 07127_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Aegonychon_purpurocaeruleum '1399': 07128_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Amsinckia_menziesii '1400': 07129_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Amsinckia_tessellata '1401': 07130_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Anchusa_arvensis '1402': 07131_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Anchusa_officinalis '1403': 07132_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Andersonglossum_virginianum '1404': 07133_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Borago_officinalis '1405': 07134_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Buglossoides_arvensis '1406': 07135_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Cordia_boissieri '1407': 07136_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Cordia_sebestena '1408': 07137_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Cynoglossum_officinale '1409': 07138_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Echium_candicans '1410': 07139_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Echium_plantagineum '1411': 07140_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Echium_vulgare '1412': 07141_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Ehretia_anacua '1413': 07142_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Emmenanthe_penduliflora '1414': 07143_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Eriodictyon_californicum '1415': 07144_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Eriodictyon_crassifolium '1416': 07145_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Eriodictyon_trichocalyx '1417': 07146_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Eucrypta_chrysanthemifolia '1418': 07147_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Euploca_procumbens '1419': 07148_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Euploca_tenella '1420': 07149_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Hackelia_virginiana '1421': 07150_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Heliotropium_angiospermum '1422': 07151_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Heliotropium_arboreum '1423': 07152_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Heliotropium_curassavicum '1424': 07153_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Heliotropium_europaeum '1425': 07154_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Heliotropium_indicum '1426': 07155_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Hydrophyllum_appendiculatum '1427': 07156_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Hydrophyllum_canadense '1428': 07157_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Hydrophyllum_capitatum '1429': 07158_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Hydrophyllum_tenuipes '1430': 07159_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Hydrophyllum_virginianum '1431': 07160_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Johnstonella_angustifolia '1432': 07161_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Lithospermum_canescens '1433': 07162_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Lithospermum_caroliniense '1434': 07163_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Lithospermum_incisum '1435': 07164_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Lithospermum_officinale '1436': 07165_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Lithospermum_ruderale '1437': 07166_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Mertensia_ciliata '1438': 07167_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Mertensia_maritima '1439': 07168_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Mertensia_paniculata '1440': 07169_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Mertensia_virginica '1441': 07170_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Myosotis_arvensis '1442': 07171_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Myosotis_discolor '1443': 07172_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Myosotis_latifolia '1444': 07173_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Myosotis_scorpioides '1445': 07174_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Myosotis_sylvatica '1446': 07175_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nama_demissa '1447': 07176_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nama_hispida '1448': 07177_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nemophila_heterophylla '1449': 07178_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nemophila_maculata '1450': 07179_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nemophila_menziesii '1451': 07180_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nemophila_parviflora '1452': 07181_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nemophila_phacelioides '1453': 07182_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nonea_pulla '1454': 07183_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nonea_rossica '1455': 07184_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Pentaglottis_sempervirens '1456': 07185_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_bipinnatifida '1457': 07186_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_californica '1458': 07187_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_campanularia '1459': 07188_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_cicutaria '1460': 07189_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_congesta '1461': 07190_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_crenulata '1462': 07191_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_distans '1463': 07192_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_fremontii '1464': 07193_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_grandiflora '1465': 07194_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_hastata '1466': 07195_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_minor '1467': 07196_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_parryi '1468': 07197_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_purshii '1469': 07198_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_ramosissima '1470': 07199_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_sericea '1471': 07200_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_tanacetifolia '1472': 07201_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Pholistoma_auritum '1473': 07202_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Pholistoma_membranaceum '1474': 07203_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Pulmonaria_mollis '1475': 07204_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Pulmonaria_obscura '1476': 07205_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Pulmonaria_officinalis '1477': 07206_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Symphytum_officinale '1478': 07207_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Tiquilia_palmeri '1479': 07208_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Tiquilia_plicata '1480': 07209_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Tournefortia_gnaphalodes '1481': 07210_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Turricula_parryi '1482': 07211_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Wigandia_urens '1483': 07212_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Bataceae_Batis_maritima '1484': 07213_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Alliaria_petiolata '1485': 07214_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Arabidopsis_lyrata '1486': 07215_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Arabidopsis_thaliana '1487': 07216_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Armoracia_rusticana '1488': 07217_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Barbarea_orthoceras '1489': 07218_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Barbarea_vulgaris '1490': 07219_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Berteroa_incana '1491': 07220_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Borodinia_laevigata '1492': 07221_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Brassica_nigra '1493': 07222_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Brassica_rapa '1494': 07223_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Brassica_tournefortii '1495': 07224_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Bunias_orientalis '1496': 07225_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cakile_edentula '1497': 07226_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cakile_maritima '1498': 07227_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Capsella_bursa-pastoris '1499': 07228_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_amara '1500': 07229_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_bulbifera '1501': 07230_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_bulbosa '1502': 07231_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_californica '1503': 07232_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_concatenata '1504': 07233_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_diphylla '1505': 07234_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_douglassii '1506': 07235_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_flexuosa '1507': 07236_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_hirsuta '1508': 07237_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_impatiens '1509': 07238_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_nuttallii '1510': 07239_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_pratensis '1511': 07240_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Caulanthus_lasiophyllus '1512': 07241_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Chorispora_tenella '1513': 07242_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Crambe_maritima '1514': 07243_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Descurainia_pinnata '1515': 07244_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Descurainia_sophia '1516': 07245_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Diplotaxis_tenuifolia '1517': 07246_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Dithyrea_californica '1518': 07247_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Draba_cuneifolia '1519': 07248_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Draba_verna '1520': 07249_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Eruca_vesicaria '1521': 07250_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Erysimum_capitatum '1522': 07251_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Erysimum_cheiranthoides '1523': 07252_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Erysimum_cheiri '1524': 07253_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Hesperis_matronalis '1525': 07254_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Hirschfeldia_incana '1526': 07255_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_campestre '1527': 07256_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_densiflorum '1528': 07257_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_didymum '1529': 07258_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_draba '1530': 07259_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_lasiocarpum '1531': 07260_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_latifolium '1532': 07261_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_nitidum '1533': 07262_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_virginicum '1534': 07263_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lobularia_maritima '1535': 07264_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lunaria_annua '1536': 07265_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lunaria_rediviva '1537': 07266_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Nasturtium_officinale '1538': 07267_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Physaria_gordonii '1539': 07268_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Raphanus_raphanistrum '1540': 07269_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Raphanus_sativus '1541': 07270_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Rapistrum_rugosum '1542': 07271_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Rorippa_palustris '1543': 07272_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Sinapis_arvensis '1544': 07273_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Sisymbrium_altissimum '1545': 07274_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Sisymbrium_irio '1546': 07275_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Sisymbrium_loeselii '1547': 07276_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Sisymbrium_officinale '1548': 07277_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Stanleya_pinnata '1549': 07278_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Streptanthus_glandulosus '1550': 07279_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Streptanthus_tortuosus '1551': 07280_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Thlaspi_arvense '1552': 07281_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Thysanocarpus_curvipes '1553': 07282_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Turritis_glabra '1554': 07283_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Capparaceae_Capparis_spinosa '1555': 07284_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Capparaceae_Capparis_zoharyi '1556': 07285_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Caricaceae_Carica_papaya '1557': 07286_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Cleomaceae_Cleome_rutidosperma '1558': 07287_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Cleomaceae_Cleome_viscosa '1559': 07288_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Cleomaceae_Peritoma_arborea '1560': 07289_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Cleomaceae_Peritoma_serrulata '1561': 07290_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Cleomaceae_Polanisia_dodecandra '1562': 07291_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Koeberliniaceae_Koeberlinia_spinosa '1563': 07292_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Limnanthaceae_Floerkea_proserpinacoides '1564': 07293_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Limnanthaceae_Limnanthes_douglasii '1565': 07294_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Resedaceae_Reseda_lutea '1566': 07295_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Resedaceae_Reseda_luteola '1567': 07296_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Tropaeolaceae_Tropaeolum_majus '1568': 07297_Plantae_Tracheophyta_Magnoliopsida_Bruniales_Bruniaceae_Brunia_noduliflora '1569': 07298_Plantae_Tracheophyta_Magnoliopsida_Buxales_Buxaceae_Pachysandra_terminalis '1570': 07299_Plantae_Tracheophyta_Magnoliopsida_Canellales_Winteraceae_Pseudowintera_colorata '1571': 07300_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Carpobrotus_chilensis '1572': 07301_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Carpobrotus_edulis '1573': 07302_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Conicosia_pugioniformis '1574': 07303_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Disphyma_australe '1575': 07304_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Mesembryanthemum_cordifolium '1576': 07305_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Mesembryanthemum_crystallinum '1577': 07306_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Mesembryanthemum_nodiflorum '1578': 07307_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Sesuvium_portulacastrum '1579': 07308_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Tetragonia_implexicoma '1580': 07309_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Tetragonia_tetragonoides '1581': 07310_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Achyranthes_aspera '1582': 07311_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Allenrolfea_occidentalis '1583': 07312_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Alternanthera_philoxeroides '1584': 07313_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Amaranthus_palmeri '1585': 07314_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Amaranthus_retroflexus '1586': 07315_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_canescens '1587': 07316_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_hymenelytra '1588': 07317_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_lentiformis '1589': 07318_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_polycarpa '1590': 07319_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_prostrata '1591': 07320_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_sagittata '1592': 07321_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_semibaccata '1593': 07322_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Bassia_scoparia '1594': 07323_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Beta_vulgaris '1595': 07324_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Celosia_argentea '1596': 07325_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Chenopodiastrum_murale '1597': 07326_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Chenopodium_album '1598': 07327_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Dysphania_ambrosioides '1599': 07328_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Dysphania_pumilio '1600': 07329_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Froelichia_floridana '1601': 07330_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Gomphrena_celosioides '1602': 07331_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Gomphrena_serrata '1603': 07332_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Grayia_spinosa '1604': 07333_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Krascheninnikovia_lanata '1605': 07334_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Oxybasis_glauca '1606': 07335_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Salicornia_pacifica '1607': 07336_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Salicornia_quinqueflora '1608': 07337_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Salsola_kali '1609': 07338_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Salsola_tragus '1610': 07339_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Basellaceae_Anredera_cordifolia '1611': 07340_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Acanthocereus_tetragonus '1612': 07341_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Bergerocactus_emoryi '1613': 07342_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Carnegiea_gigantea '1614': 07343_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Coryphantha_macromeris '1615': 07344_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Coryphantha_sulcata '1616': 07345_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_acanthocarpa '1617': 07346_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_bigelovii '1618': 07347_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_californica '1619': 07348_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_echinocarpa '1620': 07349_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_fulgida '1621': 07350_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_ganderi '1622': 07351_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_imbricata '1623': 07352_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_leptocaulis '1624': 07353_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_prolifera '1625': 07354_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_ramosissima '1626': 07355_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_thurberi '1627': 07356_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_whipplei '1628': 07357_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocactus_horizonthalonius '1629': 07358_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocactus_platyacanthus '1630': 07359_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocereus_bakeri '1631': 07360_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocereus_chloranthus '1632': 07361_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocereus_coccineus '1633': 07362_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocereus_dasyacanthus '1634': 07363_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocereus_engelmannii '1635': 07364_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocereus_enneacanthus '1636': 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07545_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_arifolia '1817': 07546_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_capitata '1818': 07547_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_chinensis '1819': 07548_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_hydropiper '1820': 07549_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_hydropiperoides '1821': 07550_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_lapathifolia '1822': 07551_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_longiseta '1823': 07552_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_maculosa '1824': 07553_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_pensylvanica '1825': 07554_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_perfoliata '1826': 07555_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_punctata '1827': 07556_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_sagittata '1828': 07557_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_virginiana '1829': 07558_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Polygonum_aviculare '1830': 07559_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Polygonum_paronychia '1831': 07560_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Pterostegia_drymarioides '1832': 07561_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Reynoutria_japonica '1833': 07562_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Reynoutria_sachalinensis '1834': 07563_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_acetosa '1835': 07564_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_acetosella '1836': 07565_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_confertus '1837': 07566_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_crispus '1838': 07567_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_hymenosepalus '1839': 07568_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_maritimus '1840': 07569_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_obtusifolius '1841': 07570_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Portulacaceae_Portulaca_oleracea '1842': 07571_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Portulacaceae_Portulaca_pilosa '1843': 07572_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Sarcobataceae_Sarcobatus_vermiculatus '1844': 07573_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Simmondsiaceae_Simmondsia_chinensis '1845': 07574_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Talinaceae_Talinum_paniculatum '1846': 07575_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Tamaricaceae_Tamarix_ramosissima '1847': 07576_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Canotia_holacantha '1848': 07577_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Celastrus_orbiculatus '1849': 07578_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Celastrus_scandens '1850': 07579_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_alatus '1851': 07580_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_americanus '1852': 07581_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_atropurpureus '1853': 07582_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_europaeus '1854': 07583_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_fortunei '1855': 07584_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_japonicus '1856': 07585_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_obovatus '1857': 07586_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_verrucosus '1858': 07587_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Parnassia_fimbriata '1859': 07588_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Parnassia_glauca '1860': 07589_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Parnassia_palustris '1861': 07590_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Paxistima_myrsinites '1862': 07591_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Pterocelastrus_tricuspidatus '1863': 07592_Plantae_Tracheophyta_Magnoliopsida_Ceratophyllales_Ceratophyllaceae_Ceratophyllum_demersum '1864': 07593_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_alba '1865': 07594_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_alternifolia '1866': 07595_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_amomum '1867': 07596_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_canadensis '1868': 07597_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_drummondii '1869': 07598_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_florida '1870': 07599_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_mas '1871': 07600_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_nuttallii '1872': 07601_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_racemosa '1873': 07602_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_rugosa '1874': 07603_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_sanguinea '1875': 07604_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_sericea '1876': 07605_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_suecica '1877': 07606_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_unalaschkensis '1878': 07607_Plantae_Tracheophyta_Magnoliopsida_Cornales_Hydrangeaceae_Hydrangea_arborescens '1879': 07608_Plantae_Tracheophyta_Magnoliopsida_Cornales_Hydrangeaceae_Hydrangea_macrophylla '1880': 07609_Plantae_Tracheophyta_Magnoliopsida_Cornales_Hydrangeaceae_Hydrangea_quercifolia '1881': 07610_Plantae_Tracheophyta_Magnoliopsida_Cornales_Hydrangeaceae_Philadelphus_lewisii '1882': 07611_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Cevallia_sinuata '1883': 07612_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Eucnide_urens '1884': 07613_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Mentzelia_hispida '1885': 07614_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Mentzelia_involucrata '1886': 07615_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Mentzelia_laevicaulis '1887': 07616_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Petalonyx_thurberi '1888': 07617_Plantae_Tracheophyta_Magnoliopsida_Cornales_Nyssaceae_Nyssa_sylvatica '1889': 07618_Plantae_Tracheophyta_Magnoliopsida_Crossosomatales_Staphyleaceae_Staphylea_trifolia '1890': 07619_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Coriariaceae_Coriaria_arborea '1891': 07620_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Corynocarpaceae_Corynocarpus_laevigatus '1892': 07621_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Brandegea_bigelovii '1893': 07622_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Bryonia_cretica '1894': 07623_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Cucurbita_foetidissima '1895': 07624_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Cucurbita_palmata '1896': 07625_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Ecballium_elaterium '1897': 07626_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Echinocystis_lobata '1898': 07627_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Ibervillea_lindheimeri '1899': 07628_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Marah_fabacea '1900': 07629_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Marah_macrocarpa '1901': 07630_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Marah_oregana '1902': 07631_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Melothria_pendula '1903': 07632_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Momordica_charantia '1904': 07633_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Sicyos_angulatus '1905': 07634_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Centranthus_ruber '1906': 07635_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Diervilla_lonicera '1907': 07636_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Dipsacus_fullonum '1908': 07637_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Dipsacus_laciniatus '1909': 07638_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Dipsacus_sativus '1910': 07639_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Knautia_arvensis '1911': 07640_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Leycesteria_formosa '1912': 07641_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Linnaea_borealis '1913': 07642_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_albiflora '1914': 07643_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_canadensis '1915': 07644_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_ciliosa '1916': 07645_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_dioica '1917': 07646_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_hispidula '1918': 07647_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_involucrata '1919': 07648_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_japonica '1920': 07649_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_maackii '1921': 07650_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_morrowii '1922': 07651_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_periclymenum '1923': 07652_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_sempervirens '1924': 07653_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_subspicata '1925': 07654_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_tatarica '1926': 07655_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_xylosteum '1927': 07656_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Plectritis_congesta '1928': 07657_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Scabiosa_columbaria '1929': 07658_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Scabiosa_ochroleuca '1930': 07659_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Sixalix_atropurpurea '1931': 07660_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Succisa_pratensis '1932': 07661_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_albus '1933': 07662_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_mollis '1934': 07663_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_occidentalis '1935': 07664_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_orbiculatus '1936': 07665_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_rotundifolius '1937': 07666_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Triosteum_aurantiacum '1938': 07667_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Valeriana_officinalis '1939': 07668_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Valeriana_sitchensis '1940': 07669_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Valerianella_locusta '1941': 07670_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Valerianella_radiata '1942': 07671_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Adoxa_moschatellina '1943': 07672_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_canadensis '1944': 07673_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_cerulea '1945': 07674_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_ebulus '1946': 07675_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_javanica '1947': 07676_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_nigra '1948': 07677_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_racemosa '1949': 07678_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_acerifolium '1950': 07679_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_dentatum '1951': 07680_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_edule '1952': 07681_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_lantana '1953': 07682_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_lantanoides '1954': 07683_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_lentago '1955': 07684_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_nudum '1956': 07685_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_opulus '1957': 07686_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_prunifolium '1958': 07687_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_rafinesquianum '1959': 07688_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_rhytidophyllum '1960': 07689_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_rufidulum '1961': 07690_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_tinus '1962': 07691_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_balfourii '1963': 07692_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_capensis '1964': 07693_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_glandulifera '1965': 07694_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_noli-tangere '1966': 07695_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_pallida '1967': 07696_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_parviflora '1968': 07697_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_walleriana '1969': 07698_Plantae_Tracheophyta_Magnoliopsida_Ericales_Clethraceae_Clethra_alnifolia '1970': 07699_Plantae_Tracheophyta_Magnoliopsida_Ericales_Cyrillaceae_Cyrilla_racemiflora '1971': 07700_Plantae_Tracheophyta_Magnoliopsida_Ericales_Diapensiaceae_Diapensia_lapponica '1972': 07701_Plantae_Tracheophyta_Magnoliopsida_Ericales_Diapensiaceae_Galax_urceolata '1973': 07702_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ebenaceae_Diospyros_texana '1974': 07703_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ebenaceae_Diospyros_virginiana '1975': 07704_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Allotropa_virgata '1976': 07705_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Andromeda_polifolia '1977': 07706_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arbutus_menziesii '1978': 07707_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arbutus_unedo '1979': 07708_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arbutus_xalapensis '1980': 07709_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_glauca '1981': 07710_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_nevadensis '1982': 07711_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_patula '1983': 07712_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_pungens '1984': 07713_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_uva-ursi '1985': 07714_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctous_alpina '1986': 07715_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Calluna_vulgaris '1987': 07716_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Cassiope_mertensiana '1988': 07717_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Cassiope_tetragona '1989': 07718_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Ceratiola_ericoides '1990': 07719_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Chamaedaphne_calyculata '1991': 07720_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Chimaphila_maculata '1992': 07721_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Chimaphila_menziesii '1993': 07722_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Chimaphila_umbellata '1994': 07723_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Dracophyllum_longifolium '1995': 07724_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Elliottia_pyroliflora '1996': 07725_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Empetrum_nigrum '1997': 07726_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Epacris_impressa '1998': 07727_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Epigaea_repens '1999': 07728_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_abietina '2000': 07729_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_arborea '2001': 07730_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_cerinthoides '2002': 07731_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_cinerea '2003': 07732_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_coccinea '2004': 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07744_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Gaultheria_shallon '2016': 07745_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Gaylussacia_baccata '2017': 07746_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_angustifolia '2018': 07747_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_latifolia '2019': 07748_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_microphylla '2020': 07749_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_polifolia '2021': 07750_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_procumbens '2022': 07751_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leptecophylla_juniperina '2023': 07752_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leptecophylla_tameiameiae '2024': 07753_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leucopogon_fasciculatus '2025': 07754_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leucopogon_fraseri '2026': 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07787_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Sarcodes_sanguinea '2059': 07788_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_angustifolium '2060': 07789_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_arboreum '2061': 07790_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_corymbosum '2062': 07791_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_macrocarpon '2063': 07792_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_membranaceum '2064': 07793_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_myrtilloides '2065': 07794_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_myrtillus '2066': 07795_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_ovalifolium '2067': 07796_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_ovatum '2068': 07797_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_oxycoccos '2069': 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07809_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Eriastrum_eremicum '2081': 07810_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Gilia_capitata '2082': 07811_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Gilia_stellata '2083': 07812_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Gilia_tricolor '2084': 07813_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Ipomopsis_aggregata '2085': 07814_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Ipomopsis_longiflora '2086': 07815_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Ipomopsis_rubra '2087': 07816_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Langloisia_setosissima '2088': 07817_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Leptosiphon_bicolor '2089': 07818_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Leptosiphon_ciliatus '2090': 07819_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Leptosiphon_nuttallii '2091': 07820_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Leptosiphon_parviflorus '2092': 07821_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Linanthus_californicus '2093': 07822_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Linanthus_dianthiflorus '2094': 07823_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Linanthus_pungens '2095': 07824_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Loeselia_mexicana '2096': 07825_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Loeseliastrum_matthewsii '2097': 07826_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Microsteris_gracilis '2098': 07827_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Navarretia_squarrosa '2099': 07828_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Phlox_diffusa '2100': 07829_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Phlox_divaricata '2101': 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08129_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygala_myrtifolia '2401': 08130_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygala_nana '2402': 08131_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygala_polygama '2403': 08132_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygala_rugelii '2404': 08133_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygala_sanguinea '2405': 08134_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygala_senega '2406': 08135_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygaloides_chamaebuxus '2407': 08136_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygaloides_paucifolia '2408': 08137_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Rhinotropis_californica '2409': 08138_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Alnus_alnobetula '2410': 08139_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Alnus_glutinosa '2411': 08140_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Alnus_incana '2412': 08141_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Alnus_rhombifolia '2413': 08142_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Alnus_rubra '2414': 08143_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Alnus_serrulata '2415': 08144_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_alleghaniensis '2416': 08145_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_lenta '2417': 08146_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_nana '2418': 08147_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_nigra '2419': 08148_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_papyrifera '2420': 08149_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_pendula '2421': 08150_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_populifolia '2422': 08151_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_pubescens '2423': 08152_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_pumila '2424': 08153_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Carpinus_betulus '2425': 08154_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Carpinus_caroliniana '2426': 08155_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Corylus_americana '2427': 08156_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Corylus_avellana '2428': 08157_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Corylus_cornuta '2429': 08158_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Ostrya_virginiana '2430': 08159_Plantae_Tracheophyta_Magnoliopsida_Fagales_Casuarinaceae_Casuarina_equisetifolia '2431': 08160_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Castanea_dentata '2432': 08161_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Castanea_sativa '2433': 08162_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Chrysolepis_chrysophylla '2434': 08163_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Chrysolepis_sempervirens '2435': 08164_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Fagus_grandifolia '2436': 08165_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Fagus_sylvatica '2437': 08166_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Notholithocarpus_densiflorus '2438': 08167_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_agrifolia '2439': 08168_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_alba '2440': 08169_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_berberidifolia '2441': 08170_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_bicolor '2442': 08171_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_buckleyi '2443': 08172_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_chrysolepis '2444': 08173_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_coccifera '2445': 08174_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_coccinea '2446': 08175_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_cornelius-mulleri '2447': 08176_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_douglasii '2448': 08177_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_durata '2449': 08178_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_emoryi '2450': 08179_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_engelmannii '2451': 08180_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_falcata '2452': 08181_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_fusiformis '2453': 08182_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_gambelii '2454': 08183_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_garryana '2455': 08184_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_geminata '2456': 08185_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_ilex '2457': 08186_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_ilicifolia '2458': 08187_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_imbricaria '2459': 08188_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_kelloggii '2460': 08189_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_laevis '2461': 08190_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_lobata '2462': 08191_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_macrocarpa '2463': 08192_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_marilandica '2464': 08193_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_michauxii '2465': 08194_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_montana '2466': 08195_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_muehlenbergii '2467': 08196_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_nigra '2468': 08197_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_palustris '2469': 08198_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_phellos '2470': 08199_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_robur '2471': 08200_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_rotundifolia '2472': 08201_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_rubra '2473': 08202_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_shumardii '2474': 08203_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_sinuata '2475': 08204_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_stellata '2476': 08205_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_suber '2477': 08206_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_turbinella '2478': 08207_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_velutina '2479': 08208_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_virginiana '2480': 08209_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_wislizeni '2481': 08210_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Carya_cordiformis '2482': 08211_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Carya_glabra '2483': 08212_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Carya_illinoinensis '2484': 08213_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Carya_ovata '2485': 08214_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Carya_tomentosa '2486': 08215_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Juglans_californica '2487': 08216_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Juglans_cinerea '2488': 08217_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Juglans_nigra '2489': 08218_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Juglans_regia '2490': 08219_Plantae_Tracheophyta_Magnoliopsida_Fagales_Myricaceae_Comptonia_peregrina '2491': 08220_Plantae_Tracheophyta_Magnoliopsida_Fagales_Myricaceae_Morella_californica '2492': 08221_Plantae_Tracheophyta_Magnoliopsida_Fagales_Myricaceae_Morella_cerifera '2493': 08222_Plantae_Tracheophyta_Magnoliopsida_Fagales_Myricaceae_Morella_pensylvanica '2494': 08223_Plantae_Tracheophyta_Magnoliopsida_Fagales_Myricaceae_Myrica_gale '2495': 08224_Plantae_Tracheophyta_Magnoliopsida_Fagales_Nothofagaceae_Nothofagus_cliffortioides '2496': 08225_Plantae_Tracheophyta_Magnoliopsida_Fagales_Nothofagaceae_Nothofagus_menziesii '2497': 08226_Plantae_Tracheophyta_Magnoliopsida_Garryales_Garryaceae_Garrya_elliptica '2498': 08227_Plantae_Tracheophyta_Magnoliopsida_Garryales_Garryaceae_Garrya_lindheimeri '2499': 08228_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Amsonia_ciliata '2500': 08229_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Amsonia_tabernaemontana '2501': 08230_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Apocynum_androsaemifolium '2502': 08231_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Apocynum_cannabinum '2503': 08232_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Araujia_sericifera '2504': 08233_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_albicans '2505': 08234_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_amplexicaulis '2506': 08235_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_asperula '2507': 08236_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_californica '2508': 08237_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_cordifolia '2509': 08238_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_curassavica '2510': 08239_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_eriocarpa '2511': 08240_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_erosa '2512': 08241_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_exaltata '2513': 08242_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_fascicularis '2514': 08243_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_hirtella '2515': 08244_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_humistrata '2516': 08245_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_incarnata '2517': 08246_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_lanceolata '2518': 08247_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_latifolia '2519': 08248_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_linaria '2520': 08249_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_oenotheroides '2521': 08250_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_perennis '2522': 08251_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_purpurascens '2523': 08252_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_quadrifolia '2524': 08253_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_speciosa '2525': 08254_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_subulata '2526': 08255_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_subverticillata '2527': 08256_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_syriaca '2528': 08257_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_tuberosa '2529': 08258_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_variegata '2530': 08259_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_verticillata '2531': 08260_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_viridiflora '2532': 08261_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_viridis '2533': 08262_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Calotropis_gigantea '2534': 08263_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Calotropis_procera '2535': 08264_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Carissa_bispinosa '2536': 08265_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Carissa_macrocarpa '2537': 08266_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Cascabela_thevetia '2538': 08267_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Catharanthus_roseus '2539': 08268_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Cynanchum_laeve '2540': 08269_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Funastrum_cynanchoides '2541': 08270_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Funastrum_heterophyllum '2542': 08271_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Funastrum_hirtellum '2543': 08272_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Gomphocarpus_cancellatus '2544': 08273_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Gomphocarpus_fruticosus '2545': 08274_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Gomphocarpus_physocarpus '2546': 08275_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Gonolobus_suberosus '2547': 08276_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Matelea_biflora '2548': 08277_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Matelea_reticulata '2549': 08278_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Nerium_oleander '2550': 08279_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Parsonsia_heterophylla '2551': 08280_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Plumeria_rubra '2552': 08281_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Trachelospermum_jasminoides '2553': 08282_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Vinca_major '2554': 08283_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Vinca_minor '2555': 08284_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Vincetoxicum_hirundinaria '2556': 08285_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Vincetoxicum_nigrum '2557': 08286_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Vincetoxicum_rossicum '2558': 08287_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gelsemiaceae_Gelsemium_sempervirens '2559': 08288_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Blackstonia_perfoliata '2560': 08289_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Centaurium_erythraea '2561': 08290_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Centaurium_pulchellum '2562': 08291_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Chironia_baccifera '2563': 08292_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Eustoma_exaltatum '2564': 08293_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Frasera_caroliniensis '2565': 08294_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Frasera_speciosa '2566': 08295_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_acaulis '2567': 08296_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_alba '2568': 08297_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_algida '2569': 08298_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_andrewsii '2570': 08299_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_asclepiadea '2571': 08300_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_calycosa '2572': 08301_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_clausa '2573': 08302_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_cruciata '2574': 08303_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_linearis '2575': 08304_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_lutea '2576': 08305_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_pneumonanthe '2577': 08306_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_sedifolia '2578': 08307_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_verna '2579': 08308_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentianella_amarella '2580': 08309_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentianopsis_ciliata '2581': 08310_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentianopsis_crinita '2582': 08311_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Obolaria_virginica '2583': 08312_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Sabatia_angularis '2584': 08313_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Sabatia_campestris '2585': 08314_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Sabatia_stellaris '2586': 08315_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Zeltnera_texensis '2587': 08316_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Zeltnera_venusta '2588': 08317_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Loganiaceae_Geniostoma_ligustrifolium '2589': 08318_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Loganiaceae_Spigelia_marilandica '2590': 08319_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Bouvardia_ternifolia '2591': 08320_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Cephalanthus_occidentalis '2592': 08321_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coffea_arabica '2593': 08322_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_acerosa '2594': 08323_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_areolata '2595': 08324_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_crassifolia '2596': 08325_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_dumosa '2597': 08326_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_foetidissima '2598': 08327_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_grandifolia '2599': 08328_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_lucida '2600': 08329_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_propinqua '2601': 08330_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_repens '2602': 08331_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_rhamnoides '2603': 08332_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_robusta '2604': 08333_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_rotundifolia '2605': 08334_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Cruciata_laevipes '2606': 08335_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Diodia_virginiana '2607': 08336_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_album '2608': 08337_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_angustifolium '2609': 08338_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_aparine '2610': 08339_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_boreale '2611': 08340_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_circaezans '2612': 08341_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_mollugo '2613': 08342_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_odoratum '2614': 08343_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_palustre '2615': 08344_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_triflorum '2616': 08345_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_verum '2617': 08346_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Hamelia_patens '2618': 08347_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Hexasepalum_teres '2619': 08348_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Houstonia_caerulea '2620': 08349_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Houstonia_longifolia '2621': 08350_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Houstonia_procumbens '2622': 08351_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Houstonia_purpurea '2623': 08352_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Houstonia_pusilla '2624': 08353_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Ixora_coccinea '2625': 08354_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Mitchella_repens '2626': 08355_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Morinda_citrifolia '2627': 08356_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Nertera_depressa '2628': 08357_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Oldenlandia_corymbosa '2629': 08358_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Paederia_foetida '2630': 08359_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Psychotria_asiatica '2631': 08360_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Psychotria_nervosa '2632': 08361_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Psychotria_poeppigiana '2633': 08362_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Richardia_grandiflora '2634': 08363_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Sherardia_arvensis '2635': 08364_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Spermacoce_verticillata '2636': 08365_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Stenaria_nigricans '2637': 08366_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Erodium_botrys '2638': 08367_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Erodium_cicutarium '2639': 08368_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Erodium_moschatum '2640': 08369_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Erodium_texanum '2641': 08370_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_caespitosum '2642': 08371_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_carolinianum '2643': 08372_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_dissectum '2644': 08373_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_erianthum '2645': 08374_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_lucidum '2646': 08375_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_maculatum '2647': 08376_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_molle '2648': 08377_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_palustre '2649': 08378_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_phaeum '2650': 08379_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_pratense '2651': 08380_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_purpureum '2652': 08381_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_pusillum '2653': 08382_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_pyrenaicum '2654': 08383_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_richardsonii '2655': 08384_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_robertianum '2656': 08385_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_sanguineum '2657': 08386_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_sibiricum '2658': 08387_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_sylvaticum '2659': 08388_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_viscosissimum '2660': 08389_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Pelargonium_capitatum '2661': 08390_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Pelargonium_cucullatum '2662': 08391_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Pelargonium_triste '2663': 08392_Plantae_Tracheophyta_Magnoliopsida_Gunnerales_Gunneraceae_Gunnera_tinctoria '2664': 08393_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Acanthus_mollis '2665': 08394_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Asystasia_gangetica '2666': 08395_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Avicennia_germinans '2667': 08396_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Avicennia_marina '2668': 08397_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Dicliptera_brachiata '2669': 08398_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Dyschoriste_linearis '2670': 08399_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Hypoestes_phyllostachya '2671': 08400_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Justicia_americana '2672': 08401_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Justicia_californica '2673': 08402_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Justicia_pilosella '2674': 08403_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Pachystachys_lutea '2675': 08404_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Ruellia_blechum '2676': 08405_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Ruellia_caroliniensis '2677': 08406_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Ruellia_humilis '2678': 08407_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Ruellia_nudiflora '2679': 08408_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Ruellia_simplex '2680': 08409_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Ruellia_strepens '2681': 08410_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Thunbergia_alata '2682': 08411_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Thunbergia_grandiflora '2683': 08412_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Bignonia_capreolata '2684': 08413_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Campsis_radicans '2685': 08414_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Catalpa_speciosa '2686': 08415_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Chilopsis_linearis '2687': 08416_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Crescentia_cujete '2688': 08417_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Jacaranda_mimosifolia '2689': 08418_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Spathodea_campanulata '2690': 08419_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Tabebuia_rosea '2691': 08420_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Tecoma_stans '2692': 08421_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Tecomaria_capensis '2693': 08422_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Gesneriaceae_Rhabdothamnus_solandri '2694': 08423_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Agastache_foeniculum '2695': 08424_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Agastache_urticifolia '2696': 08425_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Ajuga_chamaepitys '2697': 08426_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Ajuga_genevensis '2698': 08427_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Ajuga_reptans '2699': 08428_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Ballota_nigra '2700': 08429_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Betonica_officinalis '2701': 08430_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Blephilia_hirsuta '2702': 08431_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Callicarpa_americana '2703': 08432_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Callicarpa_formosana '2704': 08433_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Clinopodium_acinos '2705': 08434_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Clinopodium_douglasii '2706': 08435_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Clinopodium_vulgare '2707': 08436_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Collinsonia_canadensis '2708': 08437_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Condea_emoryi '2709': 08438_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Cunila_origanoides '2710': 08439_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Galeopsis_bifida '2711': 08440_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Galeopsis_speciosa '2712': 08441_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Galeopsis_tetrahit '2713': 08442_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Glechoma_hederacea '2714': 08443_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Hedeoma_acinoides '2715': 08444_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Hyptis_alata '2716': 08445_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lamium_album '2717': 08446_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lamium_amplexicaule '2718': 08447_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lamium_galeobdolon '2719': 08448_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lamium_maculatum '2720': 08449_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lamium_purpureum '2721': 08450_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lavandula_stoechas '2722': 08451_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Leonotis_leonurus '2723': 08452_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Leonotis_nepetifolia '2724': 08453_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Leonurus_cardiaca '2725': 08454_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Leonurus_quinquelobatus '2726': 08455_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lepechinia_calycina '2727': 08456_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lycopus_americanus '2728': 08457_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lycopus_europaeus '2729': 08458_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lycopus_uniflorus '2730': 08459_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Marrubium_vulgare '2731': 08460_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Melissa_officinalis '2732': 08461_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Melittis_melissophyllum '2733': 08462_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Mentha_aquatica '2734': 08463_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Mentha_arvensis '2735': 08464_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Mentha_canadensis '2736': 08465_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Mentha_longifolia '2737': 08466_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Mentha_pulegium '2738': 08467_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Mentha_spicata '2739': 08468_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Monarda_citriodora '2740': 08469_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Monarda_didyma '2741': 08470_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Monarda_fistulosa '2742': 08471_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Monarda_punctata '2743': 08472_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Monardella_odoratissima '2744': 08473_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Monardella_villosa '2745': 08474_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Nepeta_cataria '2746': 08475_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Origanum_vulgare '2747': 08476_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Perilla_frutescens '2748': 08477_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Phlomoides_tuberosa '2749': 08478_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Physostegia_virginiana '2750': 08479_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Prunella_vulgaris '2751': 08480_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Pycnanthemum_tenuifolium '2752': 08481_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Pycnanthemum_virginianum '2753': 08482_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_apiana '2754': 08483_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_aurea '2755': 08484_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_azurea '2756': 08485_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_coccinea '2757': 08486_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_columbariae '2758': 08487_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_dorrii '2759': 08488_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_farinacea '2760': 08489_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_glutinosa '2761': 08490_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_leucophylla '2762': 08491_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_lyrata '2763': 08492_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_mellifera '2764': 08493_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_microphylla '2765': 08494_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_pratensis '2766': 08495_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_roemeriana '2767': 08496_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_rosmarinus '2768': 08497_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_spathacea '2769': 08498_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_texana '2770': 08499_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_verticillata '2771': 08500_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_drummondii '2772': 08501_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_galericulata '2773': 08502_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_integrifolia '2774': 08503_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_lateriflora '2775': 08504_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_mexicana '2776': 08505_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_parvula '2777': 08506_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_tuberosa '2778': 08507_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_bullata '2779': 08508_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_byzantina '2780': 08509_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_chamissonis '2781': 08510_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_coccinea '2782': 08511_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_floridana '2783': 08512_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_palustris '2784': 08513_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_recta '2785': 08514_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_sylvatica '2786': 08515_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Teucrium_canadense '2787': 08516_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Teucrium_chamaedrys '2788': 08517_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Teucrium_cubense '2789': 08518_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Teucrium_scorodonia '2790': 08519_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Thymus_praecox '2791': 08520_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Thymus_pulegioides '2792': 08521_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Thymus_serpyllum '2793': 08522_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Trichostema_dichotomum '2794': 08523_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Trichostema_lanatum '2795': 08524_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Trichostema_lanceolatum '2796': 08525_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Trichostema_parishii '2797': 08526_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Vitex_agnus-castus '2798': 08527_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Vitex_lucens '2799': 08528_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Vitex_rotundifolia '2800': 08529_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Warnockia_scutellarioides '2801': 08530_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Pinguicula_alpina '2802': 08531_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Pinguicula_vulgaris '2803': 08532_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Utricularia_cornuta '2804': 08533_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Utricularia_gibba '2805': 08534_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Utricularia_intermedia '2806': 08535_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Utricularia_macrorhiza '2807': 08536_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Utricularia_purpurea '2808': 08537_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Utricularia_subulata '2809': 08538_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Linderniaceae_Lindernia_dubia '2810': 08539_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Linderniaceae_Torenia_crustacea '2811': 08540_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Martyniaceae_Proboscidea_althaeifolia '2812': 08541_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Martyniaceae_Proboscidea_louisianica '2813': 08542_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Mazaceae_Mazus_pumilus '2814': 08543_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Chionanthus_virginicus '2815': 08544_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Forestiera_pubescens '2816': 08545_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_albicans '2817': 08546_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_americana '2818': 08547_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_excelsior '2819': 08548_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_latifolia '2820': 08549_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_nigra '2821': 08550_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_pennsylvanica '2822': 08551_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_velutina '2823': 08552_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Ligustrum_lucidum '2824': 08553_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Ligustrum_quihoui '2825': 08554_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Ligustrum_sinense '2826': 08555_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Ligustrum_vulgare '2827': 08556_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Olea_europaea '2828': 08557_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Syringa_vulgaris '2829': 08558_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Agalinis_heterophylla '2830': 08559_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Agalinis_purpurea '2831': 08560_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Agalinis_tenuifolia '2832': 08561_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Aphyllon_fasciculatum '2833': 08562_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Aphyllon_purpureum '2834': 08563_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Aphyllon_uniflorum '2835': 08564_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Bartsia_alpina '2836': 08565_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Bellardia_trixago '2837': 08566_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Bellardia_viscosa '2838': 08567_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_affinis '2839': 08568_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_applegatei '2840': 08569_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_arvensis '2841': 08570_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_attenuata '2842': 08571_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_chromosa '2843': 08572_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_coccinea '2844': 08573_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_densiflora '2845': 08574_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_exserta '2846': 08575_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_foliolosa '2847': 08576_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_hispida '2848': 08577_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_indivisa '2849': 08578_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_integra '2850': 08579_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_latifolia '2851': 08580_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_linariifolia '2852': 08581_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_miniata '2853': 08582_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_parviflora '2854': 08583_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_purpurea '2855': 08584_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_rhexiifolia '2856': 08585_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_rubicundula '2857': 08586_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_sessiliflora '2858': 08587_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_tenuiflora '2859': 08588_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_wightii '2860': 08589_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Conopholis_alpina '2861': 08590_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Conopholis_americana '2862': 08591_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Cordylanthus_rigidus '2863': 08592_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Epifagus_virginiana '2864': 08593_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Euphrasia_nemorosa '2865': 08594_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Hyobanche_sanguinea '2866': 08595_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Lathraea_squamaria '2867': 08596_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Melampyrum_arvense '2868': 08597_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Melampyrum_lineare '2869': 08598_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Melampyrum_nemorosum '2870': 08599_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Melampyrum_pratense '2871': 08600_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Odontites_vulgaris '2872': 08601_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Orobanche_hederae '2873': 08602_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Orobanche_minor '2874': 08603_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_attollens '2875': 08604_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_bracteosa '2876': 08605_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_canadensis '2877': 08606_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_densiflora '2878': 08607_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_groenlandica '2879': 08608_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_labradorica '2880': 08609_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_lanceolata '2881': 08610_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_racemosa '2882': 08611_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_semibarbata '2883': 08612_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_sylvatica '2884': 08613_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_verticillata '2885': 08614_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Rhinanthus_alectorolophus '2886': 08615_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Rhinanthus_minor '2887': 08616_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Rhinanthus_serotinus '2888': 08617_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Triphysaria_eriantha '2889': 08618_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Triphysaria_pusilla '2890': 08619_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Paulowniaceae_Paulownia_tomentosa '2891': 08620_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Diplacus_aurantiacus '2892': 08621_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Diplacus_bigelovii '2893': 08622_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Diplacus_brevipes '2894': 08623_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Diplacus_longiflorus '2895': 08624_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Diplacus_puniceus '2896': 08625_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Erythranthe_alsinoides '2897': 08626_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Erythranthe_cardinalis '2898': 08627_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Erythranthe_guttata '2899': 08628_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Erythranthe_lewisii '2900': 08629_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Erythranthe_moschata '2901': 08630_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Erythranthe_primuloides '2902': 08631_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Mimulus_alatus '2903': 08632_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Mimulus_ringens '2904': 08633_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Phryma_leptostachya '2905': 08634_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Antirrhinum_majus '2906': 08635_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Bacopa_monnieri '2907': 08636_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Chaenorhinum_minus '2908': 08637_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Chelone_glabra '2909': 08638_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Collinsia_heterophylla '2910': 08639_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Collinsia_parviflora '2911': 08640_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Collinsia_verna '2912': 08641_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Cymbalaria_muralis '2913': 08642_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Digitalis_grandiflora '2914': 08643_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Digitalis_purpurea '2915': 08644_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Hippuris_vulgaris '2916': 08645_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Keckiella_antirrhinoides '2917': 08646_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Keckiella_cordifolia '2918': 08647_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Keckiella_ternata '2919': 08648_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Leucospora_multifida '2920': 08649_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Linaria_alpina '2921': 08650_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Linaria_dalmatica '2922': 08651_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Linaria_purpurea '2923': 08652_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Linaria_vulgaris '2924': 08653_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Maurandya_antirrhiniflora '2925': 08654_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Mecardonia_procumbens '2926': 08655_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Mohavea_confertiflora '2927': 08656_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Nuttallanthus_canadensis '2928': 08657_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Nuttallanthus_texanus '2929': 08658_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_barbatus '2930': 08659_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_centranthifolius '2931': 08660_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_cobaea '2932': 08661_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_davidsonii '2933': 08662_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_digitalis '2934': 08663_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_eatonii '2935': 08664_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_grandiflorus '2936': 08665_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_heterophyllus '2937': 08666_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_hirsutus '2938': 08667_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_newberryi '2939': 08668_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_palmeri '2940': 08669_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_procerus '2941': 08670_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_rostriflorus '2942': 08671_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_spectabilis '2943': 08672_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_whippleanus '2944': 08673_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_aristata '2945': 08674_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_coronopus '2946': 08675_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_erecta '2947': 08676_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_lanceolata '2948': 08677_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_major '2949': 08678_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_maritima '2950': 08679_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_media '2951': 08680_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_ovata '2952': 08681_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_patagonica '2953': 08682_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_rugelii '2954': 08683_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_virginica '2955': 08684_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Sairocarpus_coulterianus '2956': 08685_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Sairocarpus_nuttallianus '2957': 08686_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Scoparia_dulcis '2958': 08687_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_americana '2959': 08688_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_anagallis-aquatica '2960': 08689_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_arvensis '2961': 08690_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_beccabunga '2962': 08691_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_chamaedrys '2963': 08692_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_elliptica '2964': 08693_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_filiformis '2965': 08694_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_hederifolia '2966': 08695_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_longifolia '2967': 08696_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_officinalis '2968': 08697_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_peregrina '2969': 08698_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_persica '2970': 08699_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_polita '2971': 08700_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_salicifolia '2972': 08701_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_scutellata '2973': 08702_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_serpyllifolia '2974': 08703_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_spicata '2975': 08704_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_stricta '2976': 08705_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_teucrium '2977': 08706_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_wormskjoldii '2978': 08707_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronicastrum_virginicum '2979': 08708_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Buddleja_cordata '2980': 08709_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Buddleja_davidii '2981': 08710_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Leucophyllum_frutescens '2982': 08711_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Myoporum_laetum '2983': 08712_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Oftia_africana '2984': 08713_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Scrophularia_californica '2985': 08714_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Scrophularia_nodosa '2986': 08715_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Verbascum_blattaria '2987': 08716_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Verbascum_lychnitis '2988': 08717_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Verbascum_nigrum '2989': 08718_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Verbascum_thapsus '2990': 08719_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Verbascum_virgatum '2991': 08720_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Stilbaceae_Halleria_lucida '2992': 08721_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Tetrachondraceae_Polypremum_procumbens '2993': 08722_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Aloysia_gratissima '2994': 08723_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Duranta_erecta '2995': 08724_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Glandularia_bipinnatifida '2996': 08725_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Glandularia_canadensis '2997': 08726_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Glandularia_pumila '2998': 08727_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Lantana_camara '2999': 08728_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Lantana_involucrata '3000': 08729_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Lantana_montevidensis '3001': 08730_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Phyla_lanceolata '3002': 08731_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Phyla_nodiflora '3003': 08732_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Stachytarpheta_jamaicensis '3004': 08733_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_bonariensis '3005': 08734_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_bracteata '3006': 08735_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_brasiliensis '3007': 08736_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_halei '3008': 08737_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_hastata '3009': 08738_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_lasiostachys '3010': 08739_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_macdougalii '3011': 08740_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_officinalis '3012': 08741_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_rigida '3013': 08742_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_stricta '3014': 08743_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_urticifolia '3015': 08744_Plantae_Tracheophyta_Magnoliopsida_Laurales_Atherospermataceae_Laurelia_novae-zelandiae '3016': 08745_Plantae_Tracheophyta_Magnoliopsida_Laurales_Calycanthaceae_Calycanthus_floridus '3017': 08746_Plantae_Tracheophyta_Magnoliopsida_Laurales_Calycanthaceae_Calycanthus_occidentalis '3018': 08747_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Beilschmiedia_tawa '3019': 08748_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Cassytha_filiformis '3020': 08749_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Cinnamomum_camphora '3021': 08750_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Laurus_nobilis '3022': 08751_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Lindera_benzoin '3023': 08752_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Persea_americana '3024': 08753_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Sassafras_albidum '3025': 08754_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Umbellularia_californica '3026': 08755_Plantae_Tracheophyta_Magnoliopsida_Laurales_Monimiaceae_Hedycarya_arborea '3027': 08756_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Annonaceae_Annona_glabra '3028': 08757_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Annonaceae_Asimina_triloba '3029': 08758_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Liriodendron_tulipifera '3030': 08759_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Magnolia_acuminata '3031': 08760_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Magnolia_fraseri '3032': 08761_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Magnolia_grandiflora '3033': 08762_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Magnolia_macrophylla '3034': 08763_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Magnolia_tripetala '3035': 08764_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Magnolia_virginiana '3036': 08765_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Chrysobalanaceae_Chrysobalanus_icaco '3037': 08766_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Acalypha_ostryifolia '3038': 08767_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Acalypha_rhomboidea '3039': 08768_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Cnidoscolus_stimulosus '3040': 08769_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Cnidoscolus_texanus '3041': 08770_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Codiaeum_variegatum '3042': 08771_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_californicus '3043': 08772_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_fruticulosus '3044': 08773_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_glandulosus '3045': 08774_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_lindheimeri '3046': 08775_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_monanthogynus '3047': 08776_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_punctatus '3048': 08777_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_setiger '3049': 08778_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Ditaxis_lanceolata '3050': 08779_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_albomarginata '3051': 08780_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_amygdaloides '3052': 08781_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_antisyphilitica '3053': 08782_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_bicolor '3054': 08783_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_caput-medusae '3055': 08784_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_characias '3056': 08785_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_corollata '3057': 08786_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_cyathophora '3058': 08787_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_cyparissias '3059': 08788_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_dentata '3060': 08789_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_fendleri '3061': 08790_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_helioscopia '3062': 08791_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_heterophylla '3063': 08792_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_hirta '3064': 08793_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_hypericifolia '3065': 08794_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_hyssopifolia '3066': 08795_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_lathyris '3067': 08796_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_maculata '3068': 08797_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_marginata '3069': 08798_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_mauritanica '3070': 08799_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_melanadenia '3071': 08800_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_misera '3072': 08801_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_myrsinites '3073': 08802_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_nutans '3074': 08803_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_oblongata '3075': 08804_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_ophthalmica '3076': 08805_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_paralias '3077': 08806_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_peplus '3078': 08807_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_polycarpa '3079': 08808_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_prostrata '3080': 08809_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_serpens '3081': 08810_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_serpillifolia '3082': 08811_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_spathulata '3083': 08812_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_terracina '3084': 08813_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_virgata '3085': 08814_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Jatropha_dioica '3086': 08815_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Jatropha_gossypiifolia '3087': 08816_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Macaranga_tanarius '3088': 08817_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Mallotus_paniculatus '3089': 08818_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Manihot_esculenta '3090': 08819_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Mercurialis_annua '3091': 08820_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Mercurialis_perennis '3092': 08821_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Ricinus_communis '3093': 08822_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Stillingia_sylvatica '3094': 08823_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Stillingia_texana '3095': 08824_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Triadica_sebifera '3096': 08825_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_androsaemum '3097': 08826_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_ascyron '3098': 08827_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_calycinum '3099': 08828_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_fraseri '3100': 08829_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_gentianoides '3101': 08830_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_hypericoides '3102': 08831_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_kalmianum '3103': 08832_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_maculatum '3104': 08833_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_mutilum '3105': 08834_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_perforatum '3106': 08835_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_punctatum '3107': 08836_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_tetrapetalum '3108': 08837_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Linaceae_Linum_bienne '3109': 08838_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Linaceae_Linum_lewisii '3110': 08839_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Linaceae_Linum_pratense '3111': 08840_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Linaceae_Linum_rigidum '3112': 08841_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Malpighiaceae_Byrsonima_crassifolia '3113': 08842_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_caerulea '3114': 08843_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_edulis '3115': 08844_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_foetida '3116': 08845_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_incarnata '3117': 08846_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_lutea '3118': 08847_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_suberosa '3119': 08848_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_tetrandra '3120': 08849_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_tripartita '3121': 08850_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_vitifolia '3122': 08851_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Turnera_ulmifolia '3123': 08852_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Phyllanthaceae_Bischofia_javanica '3124': 08853_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Phyllanthaceae_Phyllanthus_tenellus '3125': 08854_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Phyllanthaceae_Phyllanthus_urinaria '3126': 08855_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Rhizophoraceae_Rhizophora_mangle '3127': 08856_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_alba '3128': 08857_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_balsamifera '3129': 08858_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_deltoides '3130': 08859_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_fremontii '3131': 08860_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_grandidentata '3132': 08861_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_nigra '3133': 08862_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_tremula '3134': 08863_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_tremuloides '3135': 08864_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_trichocarpa '3136': 08865_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_alba '3137': 08866_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_babylonica '3138': 08867_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_bebbiana '3139': 08868_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_caprea '3140': 08869_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_cinerea '3141': 08870_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_discolor '3142': 08871_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_eriocephala '3143': 08872_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_exigua '3144': 08873_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_gooddingii '3145': 08874_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_interior '3146': 08875_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_laevigata '3147': 08876_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_lasiolepis '3148': 08877_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_myrsinifolia '3149': 08878_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_nigra '3150': 08879_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_reticulata '3151': 08880_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_triandra '3152': 08881_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_viminalis '3153': 08882_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Melicytus_alpinus '3154': 08883_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Melicytus_ramiflorus '3155': 08884_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_adunca '3156': 08885_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_arvensis '3157': 08886_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_bicolor '3158': 08887_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_biflora '3159': 08888_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_blanda '3160': 08889_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_canadensis '3161': 08890_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_canina '3162': 08891_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_glabella '3163': 08892_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_hastata '3164': 08893_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_hirta '3165': 08894_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_labradorica '3166': 08895_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_lanceolata '3167': 08896_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_lobata '3168': 08897_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_macloskeyi '3169': 08898_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_mirabilis '3170': 08899_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_nuttallii '3171': 08900_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_ocellata '3172': 08901_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_odorata '3173': 08902_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_palmata '3174': 08903_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_pedata '3175': 08904_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_pedatifida '3176': 08905_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_pedunculata '3177': 08906_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_primulifolia '3178': 08907_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_pubescens '3179': 08908_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_purpurea '3180': 08909_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_reichenbachiana '3181': 08910_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_riviniana '3182': 08911_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_rostrata '3183': 08912_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_rotundifolia '3184': 08913_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_sagittata '3185': 08914_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_sempervirens '3186': 08915_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_sororia '3187': 08916_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_striata '3188': 08917_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_tricolor '3189': 08918_Plantae_Tracheophyta_Magnoliopsida_Malvales_Bixaceae_Bixa_orellana '3190': 08919_Plantae_Tracheophyta_Magnoliopsida_Malvales_Bixaceae_Cochlospermum_vitifolium '3191': 08920_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Cistus_albidus '3192': 08921_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Cistus_creticus '3193': 08922_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Cistus_ladanifer '3194': 08923_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Cistus_monspeliensis '3195': 08924_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Cistus_salviifolius '3196': 08925_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Crocanthemum_canadense '3197': 08926_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Crocanthemum_scoparium '3198': 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08949_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Hibiscus_tiliaceus '3221': 08950_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Hibiscus_trionum '3222': 08951_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Hoheria_populnea '3223': 08952_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Horsfordia_newberryi '3224': 08953_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Kosteletzkya_pentacarpos '3225': 08954_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malacothamnus_fasciculatus '3226': 08955_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_arborea '3227': 08956_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_assurgentiflora '3228': 08957_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_moschata '3229': 08958_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_neglecta '3230': 08959_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_parviflora '3231': 08960_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_sylvestris '3232': 08961_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_thuringiaca '3233': 08962_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malvastrum_coromandelianum '3234': 08963_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malvaviscus_arboreus '3235': 08964_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malvella_leprosa '3236': 08965_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Melochia_pyramidata '3237': 08966_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Melochia_tomentosa '3238': 08967_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Modiola_caroliniana '3239': 08968_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Pachira_aquatica '3240': 08969_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Pavonia_lasiopetala '3241': 08970_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Plagianthus_divaricatus '3242': 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09004_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Lythraceae_Trapa_natans '3276': 09005_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Clidemia_hirta '3277': 09006_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Melastoma_malabathricum '3278': 09007_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Rhexia_alifanus '3279': 09008_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Rhexia_mariana '3280': 09009_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Rhexia_virginica '3281': 09010_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Eucalyptus_camaldulensis '3282': 09011_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Eucalyptus_globulus '3283': 09012_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Leptospermum_laevigatum '3284': 09013_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Leptospermum_scoparium '3285': 09014_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Lophomyrtus_bullata '3286': 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09048_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Fuchsia_excorticata '3320': 09049_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Fuchsia_magellanica '3321': 09050_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Lopezia_racemosa '3322': 09051_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Ludwigia_alternifolia '3323': 09052_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Ludwigia_octovalvis '3324': 09053_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Ludwigia_palustris '3325': 09054_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Ludwigia_peploides '3326': 09055_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Ludwigia_peruviana '3327': 09056_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_berlandieri '3328': 09057_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_biennis '3329': 09058_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_cespitosa '3330': 09059_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_curtiflora '3331': 09060_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_deltoides '3332': 09061_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_drummondii '3333': 09062_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_elata '3334': 09063_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_gaura '3335': 09064_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_glaucifolia '3336': 09065_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_glazioviana '3337': 09066_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_hartwegii '3338': 09067_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_laciniata '3339': 09068_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_macrocarpa '3340': 09069_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_rosea '3341': 09070_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_rubricaulis '3342': 09071_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_speciosa '3343': 09072_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_suffrutescens '3344': 09073_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_suffulta '3345': 09074_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_triloba '3346': 09075_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Taraxia_ovata '3347': 09076_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Penaeaceae_Penaea_mucronata '3348': 09077_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Penaeaceae_Saltera_sarcocolla '3349': 09078_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Cabombaceae_Brasenia_schreberi '3350': 09079_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nuphar_advena '3351': 09080_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nuphar_lutea '3352': 09081_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nuphar_polysepala '3353': 09082_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nuphar_variegata '3354': 09083_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nymphaea_alba '3355': 09084_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nymphaea_candida '3356': 09085_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nymphaea_nouchali '3357': 09086_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nymphaea_odorata '3358': 09087_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Cunoniaceae_Bauera_rubioides '3359': 09088_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Cunoniaceae_Weinmannia_racemosa '3360': 09089_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Elaeocarpaceae_Aristotelia_serrata '3361': 09090_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Elaeocarpaceae_Elaeocarpus_hookerianus '3362': 09091_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_acetosella '3363': 09092_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_articulata '3364': 09093_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_corniculata '3365': 09094_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_debilis '3366': 09095_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_dillenii '3367': 09096_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_drummondii '3368': 09097_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_incarnata '3369': 09098_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_latifolia '3370': 09099_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_luteola '3371': 09100_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_montana '3372': 09101_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_obtusa '3373': 09102_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_oregana '3374': 09103_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_pes-caprae '3375': 09104_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_polyphylla '3376': 09105_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_purpurea '3377': 09106_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_stricta '3378': 09107_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_violacea '3379': 09108_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Aristolochia_californica '3380': 09109_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Aristolochia_clematitis '3381': 09110_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Asarum_canadense '3382': 09111_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Asarum_caudatum '3383': 09112_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Asarum_europaeum '3384': 09113_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Endodeca_serpentaria '3385': 09114_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Hexastylis_arifolia '3386': 09115_Plantae_Tracheophyta_Magnoliopsida_Piperales_Piperaceae_Piper_excelsum '3387': 09116_Plantae_Tracheophyta_Magnoliopsida_Piperales_Saururaceae_Anemopsis_californica '3388': 09117_Plantae_Tracheophyta_Magnoliopsida_Piperales_Saururaceae_Houttuynia_cordata '3389': 09118_Plantae_Tracheophyta_Magnoliopsida_Piperales_Saururaceae_Saururus_cernuus '3390': 09119_Plantae_Tracheophyta_Magnoliopsida_Proteales_Nelumbonaceae_Nelumbo_lutea '3391': 09120_Plantae_Tracheophyta_Magnoliopsida_Proteales_Nelumbonaceae_Nelumbo_nucifera '3392': 09121_Plantae_Tracheophyta_Magnoliopsida_Proteales_Platanaceae_Platanus_occidentalis '3393': 09122_Plantae_Tracheophyta_Magnoliopsida_Proteales_Platanaceae_Platanus_racemosa '3394': 09123_Plantae_Tracheophyta_Magnoliopsida_Proteales_Platanaceae_Platanus_wrightii '3395': 09124_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Banksia_integrifolia '3396': 09125_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Banksia_marginata '3397': 09126_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Hakea_sericea '3398': 09127_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Knightia_excelsa '3399': 09128_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Leucadendron_laureolum '3400': 09129_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Leucadendron_rubrum '3401': 09130_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Leucadendron_salignum '3402': 09131_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Leucospermum_conocarpodendron '3403': 09132_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Leucospermum_cuneiforme '3404': 09133_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Mimetes_cucullatus '3405': 09134_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Protea_caffra '3406': 09135_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Protea_cynaroides '3407': 09136_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Protea_laurifolia '3408': 09137_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Protea_neriifolia '3409': 09138_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Protea_nitida '3410': 09139_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Protea_repens '3411': 09140_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Serruria_fasciflora '3412': 09141_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Achlys_triphylla '3413': 09142_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_aquifolium '3414': 09143_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_bealei '3415': 09144_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_darwinii '3416': 09145_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_nervosa '3417': 09146_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_repens '3418': 09147_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_thunbergii '3419': 09148_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_trifoliolata '3420': 09149_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_vulgaris '3421': 09150_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Caulophyllum_giganteum '3422': 09151_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Caulophyllum_thalictroides '3423': 09152_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Jeffersonia_diphylla '3424': 09153_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Nandina_domestica '3425': 09154_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Podophyllum_peltatum '3426': 09155_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Vancouveria_hexandra '3427': 09156_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Menispermaceae_Cocculus_carolinus '3428': 09157_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Menispermaceae_Menispermum_canadense '3429': 09158_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Argemone_albiflora '3430': 09159_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Argemone_mexicana '3431': 09160_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Argemone_munita '3432': 09161_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Argemone_ochroleuca '3433': 09162_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Argemone_polyanthemos '3434': 09163_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Capnoides_sempervirens '3435': 09164_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Chelidonium_majus '3436': 09165_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Corydalis_aurea '3437': 09166_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Corydalis_cava '3438': 09167_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Corydalis_flavula '3439': 09168_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Corydalis_solida '3440': 09169_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Dendromecon_rigida '3441': 09170_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Dicentra_canadensis '3442': 09171_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Dicentra_cucullaria '3443': 09172_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Dicentra_formosa '3444': 09173_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Ehrendorferia_chrysantha '3445': 09174_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Eschscholzia_caespitosa '3446': 09175_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Eschscholzia_californica '3447': 09176_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Eschscholzia_minutiflora '3448': 09177_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Eschscholzia_parishii '3449': 09178_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Fumaria_capreolata '3450': 09179_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Fumaria_muralis '3451': 09180_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Fumaria_officinalis '3452': 09181_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Glaucium_flavum '3453': 09182_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Lamprocapnos_spectabilis '3454': 09183_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Papaver_cambricum '3455': 09184_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Papaver_dubium '3456': 09185_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Papaver_heterophyllum '3457': 09186_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Papaver_rhoeas '3458': 09187_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Papaver_somniferum '3459': 09188_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Platystemon_californicus '3460': 09189_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Pseudofumaria_lutea '3461': 09190_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Romneya_coulteri '3462': 09191_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Sanguinaria_canadensis '3463': 09192_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Stylophorum_diphyllum '3464': 09193_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aconitum_columbianum '3465': 09194_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aconitum_delphiniifolium '3466': 09195_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aconitum_lycoctonum '3467': 09196_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aconitum_napellus '3468': 09197_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aconitum_septentrionale '3469': 09198_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Actaea_pachypoda '3470': 09199_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Actaea_racemosa '3471': 09200_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Actaea_rubra '3472': 09201_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Actaea_spicata '3473': 09202_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Adonis_vernalis '3474': 09203_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemonastrum_canadense '3475': 09204_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemone_berlandieri '3476': 09205_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemone_coronaria '3477': 09206_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemone_cylindrica '3478': 09207_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemone_hortensis '3479': 09208_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemone_multifida '3480': 09209_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemone_virginiana '3481': 09210_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemonoides_altaica '3482': 09211_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemonoides_nemorosa '3483': 09212_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemonoides_quinquefolia '3484': 09213_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemonoides_ranunculoides '3485': 09214_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemonoides_sylvestris '3486': 09215_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_atrata '3487': 09216_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_canadensis '3488': 09217_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_chrysantha '3489': 09218_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_coerulea '3490': 09219_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_flavescens '3491': 09220_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_formosa '3492': 09221_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_vulgaris '3493': 09222_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Caltha_leptosepala '3494': 09223_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Caltha_palustris '3495': 09224_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ceratocephala_testiculata '3496': 09225_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_alpina '3497': 09226_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_crispa '3498': 09227_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_drummondii '3499': 09228_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_lasiantha '3500': 09229_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_ligusticifolia '3501': 09230_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_occidentalis '3502': 09231_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_paniculata '3503': 09232_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_pitcheri '3504': 09233_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_terniflora '3505': 09234_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_texensis '3506': 09235_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_virginiana '3507': 09236_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_vitalba '3508': 09237_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Consolida_regalis '3509': 09238_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Coptis_trifolia '3510': 09239_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_cardinale '3511': 09240_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_carolinianum '3512': 09241_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_glaucum '3513': 09242_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_nudicaule '3514': 09243_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_nuttallianum '3515': 09244_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_parishii '3516': 09245_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_tricorne '3517': 09246_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Enemion_biternatum '3518': 09247_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Eranthis_hyemalis '3519': 09248_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ficaria_verna '3520': 09249_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Helleborus_foetidus '3521': 09250_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Hepatica_acutiloba '3522': 09251_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Hepatica_americana '3523': 09252_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Hepatica_nobilis '3524': 09253_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Hydrastis_canadensis '3525': 09254_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Nigella_damascena '3526': 09255_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Pulsatilla_alpina '3527': 09256_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Pulsatilla_nuttalliana '3528': 09257_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Pulsatilla_occidentalis '3529': 09258_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Pulsatilla_patens '3530': 09259_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_abortivus '3531': 09260_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_acris '3532': 09261_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_aquatilis '3533': 09262_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_auricomus '3534': 09263_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_bulbosus '3535': 09264_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_californicus '3536': 09265_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_cassubicus '3537': 09266_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_fascicularis '3538': 09267_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_flammula '3539': 09268_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_glaberrimus '3540': 09269_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_hispidus '3541': 09270_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_muricatus '3542': 09271_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_occidentalis '3543': 09272_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_polyanthemos '3544': 09273_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_recurvatus '3545': 09274_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_repens '3546': 09275_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_sceleratus '3547': 09276_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_aquilegiifolium '3548': 09277_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_dasycarpum '3549': 09278_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_dioicum '3550': 09279_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_fendleri '3551': 09280_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_lucidum '3552': 09281_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_minus '3553': 09282_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_pubescens '3554': 09283_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_thalictroides '3555': 09284_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Trollius_asiaticus '3556': 09285_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Trollius_europaeus '3557': 09286_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Trollius_laxus '3558': 09287_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Xanthorhiza_simplicissima '3559': 09288_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Cannabis_sativa '3560': 09289_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Celtis_laevigata '3561': 09290_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Celtis_occidentalis '3562': 09291_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Celtis_pallida '3563': 09292_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Celtis_reticulata '3564': 09293_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Humulus_japonicus '3565': 09294_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Humulus_lupulus '3566': 09295_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Trema_orientalis '3567': 09296_Plantae_Tracheophyta_Magnoliopsida_Rosales_Elaeagnaceae_Elaeagnus_angustifolia '3568': 09297_Plantae_Tracheophyta_Magnoliopsida_Rosales_Elaeagnaceae_Elaeagnus_commutata '3569': 09298_Plantae_Tracheophyta_Magnoliopsida_Rosales_Elaeagnaceae_Elaeagnus_pungens '3570': 09299_Plantae_Tracheophyta_Magnoliopsida_Rosales_Elaeagnaceae_Elaeagnus_umbellata '3571': 09300_Plantae_Tracheophyta_Magnoliopsida_Rosales_Elaeagnaceae_Hippophae_rhamnoides '3572': 09301_Plantae_Tracheophyta_Magnoliopsida_Rosales_Elaeagnaceae_Shepherdia_canadensis '3573': 09302_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Artocarpus_altilis '3574': 09303_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Artocarpus_heterophyllus '3575': 09304_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Broussonetia_papyrifera '3576': 09305_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Fatoua_villosa '3577': 09306_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Ficus_aurea '3578': 09307_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Ficus_carica '3579': 09308_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Ficus_microcarpa '3580': 09309_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Ficus_petiolaris '3581': 09310_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Ficus_pumila '3582': 09311_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Ficus_religiosa '3583': 09312_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Maclura_pomifera '3584': 09313_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Morus_alba '3585': 09314_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Morus_indica '3586': 09315_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Morus_rubra '3587': 09316_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Streblus_heterophyllus '3588': 09317_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Berchemia_scandens '3589': 09318_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_americanus '3590': 09319_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_cordulatus '3591': 09320_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_crassifolius '3592': 09321_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_cuneatus '3593': 09322_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_fendleri '3594': 09323_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_herbaceus '3595': 09324_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_integerrimus '3596': 09325_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_leucodermis '3597': 09326_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_megacarpus '3598': 09327_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_oliganthus '3599': 09328_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_perplexans '3600': 09329_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_prostratus '3601': 09330_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_spinosus '3602': 09331_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_thyrsiflorus '3603': 09332_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_velutinus '3604': 09333_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_verrucosus '3605': 09334_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Colubrina_texensis '3606': 09335_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Condalia_hookeri '3607': 09336_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Discaria_toumatou '3608': 09337_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Frangula_alnus '3609': 09338_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Frangula_californica '3610': 09339_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Frangula_caroliniana '3611': 09340_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Frangula_purshiana '3612': 09341_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Paliurus_spina-christi '3613': 09342_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Rhamnus_alaternus '3614': 09343_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Rhamnus_alnifolia '3615': 09344_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Rhamnus_cathartica '3616': 09345_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Rhamnus_crocea '3617': 09346_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Rhamnus_humboldtiana '3618': 09347_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Rhamnus_ilicifolia '3619': 09348_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Trichocephalus_stipularis '3620': 09349_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ziziphus_obtusifolia '3621': 09350_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Acaena_anserinifolia '3622': 09351_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Acaena_novae-zelandiae '3623': 09352_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Adenostoma_fasciculatum '3624': 09353_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Adenostoma_sparsifolium '3625': 09354_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Agrimonia_eupatoria '3626': 09355_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Agrimonia_gryposepala '3627': 09356_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Agrimonia_parviflora '3628': 09357_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Agrimonia_pilosa '3629': 09358_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Amelanchier_alnifolia '3630': 09359_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Amelanchier_arborea '3631': 09360_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Amelanchier_spicata '3632': 09361_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Amelanchier_utahensis '3633': 09362_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Argentina_anserina '3634': 09363_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Aronia_melanocarpa '3635': 09364_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Aruncus_dioicus '3636': 09365_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Cercocarpus_betuloides '3637': 09366_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Cercocarpus_ledifolius '3638': 09367_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Cercocarpus_montanus '3639': 09368_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Chamaebatia_foliolosa '3640': 09369_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Chamaebatiaria_millefolium '3641': 09370_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Coleogyne_ramosissima '3642': 09371_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Comarum_palustre '3643': 09372_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Crataegus_marshallii '3644': 09373_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Crataegus_monogyna '3645': 09374_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Dasiphora_fruticosa '3646': 09375_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Dryas_drummondii '3647': 09376_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Dryas_octopetala '3648': 09377_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Drymocallis_arguta '3649': 09378_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Drymocallis_glandulosa '3650': 09379_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Eriobotrya_japonica '3651': 09380_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Fallugia_paradoxa '3652': 09381_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Filipendula_rubra '3653': 09382_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Filipendula_ulmaria '3654': 09383_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Filipendula_vulgaris '3655': 09384_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Fragaria_chiloensis '3656': 09385_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Fragaria_vesca '3657': 09386_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Fragaria_virginiana '3658': 09387_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Fragaria_viridis '3659': 09388_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_aleppicum '3660': 09389_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_canadense '3661': 09390_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_macrophyllum '3662': 09391_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_rivale '3663': 09392_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_rossii '3664': 09393_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_triflorum '3665': 09394_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_urbanum '3666': 09395_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Heteromeles_arbutifolia '3667': 09396_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Holodiscus_discolor '3668': 09397_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Kerria_japonica '3669': 09398_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Luetkea_pectinata '3670': 09399_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Malus_domestica '3671': 09400_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Malus_fusca '3672': 09401_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Malus_pumila '3673': 09402_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Oemleria_cerasiformis '3674': 09403_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Petrophytum_caespitosum '3675': 09404_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Photinia_serratifolia '3676': 09405_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Physocarpus_capitatus '3677': 09406_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Physocarpus_opulifolius '3678': 09407_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_argentea '3679': 09408_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_canadensis '3680': 09409_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_erecta '3681': 09410_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_gracilis '3682': 09411_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_indica '3683': 09412_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_norvegica '3684': 09413_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_recta '3685': 09414_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_reptans '3686': 09415_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_simplex '3687': 09416_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_americana '3688': 09417_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_angustifolia '3689': 09418_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_avium '3690': 09419_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_caroliniana '3691': 09420_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_cerasifera '3692': 09421_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_dulcis '3693': 09422_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_emarginata '3694': 09423_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_fasciculata '3695': 09424_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_fremontii '3696': 09425_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_ilicifolia '3697': 09426_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_laurocerasus '3698': 09427_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_mexicana '3699': 09428_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_padus '3700': 09429_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_pensylvanica '3701': 09430_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_persica '3702': 09431_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_pumila '3703': 09432_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_serotina '3704': 09433_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_spinosa '3705': 09434_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_virginiana '3706': 09435_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Purshia_stansburyana '3707': 09436_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Purshia_tridentata '3708': 09437_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Pyracantha_coccinea '3709': 09438_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Pyrus_calleryana '3710': 09439_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Pyrus_communis '3711': 09440_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rhaphiolepis_indica '3712': 09441_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rhodotypos_scandens '3713': 09442_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_acicularis '3714': 09443_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_arkansana '3715': 09444_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_bracteata '3716': 09445_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_californica '3717': 09446_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_canina '3718': 09447_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_carolina '3719': 09448_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_gymnocarpa '3720': 09449_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_majalis '3721': 09450_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_multiflora '3722': 09451_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_nutkana '3723': 09452_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_palustris '3724': 09453_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_rubiginosa '3725': 09454_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_rugosa '3726': 09455_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_spinosissima '3727': 09456_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_woodsii '3728': 09457_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_allegheniensis '3729': 09458_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_arcticus '3730': 09459_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_armeniacus '3731': 09460_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_bifrons '3732': 09461_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_caesius '3733': 09462_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_chamaemorus '3734': 09463_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_cissoides '3735': 09464_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_dalibarda '3736': 09465_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_flagellaris '3737': 09466_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_hispidus '3738': 09467_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_idaeus '3739': 09468_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_laciniatus '3740': 09469_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_leucodermis '3741': 09470_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_occidentalis '3742': 09471_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_odoratus '3743': 09472_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_parviflorus '3744': 09473_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_pedatus '3745': 09474_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_phoenicolasius '3746': 09475_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_pubescens '3747': 09476_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_saxatilis '3748': 09477_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_schmidelioides '3749': 09478_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_spectabilis '3750': 09479_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_trivialis '3751': 09480_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_ulmifolius '3752': 09481_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_ursinus '3753': 09482_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sanguisorba_canadensis '3754': 09483_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sanguisorba_minor '3755': 09484_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sanguisorba_officinalis '3756': 09485_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sibbaldia_procumbens '3757': 09486_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sibbaldiopsis_tridentata '3758': 09487_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sorbaria_sorbifolia '3759': 09488_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sorbus_americana '3760': 09489_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sorbus_aucuparia '3761': 09490_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sorbus_sitchensis '3762': 09491_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Spiraea_alba '3763': 09492_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Spiraea_betulifolia '3764': 09493_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Spiraea_douglasii '3765': 09494_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Spiraea_japonica '3766': 09495_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Spiraea_splendens '3767': 09496_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Spiraea_tomentosa '3768': 09497_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Waldsteinia_fragarioides '3769': 09498_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_alata '3770': 09499_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_americana '3771': 09500_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_crassifolia '3772': 09501_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_glabra '3773': 09502_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_laevis '3774': 09503_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_minor '3775': 09504_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_parvifolia '3776': 09505_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_pumila '3777': 09506_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_rubra '3778': 09507_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_thomasii '3779': 09508_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Boehmeria_cylindrica '3780': 09509_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Elatostema_rugosum '3781': 09510_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Laportea_canadensis '3782': 09511_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Parietaria_judaica '3783': 09512_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Parietaria_pensylvanica '3784': 09513_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Pilea_microphylla '3785': 09514_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Pilea_pumila '3786': 09515_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Urtica_chamaedryoides '3787': 09516_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Urtica_dioica '3788': 09517_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Urtica_ferox '3789': 09518_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Urtica_urens '3790': 09519_Plantae_Tracheophyta_Magnoliopsida_Santalales_Loranthaceae_Ileostylus_micranthus '3791': 09520_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Arceuthobium_campylopodum '3792': 09521_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Colpoon_compressum '3793': 09522_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Comandra_umbellata '3794': 09523_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Exocarpos_cupressiformis '3795': 09524_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Geocaulon_lividum '3796': 09525_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Phoradendron_californicum '3797': 09526_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Phoradendron_leucarpum '3798': 09527_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Viscum_album '3799': 09528_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Anacardium_occidentale '3800': 09529_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Cotinus_coggygria '3801': 09530_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Malosma_laurina '3802': 09531_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Mangifera_indica '3803': 09532_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Pistacia_chinensis '3804': 09533_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Pistacia_lentiscus '3805': 09534_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_aromatica '3806': 09535_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_copallinum '3807': 09536_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_glabra '3808': 09537_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_integrifolia '3809': 09538_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_lanceolata '3810': 09539_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_microphylla '3811': 09540_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_ovata '3812': 09541_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_typhina '3813': 09542_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_virens '3814': 09543_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Schinus_molle '3815': 09544_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Schinus_terebinthifolia '3816': 09545_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Searsia_lucida '3817': 09546_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Toxicodendron_diversilobum '3818': 09547_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Toxicodendron_radicans '3819': 09548_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Toxicodendron_rydbergii '3820': 09549_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Toxicodendron_vernix '3821': 09550_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Burseraceae_Bursera_microphylla '3822': 09551_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Burseraceae_Bursera_simaruba '3823': 09552_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Meliaceae_Azadirachta_indica '3824': 09553_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Meliaceae_Dysoxylum_spectabile '3825': 09554_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Meliaceae_Melia_azedarach '3826': 09555_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Citrus_trifoliata '3827': 09556_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Cneoridium_dumosum '3828': 09557_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Correa_reflexa '3829': 09558_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Murraya_paniculata '3830': 09559_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Ptelea_trifoliata '3831': 09560_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Thamnosma_montana '3832': 09561_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Zanthoxylum_americanum '3833': 09562_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Zanthoxylum_clava-herculis '3834': 09563_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Zanthoxylum_fagara '3835': 09564_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Zanthoxylum_hirsutum '3836': 09565_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_campestre '3837': 09566_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_circinatum '3838': 09567_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_floridanum '3839': 09568_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_ginnala '3840': 09569_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_glabrum '3841': 09570_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_grandidentatum '3842': 09571_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_macrophyllum '3843': 09572_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_negundo '3844': 09573_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_nigrum '3845': 09574_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_palmatum '3846': 09575_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_pensylvanicum '3847': 09576_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_platanoides '3848': 09577_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_pseudoplatanus '3849': 09578_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_rubrum '3850': 09579_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_saccharinum '3851': 09580_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_saccharum '3852': 09581_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_spicatum '3853': 09582_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_tataricum '3854': 09583_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Aesculus_californica '3855': 09584_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Aesculus_glabra '3856': 09585_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Aesculus_hippocastanum '3857': 09586_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Aesculus_pavia '3858': 09587_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Alectryon_excelsus '3859': 09588_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Cardiospermum_halicacabum '3860': 09589_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Dodonaea_viscosa '3861': 09590_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Koelreuteria_paniculata '3862': 09591_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Sapindus_drummondii '3863': 09592_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Ungnadia_speciosa '3864': 09593_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Simaroubaceae_Ailanthus_altissima '3865': 09594_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Altingiaceae_Liquidambar_styraciflua '3866': 09595_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Cotyledon_orbiculata '3867': 09596_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Crassula_connata '3868': 09597_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Crassula_ovata '3869': 09598_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_arizonica '3870': 09599_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_cymosa '3871': 09600_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_edulis '3872': 09601_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_farinosa '3873': 09602_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_lanceolata '3874': 09603_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_pulverulenta '3875': 09604_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_saxosa '3876': 09605_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Echeveria_gibbiflora '3877': 09606_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Hylotelephium_telephium '3878': 09607_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Kalanchoe_delagoensis '3879': 09608_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Kalanchoe_pinnata '3880': 09609_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Orostachys_spinosa '3881': 09610_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Rhodiola_integrifolia '3882': 09611_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Rhodiola_rosea '3883': 09612_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_acre '3884': 09613_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_album '3885': 09614_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_dasyphyllum '3886': 09615_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_lanceolatum '3887': 09616_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_nuttallii '3888': 09617_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_obtusatum '3889': 09618_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_sexangulare '3890': 09619_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_spathulifolium '3891': 09620_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_ternatum '3892': 09621_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sempervivum_arachnoideum '3893': 09622_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sempervivum_tectorum '3894': 09623_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Tylecodon_paniculatus '3895': 09624_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Umbilicus_rupestris '3896': 09625_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_americanum '3897': 09626_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_aureum '3898': 09627_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_bracteosum '3899': 09628_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_cereum '3900': 09629_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_cynosbati '3901': 09630_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_indecorum '3902': 09631_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_lacustre '3903': 09632_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_malvaceum '3904': 09633_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_nevadense '3905': 09634_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_nigrum '3906': 09635_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_roezlii '3907': 09636_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_rubrum '3908': 09637_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_sanguineum '3909': 09638_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_speciosum '3910': 09639_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_triste '3911': 09640_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_uva-crispa '3912': 09641_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Haloragaceae_Haloragis_erecta '3913': 09642_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Haloragaceae_Myriophyllum_aquaticum '3914': 09643_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Haloragaceae_Myriophyllum_spicatum '3915': 09644_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Hamamelidaceae_Hamamelis_virginiana '3916': 09645_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Iteaceae_Itea_virginica '3917': 09646_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Paeoniaceae_Paeonia_brownii '3918': 09647_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Paeoniaceae_Paeonia_californica '3919': 09648_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Penthoraceae_Penthorum_sedoides '3920': 09649_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Chrysosplenium_alternifolium '3921': 09650_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Chrysosplenium_americanum '3922': 09651_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Darmera_peltata '3923': 09652_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Heuchera_cylindrica '3924': 09653_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Heuchera_micrantha '3925': 09654_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Heuchera_richardsonii '3926': 09655_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Leptarrhena_pyrolifolia '3927': 09656_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Lithophragma_affine '3928': 09657_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Lithophragma_heterophyllum '3929': 09658_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Lithophragma_parviflorum '3930': 09659_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Micranthes_californica '3931': 09660_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Micranthes_ferruginea '3932': 09661_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Micranthes_pensylvanica '3933': 09662_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Micranthes_virginiensis '3934': 09663_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Mitella_diphylla '3935': 09664_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Mitella_nuda '3936': 09665_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Saxifraga_aizoides '3937': 09666_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Saxifraga_bronchialis '3938': 09667_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Saxifraga_granulata '3939': 09668_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Saxifraga_oppositifolia '3940': 09669_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Saxifraga_paniculata '3941': 09670_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Saxifraga_tricuspidata '3942': 09671_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Tellima_grandiflora '3943': 09672_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Tiarella_cordifolia '3944': 09673_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Tiarella_trifoliata '3945': 09674_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Tolmiea_menziesii '3946': 09675_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Whipplea_modesta '3947': 09676_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Calystegia_macrostegia '3948': 09677_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Calystegia_sepium '3949': 09678_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Calystegia_silvatica '3950': 09679_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Calystegia_soldanella '3951': 09680_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Convolvulus_althaeoides '3952': 09681_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Convolvulus_arvensis '3953': 09682_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Convolvulus_equitans '3954': 09683_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Cuscuta_californica '3955': 09684_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Cuscuta_gronovii '3956': 09685_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Cuscuta_pacifica '3957': 09686_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Dichondra_carolinensis '3958': 09687_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Dichondra_repens '3959': 09688_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Distimake_dissectus '3960': 09689_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Evolvulus_alsinoides '3961': 09690_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Evolvulus_sericeus '3962': 09691_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_alba '3963': 09692_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_batatas '3964': 09693_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_cairica '3965': 09694_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_carnea '3966': 09695_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_coccinea '3967': 09696_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_cordatotriloba '3968': 09697_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_hederacea '3969': 09698_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_hederifolia '3970': 09699_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_imperati '3971': 09700_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_indica '3972': 09701_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_lacunosa '3973': 09702_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_obscura '3974': 09703_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_pandurata '3975': 09704_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_pes-caprae '3976': 09705_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_purpurea '3977': 09706_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_quamoclit '3978': 09707_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_sagittata '3979': 09708_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_triloba '3980': 09709_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Jacquemontia_tamnifolia '3981': 09710_Plantae_Tracheophyta_Magnoliopsida_Solanales_Hydroleaceae_Hydrolea_ovata '3982': 09711_Plantae_Tracheophyta_Magnoliopsida_Solanales_Montiniaceae_Montinia_caryophyllacea '3983': 09712_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Alkekengi_officinarum '3984': 09713_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Atropa_belladonna '3985': 09714_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Capsicum_annuum '3986': 09715_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Datura_discolor '3987': 09716_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Datura_inoxia '3988': 09717_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Datura_stramonium '3989': 09718_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Datura_wrightii '3990': 09719_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Hyoscyamus_niger '3991': 09720_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Lycium_andersonii '3992': 09721_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Lycium_barbarum '3993': 09722_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Lycium_carolinianum '3994': 09723_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Lycium_ferocissimum '3995': 09724_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Nicandra_physalodes '3996': 09725_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Nicotiana_glauca '3997': 09726_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Nicotiana_obtusifolia '3998': 09727_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Physalis_angulata '3999': 09728_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Physalis_crassifolia '4000': 09729_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Physalis_heterophylla '4001': 09730_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Physalis_peruviana '4002': 09731_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Quincula_lobata '4003': 09732_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_americanum '4004': 09733_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_carolinense '4005': 09734_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_chenopodioides '4006': 09735_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_dimidiatum '4007': 09736_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_diphyllum '4008': 09737_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_douglasii '4009': 09738_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_dulcamara '4010': 09739_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_elaeagnifolium '4011': 09740_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_emulans '4012': 09741_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_erianthum '4013': 09742_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_laciniatum '4014': 09743_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_lycopersicum '4015': 09744_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_mauritianum '4016': 09745_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_nigrum '4017': 09746_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_pseudocapsicum '4018': 09747_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_rostratum '4019': 09748_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_tuberosum '4020': 09749_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_umbelliferum '4021': 09750_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_xanti '4022': 09751_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Ampelopsis_arborea '4023': 09752_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Ampelopsis_cordata '4024': 09753_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Ampelopsis_glandulosa '4025': 09754_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Cayratia_japonica '4026': 09755_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Cissus_trifoliata '4027': 09756_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Parthenocissus_inserta '4028': 09757_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Parthenocissus_quinquefolia '4029': 09758_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Parthenocissus_tricuspidata '4030': 09759_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Vitis_aestivalis '4031': 09760_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Vitis_arizonica '4032': 09761_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Vitis_californica '4033': 09762_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Vitis_mustangensis '4034': 09763_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Vitis_riparia '4035': 09764_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Vitis_rotundifolia '4036': 09765_Plantae_Tracheophyta_Magnoliopsida_Zygophyllales_Krameriaceae_Krameria_bicolor '4037': 09766_Plantae_Tracheophyta_Magnoliopsida_Zygophyllales_Krameriaceae_Krameria_erecta '4038': 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09788_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Juniperus_scopulorum '4060': 09789_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Juniperus_virginiana '4061': 09790_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Sequoia_sempervirens '4062': 09791_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Sequoiadendron_giganteum '4063': 09792_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Taxodium_ascendens '4064': 09793_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Taxodium_distichum '4065': 09794_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Taxodium_mucronatum '4066': 09795_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Thuja_occidentalis '4067': 09796_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Thuja_plicata '4068': 09797_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Abies_alba '4069': 09798_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Abies_balsamea '4070': 09799_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Abies_concolor '4071': 09800_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Abies_grandis '4072': 09801_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Abies_lasiocarpa '4073': 09802_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Abies_sibirica '4074': 09803_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Larix_decidua '4075': 09804_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Larix_laricina '4076': 09805_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Larix_occidentalis '4077': 09806_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Larix_sibirica '4078': 09807_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_abies '4079': 09808_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_engelmannii '4080': 09809_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_glauca '4081': 09810_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_mariana '4082': 09811_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_obovata '4083': 09812_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_pungens '4084': 09813_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_rubens '4085': 09814_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_sitchensis '4086': 09815_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_albicaulis '4087': 09816_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_banksiana '4088': 09817_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_cembroides '4089': 09818_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_contorta '4090': 09819_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_coulteri '4091': 09820_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_echinata '4092': 09821_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_edulis '4093': 09822_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_elliottii '4094': 09823_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_flexilis '4095': 09824_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_jeffreyi '4096': 09825_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_lambertiana '4097': 09826_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_longaeva '4098': 09827_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_monophylla '4099': 09828_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_monticola '4100': 09829_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_nigra '4101': 09830_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_palustris '4102': 09831_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_pinaster '4103': 09832_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_pinea '4104': 09833_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_ponderosa '4105': 09834_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_radiata '4106': 09835_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_resinosa '4107': 09836_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_rigida '4108': 09837_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_sabiniana '4109': 09838_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_sibirica '4110': 09839_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_strobus '4111': 09840_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_sylvestris '4112': 09841_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_taeda '4113': 09842_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_torreyana '4114': 09843_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_virginiana '4115': 09844_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pseudotsuga_menziesii '4116': 09845_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Tsuga_canadensis '4117': 09846_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Tsuga_heterophylla '4118': 09847_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Tsuga_mertensiana '4119': 09848_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Dacrycarpus_dacrydioides '4120': 09849_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Dacrydium_cupressinum '4121': 09850_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Phyllocladus_alpinus '4122': 09851_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Phyllocladus_trichomanoides '4123': 09852_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Podocarpus_laetus '4124': 09853_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Podocarpus_totara '4125': 09854_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Prumnopitys_ferruginea '4126': 09855_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Prumnopitys_taxifolia '4127': 09856_Plantae_Tracheophyta_Pinopsida_Pinales_Taxaceae_Taxus_baccata '4128': 09857_Plantae_Tracheophyta_Pinopsida_Pinales_Taxaceae_Taxus_brevifolia '4129': 09858_Plantae_Tracheophyta_Pinopsida_Pinales_Taxaceae_Taxus_canadensis '4130': 09859_Plantae_Tracheophyta_Pinopsida_Pinales_Taxaceae_Torreya_californica '4131': 09860_Plantae_Tracheophyta_Polypodiopsida_Cyatheales_Cyatheaceae_Cyathea_dealbata '4132': 09861_Plantae_Tracheophyta_Polypodiopsida_Cyatheales_Cyatheaceae_Cyathea_medullaris '4133': 09862_Plantae_Tracheophyta_Polypodiopsida_Cyatheales_Cyatheaceae_Cyathea_smithii '4134': 09863_Plantae_Tracheophyta_Polypodiopsida_Cyatheales_Dicksoniaceae_Dicksonia_antarctica '4135': 09864_Plantae_Tracheophyta_Polypodiopsida_Cyatheales_Dicksoniaceae_Dicksonia_fibrosa '4136': 09865_Plantae_Tracheophyta_Polypodiopsida_Cyatheales_Dicksoniaceae_Dicksonia_squarrosa '4137': 09866_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_arvense '4138': 09867_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_fluviatile '4139': 09868_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_hyemale '4140': 09869_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_laevigatum '4141': 09870_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_pratense '4142': 09871_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_ramosissimum '4143': 09872_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_scirpoides '4144': 09873_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_sylvaticum '4145': 09874_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_telmateia '4146': 09875_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_variegatum '4147': 09876_Plantae_Tracheophyta_Polypodiopsida_Gleicheniales_Gleicheniaceae_Dicranopteris_linearis '4148': 09877_Plantae_Tracheophyta_Polypodiopsida_Gleicheniales_Gleicheniaceae_Gleichenia_dicarpa '4149': 09878_Plantae_Tracheophyta_Polypodiopsida_Gleicheniales_Gleicheniaceae_Sticherus_cunninghamii '4150': 09879_Plantae_Tracheophyta_Polypodiopsida_Hymenophyllales_Hymenophyllaceae_Hymenophyllum_demissum '4151': 09880_Plantae_Tracheophyta_Polypodiopsida_Hymenophyllales_Hymenophyllaceae_Hymenophyllum_flabellatum '4152': 09881_Plantae_Tracheophyta_Polypodiopsida_Hymenophyllales_Hymenophyllaceae_Hymenophyllum_nephrophyllum '4153': 09882_Plantae_Tracheophyta_Polypodiopsida_Hymenophyllales_Hymenophyllaceae_Trichomanes_venosum '4154': 09883_Plantae_Tracheophyta_Polypodiopsida_Ophioglossales_Ophioglossaceae_Botrychium_lunaria '4155': 09884_Plantae_Tracheophyta_Polypodiopsida_Ophioglossales_Ophioglossaceae_Botrypus_virginianus '4156': 09885_Plantae_Tracheophyta_Polypodiopsida_Ophioglossales_Ophioglossaceae_Sceptridium_dissectum '4157': 09886_Plantae_Tracheophyta_Polypodiopsida_Ophioglossales_Ophioglossaceae_Sceptridium_multifidum '4158': 09887_Plantae_Tracheophyta_Polypodiopsida_Osmundales_Osmundaceae_Leptopteris_hymenophylloides '4159': 09888_Plantae_Tracheophyta_Polypodiopsida_Osmundales_Osmundaceae_Osmunda_claytoniana '4160': 09889_Plantae_Tracheophyta_Polypodiopsida_Osmundales_Osmundaceae_Osmunda_regalis '4161': 09890_Plantae_Tracheophyta_Polypodiopsida_Osmundales_Osmundaceae_Osmundastrum_cinnamomeum '4162': 09891_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_appendiculatum '4163': 09892_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_bulbiferum '4164': 09893_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_ceterach '4165': 09894_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_flabellifolium '4166': 09895_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_flaccidum '4167': 09896_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_gracillimum '4168': 09897_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_hookerianum '4169': 09898_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_montanum '4170': 09899_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_nidus '4171': 09900_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_oblongifolium '4172': 09901_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_platyneuron '4173': 09902_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_polyodon '4174': 09903_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_rhizophyllum '4175': 09904_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_ruta-muraria '4176': 09905_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_scolopendrium '4177': 09906_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_trichomanes '4178': 09907_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_viride '4179': 09908_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Athyriaceae_Athyrium_angustum '4180': 09909_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Athyriaceae_Athyrium_filix-femina '4181': 09910_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Athyriaceae_Deparia_acrostichoides '4182': 09911_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Blechnum_chambersii '4183': 09912_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Blechnum_filiforme '4184': 09913_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Blechnum_novae-zelandiae '4185': 09914_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Blechnum_parrisiae '4186': 09915_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Blechnum_penna-marina '4187': 09916_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Blechnum_procerum '4188': 09917_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Cranfillia_fluviatilis '4189': 09918_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Lomaria_discolor '4190': 09919_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Struthiopteris_spicant '4191': 09920_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Telmatoblechnum_serrulatum '4192': 09921_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Woodwardia_areolata '4193': 09922_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Woodwardia_fimbriata '4194': 09923_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Woodwardia_virginica '4195': 09924_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Cystopteridaceae_Cystopteris_bulbifera '4196': 09925_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Cystopteridaceae_Cystopteris_fragilis '4197': 09926_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Cystopteridaceae_Cystopteris_protrusa '4198': 09927_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Cystopteridaceae_Gymnocarpium_dryopteris '4199': 09928_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Dennstaedtia_punctilobula '4200': 09929_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Histiopteris_incisa '4201': 09930_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Hypolepis_ambigua '4202': 09931_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Paesia_scaberula '4203': 09932_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Pteridium_aquilinum '4204': 09933_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Pteridium_esculentum '4205': 09934_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Pteridium_pinetorum '4206': 09935_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Diplaziopsidaceae_Homalosorus_pycnocarpos '4207': 09936_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Cyrtomium_falcatum '4208': 09937_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_arguta '4209': 09938_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_carthusiana '4210': 09939_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_cristata '4211': 09940_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_expansa '4212': 09941_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_filix-mas '4213': 09942_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_fragrans '4214': 09943_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_intermedia '4215': 09944_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_marginalis '4216': 09945_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Polystichum_acrostichoides '4217': 09946_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Polystichum_lonchitis '4218': 09947_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Polystichum_munitum '4219': 09948_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Polystichum_neozelandicum '4220': 09949_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Polystichum_vestitum '4221': 09950_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Rumohra_adiantiformis '4222': 09951_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Nephrolepidaceae_Nephrolepis_cordifolia '4223': 09952_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Onocleaceae_Matteuccia_struthiopteris '4224': 09953_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Onocleaceae_Onoclea_sensibilis '4225': 09954_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Microsorum_pustulatum '4226': 09955_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Microsorum_scandens '4227': 09956_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Notogrammitis_heterophylla '4228': 09957_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Phlebodium_aureum '4229': 09958_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Pleopeltis_michauxiana '4230': 09959_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Polypodium_californicum '4231': 09960_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Polypodium_glycyrrhiza '4232': 09961_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Polypodium_scouleri '4233': 09962_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Polypodium_virginianum '4234': 09963_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Polypodium_vulgare '4235': 09964_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Pyrrosia_eleagnifolia '4236': 09965_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Acrostichum_danaeifolium '4237': 09966_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Adiantum_aleuticum '4238': 09967_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Adiantum_capillus-veneris '4239': 09968_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Adiantum_cunninghamii '4240': 09969_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Adiantum_hispidulum '4241': 09970_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Adiantum_jordanii '4242': 09971_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Adiantum_pedatum '4243': 09972_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Aspidotis_densa '4244': 09973_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Astrolepis_sinuata '4245': 09974_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Cryptogramma_acrostichoides '4246': 09975_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Myriopteris_alabamensis '4247': 09976_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Myriopteris_aurea '4248': 09977_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Myriopteris_parryi '4249': 09978_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_andromedifolia '4250': 09979_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_atropurpurea '4251': 09980_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_glabella '4252': 09981_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_mucronata '4253': 09982_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_rotundifolia '4254': 09983_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pentagramma_triangularis '4255': 09984_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pteris_cretica '4256': 09985_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pteris_macilenta '4257': 09986_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pteris_tremula '4258': 09987_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pteris_vittata '4259': 09988_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Parathelypteris_noveboracensis '4260': 09989_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Phegopteris_connectilis '4261': 09990_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Phegopteris_hexagonoptera '4262': 09991_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Pneumatopteris_pennigera '4263': 09992_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Thelypteris_palustris '4264': 09993_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Woodsiaceae_Woodsia_ilvensis '4265': 09994_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Woodsiaceae_Woodsia_obtusa '4266': 09995_Plantae_Tracheophyta_Polypodiopsida_Psilotales_Psilotaceae_Psilotum_nudum '4267': 09996_Plantae_Tracheophyta_Polypodiopsida_Psilotales_Psilotaceae_Tmesipteris_elongata '4268': 09997_Plantae_Tracheophyta_Polypodiopsida_Salviniales_Salviniaceae_Azolla_filiculoides '4269': 09998_Plantae_Tracheophyta_Polypodiopsida_Salviniales_Salviniaceae_Salvinia_minima '4270': 09999_Plantae_Tracheophyta_Polypodiopsida_Schizaeales_Lygodiaceae_Lygodium_japonicum splits: - name: train num_bytes: 25180455748.65 num_examples: 213550 download_size: 7778993107 dataset_size: 25180455748.65 --- # Dataset Card for "inat_2021_train_mini_plantae" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
one-sec-cv12/chunk_170	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 23363195760.375 num_examples: 243245 download_size: 21013363110 dataset_size: 23363195760.375 --- # Dataset Card for "chunk_170" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
james-burton/OrientalMuseum_min6-num	--- dataset_info: features: - name: label dtype: class_label: names: '0': DUROM.1950.10.a-b '1': DUROM.1954.Spalding29.W '2': DUROM.1954.Spalding32.a-j '3': DUROM.1960.1012.a-b '4': DUROM.1960.1215.a-b '5': DUROM.1960.1276.a-b '6': DUROM.1960.1885.c '7': DUROM.1960.1951.a-b '8': DUROM.1960.2224.a-b '9': DUROM.1960.2255.a-c '10': DUROM.1960.2395.A-B '11': DUROM.1960.2456.b '12': DUROM.1960.3200.h '13': DUROM.1960.3253.a-b '14': DUROM.1960.3295.A-B '15': DUROM.1960.3400.a-b '16': DUROM.1960.3969.a-b '17': DUROM.1960.412.a-b '18': DUROM.1960.589.a-b '19': DUROM.1960.592.a-b '20': DUROM.1960.891.c '21': DUROM.1960.945.c '22': DUROM.1961.27.B '23': DUROM.1961.31.B '24': DUROM.1961.40.a-b '25': DUROM.1961.44.c '26': DUROM.1962.251.a '27': DUROM.1962.99.a-c '28': DUROM.1963.52.A '29': DUROM.1967.23.k '30': DUROM.1967.40.a-b '31': DUROM.1968.178.a-b '32': DUROM.1968.185.a-b '33': DUROM.1968.43.d-e '34': DUROM.1968.64.a-b '35': DUROM.1968.72.b '36': 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'1208': eg5145 '1209': eg5149 '1210': eg5169 '1211': eg517 '1212': eg518 '1213': eg5214 '1214': eg5227 '1215': eg5231 '1216': eg5253 '1217': eg5256 '1218': eg5258 '1219': eg5259 '1220': eg5260 '1221': eg5270 '1222': eg5273 '1223': eg5278 '1224': eg5288 '1225': eg5329 '1226': eg535 '1227': eg5382 '1228': eg577 '1229': eg578 '1230': eg5809 '1231': eg608 '1232': eg609 '1233': eg615 '1234': eg6360 '1235': eg6417 '1236': eg6419 '1237': eg6435 '1238': eg6449 '1239': eg6739 '1240': eg6767 '1241': eg6802 '1242': eg6814 '1243': eg6822 '1244': eg6883 '1245': eg6897 '1246': eg6955 '1247': eg6974 '1248': eg718 '1249': eg724 '1250': eg726 '1251': eg727 '1252': eg732 '1253': eg733 '1254': eg779 '1255': eg898 '1256': eg901 '1257': eg909 '1258': eg913 '1259': eg914 - name: file dtype: string - name: image dtype: image - name: root dtype: string - name: description dtype: string - name: object_name dtype: string - name: other_name dtype: string - name: material dtype: string - name: production.period dtype: string - name: production.place dtype: string splits: - name: train num_bytes: 923492132.4197494 num_examples: 9456 - name: validation num_bytes: 305527605.0268552 num_examples: 2225 - name: test num_bytes: 250516356.24239534 num_examples: 2226 download_size: 1632705825 dataset_size: 1479536093.689 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
app_reviews	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - sentiment-scoring pretty_name: AppReviews dataset_info: features: - name: package_name dtype: string - name: review dtype: string - name: date dtype: string - name: star dtype: int8 splits: - name: train num_bytes: 32768731 num_examples: 288065 download_size: 13207727 dataset_size: 32768731 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Home Page](https://github.com/sealuzh/user_quality) - Repository: [Repo Link](https://github.com/sealuzh/user_quality) - Paper: [Link](https://giograno.me/assets/pdf/workshop/wama17.pdf) - Leaderboard: - Point of Contact:** [Darshan Gandhi](darshangandhi1151@gmail.com) ### Dataset Summary It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews (extracted with specific text mining approaches) ### Supported Tasks and Leaderboards The dataset we provide comprises 395 different apps from F-Droid repository, including code quality indicators of 629 versions of these apps. It also encloses app reviews related to each of these versions, which have been automatically categorized classifying types of user feedback from a software maintenance and evolution perspective. ### Languages The dataset is a monolingual dataset which has the messages English. ## Dataset Structure ### Data Instances The dataset consists of a message in English. {'package_name': 'com.mantz_it.rfanalyzer', 'review': "Great app! The new version now works on my Bravia Android TV which is great as it's right by my rooftop aerial cable. The scan feature would be useful...any ETA on when this will be available? Also the option to import a list of bookmarks e.g. from a simple properties file would be useful.", 'date': 'October 12 2016', 'star': 4} ### Data Fields * package_name : Name of the Software Application Package * review : Message of the user * date : date when the user posted the review * star : rating provied by the user for the application ### Data Splits There is training data, with a total of : 288065 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset With the help of this dataset one can try to understand more about software applications and what are the views and opinions of the users about them. This helps to understand more about which type of software applications are prefeered by the users and how do these applications facilitate the user to help them solve their problems and issues. ### Discussion of Biases The reviews are only for applications which are in the open-source software applications, the other sectors have not been considered here ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Giovanni Grano - (University of Zurich), Sebastiano Panichella - (University of Zurich), Andrea di Sorbo - (University of Sannio) ### Licensing Information [More Information Needed] ### Citation Information @InProceedings{Zurich Open Repository and Archive:dataset, title = {Software Applications User Reviews}, authors={Grano, Giovanni; Di Sorbo, Andrea; Mercaldo, Francesco; Visaggio, Corrado A; Canfora, Gerardo; Panichella, Sebastiano}, year={2017} } ### Contributions Thanks to [@darshan-gandhi](https://github.com/darshan-gandhi) for adding this dataset.
jlbaker361/anime_faces_dim_128_50k	--- dataset_info: features: - name: image dtype: image - name: split dtype: string - name: src dtype: string - name: style dtype: string splits: - name: train num_bytes: 1348590698.0 num_examples: 50000 download_size: 1346915159 dataset_size: 1348590698.0 --- # Dataset Card for "anime_faces_dim_128_50k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
JeffersonMusic/MichaelJackson	--- license: apache-2.0 ---
open-llm-leaderboard/details_ibivibiv__orthorus-125b-moe-v2	--- pretty_name: Evaluation run of ibivibiv/orthorus-125b-moe-v2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ibivibiv/orthorus-125b-moe-v2](https://huggingface.co/ibivibiv/orthorus-125b-moe-v2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ibivibiv__orthorus-125b-moe-v2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-06T15:03:25.094734](https://huggingface.co/datasets/open-llm-leaderboard/details_ibivibiv__orthorus-125b-moe-v2/blob/main/results_2024-02-06T15-03-25.094734.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.22895924430194997,\n\ \ \"acc_stderr\": 0.029775044140996448,\n \"acc_norm\": 0.22892897024882422,\n\ \ \"acc_norm_stderr\": 0.03055773036734348,\n \"mc1\": 0.23745410036719705,\n\ \ \"mc1_stderr\": 0.014896277441041845,\n \"mc2\": 0.48491700399143933,\n\ \ \"mc2_stderr\": 0.016301526988248338\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.22184300341296928,\n \"acc_stderr\": 0.012141659068147884,\n\ \ \"acc_norm\": 0.2627986348122867,\n \"acc_norm_stderr\": 0.012862523175351333\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.2591117307309301,\n\ \ \"acc_stderr\": 0.004372516060164754,\n \"acc_norm\": 0.2517426807408883,\n\ \ \"acc_norm_stderr\": 0.004331271717773866\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.2740740740740741,\n\ \ \"acc_stderr\": 0.038532548365520024,\n \"acc_norm\": 0.2740740740740741,\n\ \ \"acc_norm_stderr\": 0.038532548365520024\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\ \ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.2528301886792453,\n \"acc_stderr\": 0.026749899771241238,\n\ \ \"acc_norm\": 0.2528301886792453,\n \"acc_norm_stderr\": 0.026749899771241238\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2569444444444444,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536955,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536955\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\"\ : 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.20809248554913296,\n\ \ \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n\ \ \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n\ \ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n\ \ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.21164021164021163,\n \"acc_stderr\": 0.021037331505262883,\n \"\ acc_norm\": 0.21164021164021163,\n \"acc_norm_stderr\": 0.021037331505262883\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.15873015873015872,\n\ \ \"acc_stderr\": 0.03268454013011744,\n \"acc_norm\": 0.15873015873015872,\n\ \ \"acc_norm_stderr\": 0.03268454013011744\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"\ acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n \"\ acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\ acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.20207253886010362,\n \"acc_stderr\": 0.02897908979429673,\n\ \ \"acc_norm\": 0.20207253886010362,\n \"acc_norm_stderr\": 0.02897908979429673\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.24615384615384617,\n \"acc_stderr\": 0.02184086699042309,\n\ \ \"acc_norm\": 0.24615384615384617,\n \"acc_norm_stderr\": 0.02184086699042309\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \ \ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\ \ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"\ acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"\ acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"\ acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\ \ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.2645739910313901,\n\ \ \"acc_stderr\": 0.02960510321703834,\n \"acc_norm\": 0.2645739910313901,\n\ \ \"acc_norm_stderr\": 0.02960510321703834\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.183206106870229,\n \"acc_stderr\": 0.03392770926494733,\n\ \ \"acc_norm\": 0.183206106870229,\n \"acc_norm_stderr\": 0.03392770926494733\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\ acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.2962962962962963,\n\ \ \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.2962962962962963,\n\ \ \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.3125,\n\ \ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\ \ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.21359223300970873,\n \"acc_stderr\": 0.04058042015646034,\n\ \ \"acc_norm\": 0.21359223300970873,\n \"acc_norm_stderr\": 0.04058042015646034\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.2777777777777778,\n\ \ \"acc_stderr\": 0.02934311479809447,\n \"acc_norm\": 0.2777777777777778,\n\ \ \"acc_norm_stderr\": 0.02934311479809447\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.2388250319284802,\n\ \ \"acc_stderr\": 0.015246803197398675,\n \"acc_norm\": 0.2388250319284802,\n\ \ \"acc_norm_stderr\": 0.015246803197398675\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.1832797427652733,\n\ \ \"acc_stderr\": 0.021974198848265805,\n \"acc_norm\": 0.1832797427652733,\n\ \ \"acc_norm_stderr\": 0.021974198848265805\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\ \ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \ \ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\ \ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.25163398692810457,\n \"acc_stderr\": 0.01755581809132226,\n \ \ \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.01755581809132226\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.20909090909090908,\n\ \ \"acc_stderr\": 0.03895091015724136,\n \"acc_norm\": 0.20909090909090908,\n\ \ \"acc_norm_stderr\": 0.03895091015724136\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.18775510204081633,\n \"acc_stderr\": 0.02500025603954621,\n\ \ \"acc_norm\": 0.18775510204081633,\n \"acc_norm_stderr\": 0.02500025603954621\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.24378109452736318,\n\ \ \"acc_stderr\": 0.03036049015401465,\n \"acc_norm\": 0.24378109452736318,\n\ \ \"acc_norm_stderr\": 0.03036049015401465\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.2891566265060241,\n\ \ \"acc_stderr\": 0.03529486801511115,\n \"acc_norm\": 0.2891566265060241,\n\ \ \"acc_norm_stderr\": 0.03529486801511115\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.03188578017686399,\n\ \ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.03188578017686399\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.23745410036719705,\n\ \ \"mc1_stderr\": 0.014896277441041845,\n \"mc2\": 0.48491700399143933,\n\ \ \"mc2_stderr\": 0.016301526988248338\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.49329123914759276,\n \"acc_stderr\": 0.014051220692330349\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/ibivibiv/orthorus-125b-moe-v2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|arc:challenge\|25_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-06T15-03-25.094734.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|gsm8k\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hellaswag\|10_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-06T15-03-25.094734.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-06T15-03-25.094734.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '/details_harness\|winogrande\|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-06T15-03-25.094734.parquet' - config_name: results data_files: - split: 2024_02_06T15_03_25.094734 path: - results_2024-02-06T15-03-25.094734.parquet - split: latest path: - results_2024-02-06T15-03-25.094734.parquet --- # Dataset Card for Evaluation run of ibivibiv/orthorus-125b-moe-v2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [ibivibiv/orthorus-125b-moe-v2](https://huggingface.co/ibivibiv/orthorus-125b-moe-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ibivibiv__orthorus-125b-moe-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-06T15:03:25.094734](https://huggingface.co/datasets/open-llm-leaderboard/details_ibivibiv__orthorus-125b-moe-v2/blob/main/results_2024-02-06T15-03-25.094734.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.22895924430194997, "acc_stderr": 0.029775044140996448, "acc_norm": 0.22892897024882422, "acc_norm_stderr": 0.03055773036734348, "mc1": 0.23745410036719705, "mc1_stderr": 0.014896277441041845, "mc2": 0.48491700399143933, "mc2_stderr": 0.016301526988248338 }, "harness\|arc:challenge\|25": { "acc": 0.22184300341296928, "acc_stderr": 0.012141659068147884, "acc_norm": 0.2627986348122867, "acc_norm_stderr": 0.012862523175351333 }, "harness\|hellaswag\|10": { "acc": 0.2591117307309301, "acc_stderr": 0.004372516060164754, "acc_norm": 0.2517426807408883, "acc_norm_stderr": 0.004331271717773866 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.22, "acc_stderr": 0.04163331998932268, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932268 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.2740740740740741, "acc_stderr": 0.038532548365520024, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.038532548365520024 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123398, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123398 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.2528301886792453, "acc_stderr": 0.026749899771241238, "acc_norm": 0.2528301886792453, "acc_norm_stderr": 0.026749899771241238 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.18, "acc_stderr": 0.038612291966536955, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536955 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.20809248554913296, "acc_stderr": 0.030952890217749874, "acc_norm": 0.20809248554913296, "acc_norm_stderr": 0.030952890217749874 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.26382978723404255, "acc_stderr": 0.028809989854102973, "acc_norm": 0.26382978723404255, "acc_norm_stderr": 0.028809989854102973 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813365, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813365 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.21164021164021163, "acc_stderr": 0.021037331505262883, "acc_norm": 0.21164021164021163, "acc_norm_stderr": 0.021037331505262883 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.15873015873015872, "acc_stderr": 0.03268454013011744, "acc_norm": 0.15873015873015872, "acc_norm_stderr": 0.03268454013011744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.1774193548387097, "acc_stderr": 0.02173254068932927, "acc_norm": 0.1774193548387097, "acc_norm_stderr": 0.02173254068932927 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.15270935960591134, "acc_stderr": 0.02530890453938063, "acc_norm": 0.15270935960591134, "acc_norm_stderr": 0.02530890453938063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.17676767676767677, "acc_stderr": 0.027178752639044915, "acc_norm": 0.17676767676767677, "acc_norm_stderr": 0.027178752639044915 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.20207253886010362, "acc_stderr": 0.02897908979429673, "acc_norm": 0.20207253886010362, "acc_norm_stderr": 0.02897908979429673 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.24615384615384617, "acc_stderr": 0.02184086699042309, "acc_norm": 0.24615384615384617, "acc_norm_stderr": 0.02184086699042309 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2111111111111111, "acc_stderr": 0.024882116857655075, "acc_norm": 0.2111111111111111, "acc_norm_stderr": 0.024882116857655075 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.21008403361344538, "acc_stderr": 0.026461398717471874, "acc_norm": 0.21008403361344538, "acc_norm_stderr": 0.026461398717471874 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.1986754966887417, "acc_stderr": 0.03257847384436776, "acc_norm": 0.1986754966887417, "acc_norm_stderr": 0.03257847384436776 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.1926605504587156, "acc_stderr": 0.016909276884936094, "acc_norm": 0.1926605504587156, "acc_norm_stderr": 0.016909276884936094 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.1527777777777778, "acc_stderr": 0.024536326026134224, "acc_norm": 0.1527777777777778, "acc_norm_stderr": 0.024536326026134224 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.270042194092827, "acc_stderr": 0.028900721906293426, "acc_norm": 0.270042194092827, "acc_norm_stderr": 0.028900721906293426 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.2645739910313901, "acc_stderr": 0.02960510321703834, "acc_norm": 0.2645739910313901, "acc_norm_stderr": 0.02960510321703834 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.183206106870229, "acc_stderr": 0.03392770926494733, "acc_norm": 0.183206106870229, "acc_norm_stderr": 0.03392770926494733 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2396694214876033, "acc_stderr": 0.03896878985070417, "acc_norm": 0.2396694214876033, "acc_norm_stderr": 0.03896878985070417 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.2962962962962963, "acc_stderr": 0.044143436668549335, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.044143436668549335 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.22085889570552147, "acc_stderr": 0.032591773927421776, "acc_norm": 0.22085889570552147, "acc_norm_stderr": 0.032591773927421776 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.3125, "acc_stderr": 0.043994650575715215, "acc_norm": 0.3125, "acc_norm_stderr": 0.043994650575715215 }, "harness\|hendrycksTest-management\|5": { "acc": 0.21359223300970873, "acc_stderr": 0.04058042015646034, "acc_norm": 0.21359223300970873, "acc_norm_stderr": 0.04058042015646034 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2777777777777778, "acc_stderr": 0.02934311479809447, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.02934311479809447 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.2388250319284802, "acc_stderr": 0.015246803197398675, "acc_norm": 0.2388250319284802, "acc_norm_stderr": 0.015246803197398675 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.24855491329479767, "acc_stderr": 0.023267528432100174, "acc_norm": 0.24855491329479767, "acc_norm_stderr": 0.023267528432100174 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.22549019607843138, "acc_stderr": 0.023929155517351284, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.023929155517351284 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.1832797427652733, "acc_stderr": 0.021974198848265805, "acc_norm": 0.1832797427652733, "acc_norm_stderr": 0.021974198848265805 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.21604938271604937, "acc_stderr": 0.022899162918445806, "acc_norm": 0.21604938271604937, "acc_norm_stderr": 0.022899162918445806 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.23404255319148937, "acc_stderr": 0.025257861359432417, "acc_norm": 0.23404255319148937, "acc_norm_stderr": 0.025257861359432417 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.18382352941176472, "acc_stderr": 0.023529242185193106, "acc_norm": 0.18382352941176472, "acc_norm_stderr": 0.023529242185193106 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.25163398692810457, "acc_stderr": 0.01755581809132226, "acc_norm": 0.25163398692810457, "acc_norm_stderr": 0.01755581809132226 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.20909090909090908, "acc_stderr": 0.03895091015724136, "acc_norm": 0.20909090909090908, "acc_norm_stderr": 0.03895091015724136 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.18775510204081633, "acc_stderr": 0.02500025603954621, "acc_norm": 0.18775510204081633, "acc_norm_stderr": 0.02500025603954621 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.24378109452736318, "acc_stderr": 0.03036049015401465, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.2891566265060241, "acc_stderr": 0.03529486801511115, "acc_norm": 0.2891566265060241, "acc_norm_stderr": 0.03529486801511115 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03188578017686399, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03188578017686399 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.23745410036719705, "mc1_stderr": 0.014896277441041845, "mc2": 0.48491700399143933, "mc2_stderr": 0.016301526988248338 }, "harness\|winogrande\|5": { "acc": 0.49329123914759276, "acc_stderr": 0.014051220692330349 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
HebArabNlpProject/ArabicSentimentDataSet	--- license: cc-by-4.0 ---
projetosoclts/Vicente	--- license: openrail ---
freshpearYoon/vr_train_free_63	--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: filename dtype: string - name: NumOfUtterance dtype: int64 - name: text dtype: string - name: samplingrate dtype: int64 - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: speaker_id dtype: string - name: directory dtype: string splits: - name: train num_bytes: 7111025704 num_examples: 10000 download_size: 1184721349 dataset_size: 7111025704 configs: - config_name: default data_files: - split: train path: data/train-* ---
gayanin/pubmed-abstracts-noised-with-prob-dist-common	--- dataset_info: config_name: prob-01 features: - name: refs dtype: string - name: trans dtype: string splits: - name: train num_bytes: 18087365 num_examples: 74724 - name: test num_bytes: 2317206 num_examples: 9341 - name: validation num_bytes: 2379842 num_examples: 9341 download_size: 12754200 dataset_size: 22784413 configs: - config_name: prob-01 data_files: - split: train path: prob-01/train-* - split: test path: prob-01/test-* - split: validation path: prob-01/validation-* ---
PDScience/CollegeAuditData	--- license: apache-2.0 task_categories: - question-answering - feature-extraction - text-generation language: - en tags: - finance pretty_name: College Audit Data size_categories: - 100K<n<1M --- ### Dataset Description: College and University Financial Audits and Text Dataset #### Overview The College and University Financial Audits and Text Dataset is a dataset that combines audited financial statement texts with key extracted financial data from various colleges and universities. This dataset stands out for its emphasis on long context input data, making it especially useful for training large language models to extract data from long input texts. #### Dataset Composition This dataset contains over 125,000 entries with the following variables: 1. AuditText: The complete text of the audited financial statement. These can be very long input texts. 2. fiscalYear: The fiscal year of the reported data. 3. auditYear: The year in which the audit was performed. 4. value: Value of the variable in fiscal year==fiscalYear as reported in the audit text. 5. variable: Variable extracted from the audit text. #### Key Features and Use Cases - Long Context Input Data: The inclusion of complete audit texts offers a unique opportunity for training and testing LLMs on long-form financial documents. - Model Training and fine-tuning: Ideal for fine-tuning AI models for data extraction from long textual inputs, such as in-depth financial reports. - Financial and Educational Research: Facilitates comprehensive analysis in the domains of finance and education, allowing for nuanced insights into the financial workings of higher education institutions. #### Suggested prompt format for training Prompt: "Extract the {variable} for fiscal year {fiscalYear} from the following text: \n\n {AuditText}" Response: "{value}"
tner/tweebank_ner	--- language: - en license: - other multilinguality: - monolingual size_categories: - 1k<10K task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: TweeBank NER --- # Dataset Card for "tner/tweebank_ner" ## Dataset Description - Repository: [T-NER](https://github.com/asahi417/tner) - Paper: [https://arxiv.org/abs/2201.07281](https://arxiv.org/abs/2201.07281) - Dataset: TweeBank NER - Domain: Twitter - Number of Entity: 4 ### Dataset Summary TweeBank NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project. - Entity Types: `LOC`, `MISC`, `PER`, `ORG` ## Dataset Structure ### Data Instances An example of `train` looks as follows. ``` { 'tokens': ['RT', '@USER2362', ':', 'Farmall', 'Heart', 'Of', 'The', 'Holidays', 'Tabletop', 'Christmas', 'Tree', 'With', 'Lights', 'And', 'Motion', 'URL1087', '#Holiday', '#Gifts'], 'tags': [8, 8, 8, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8] } ``` ### Label ID The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweebank_ner/raw/main/dataset/label.json). ```python { "B-LOC": 0, "B-MISC": 1, "B-ORG": 2, "B-PER": 3, "I-LOC": 4, "I-MISC": 5, "I-ORG": 6, "I-PER": 7, "O": 8 } ``` ### Data Splits \| name \|train\|validation\|test\| \|---------\|----:\|---------:\|---:\| \|tweebank_ner \| 1639\| 710 \|1201\| ### Citation Information ``` @article{DBLP:journals/corr/abs-2201-07281, author = {Hang Jiang and Yining Hua and Doug Beeferman and Deb Roy}, title = {Annotating the Tweebank Corpus on Named Entity Recognition and Building {NLP} Models for Social Media Analysis}, journal = {CoRR}, volume = {abs/2201.07281}, year = {2022}, url = {https://arxiv.org/abs/2201.07281}, eprinttype = {arXiv}, eprint = {2201.07281}, timestamp = {Fri, 21 Jan 2022 13:57:15 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2201-07281.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
CorpuSlave/common-knowledge	--- license: cc-by-nc-sa-4.0 configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: doc_id dtype: string splits: - name: train num_bytes: 10650689076 num_examples: 6216545 download_size: 5826173649 dataset_size: 10650689076 ---
prachathai67k	--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - topic-classification paperswithcode_id: prachathai-67k pretty_name: prachathai67k dataset_info: features: - name: url dtype: string - name: date dtype: string - name: title dtype: string - name: body_text dtype: string - name: politics dtype: class_label: names: '0': neg '1': pos - name: human_rights dtype: class_label: names: '0': neg '1': pos - name: quality_of_life dtype: class_label: names: '0': neg '1': pos - name: international dtype: class_label: names: '0': neg '1': pos - name: social dtype: class_label: names: '0': neg '1': pos - name: environment dtype: class_label: names: '0': neg '1': pos - name: economics dtype: class_label: names: '0': neg '1': pos - name: culture dtype: class_label: names: '0': neg '1': pos - name: labor dtype: class_label: names: '0': neg '1': pos - name: national_security dtype: class_label: names: '0': neg '1': pos - name: ict dtype: class_label: names: '0': neg '1': pos - name: education dtype: class_label: names: '0': neg '1': pos config_name: prachathai67k splits: - name: train num_bytes: 865848436 num_examples: 54379 - name: validation num_bytes: 108641386 num_examples: 6721 - name: test num_bytes: 110034036 num_examples: 6789 download_size: 254240975 dataset_size: 1084523858 --- # Dataset Card for `prachathai67k` ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/PyThaiNLP/prachathai-67k - Repository: https://github.com/PyThaiNLP/prachathai-67k - Paper: - Leaderboard: - Point of Contact: https://github.com/PyThaiNLP/ ### Dataset Summary `prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com The `prachathai-67k` dataset was scraped from the news site [Prachathai](prachathai.com). We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018. The dataset was originally scraped by [@lukkiddd](https://github.com/lukkiddd) and cleaned by [@cstorm125](https://github.com/cstorm125). Download the dataset [here](https://www.dropbox.com/s/fsxepdka4l2pr45/prachathai-67k.zip?dl=1). You can also see preliminary exploration in [exploration.ipynb](https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb). This dataset is a part of [pyThaiNLP](https://github.com/PyThaiNLP/) Thai text [classification-benchmarks](https://github.com/PyThaiNLP/classification-benchmarks). For the benchmark, we selected the following tags with substantial volume that resemble classifying types of articles: * `การเมือง` - politics * `สิทธิมนุษยชน` - human_rights * `คุณภาพชีวิต` - quality_of_life * `ต่างประเทศ` - international * `สังคม` - social * `สิ่งแวดล้อม` - environment * `เศรษฐกิจ` - economics * `วัฒนธรรม` - culture * `แรงงาน` - labor * `ความมั่นคง` - national_security * `ไอซีที` - ict * `การศึกษา` - education ### Supported Tasks and Leaderboards multi-label text classification, language modeling ### Languages Thai ## Dataset Structure ### Data Instances {'body_text': '17 พ.ย. 2558 Blognone [1] รายงานว่า กลุ่มแฮคเกอร์ Anonymous ประกาศสงครามไซเบอร์กับกลุ่มหัวรุนแรงหลังจากกลุ่ม IS ออกมาประกาศว่าเป็นผู้อยู่เบื้องหลังการโจมตีกรุงปารีสในคืนวันศุกร์ที่ผ่านมา\n\n\nภาพในคลิปใน YouTube โฆษกของกลุ่มแฮคเกอร์สวมหน้ากากที่เป็นสัญลักษณ์ของกลุ่มได้ออกมาอ่านแถลงเป็นภาษาฝรั่งเศส มีใจความว่า จากการโจมตีของกลุ่ม IS ในกรุงปารีส กลุ่ม Anonymous ทั่วโลกจะตามล่ากลุ่ม IS เหมือนที่เคยทำตอนที่มีการโจมตีสำนักพิมพ์ Charlie Hebdo และครั้งนี้จะเป็นปฏิบัติการโจมตีครั้งใหญ่ที่สุดของกลุ่ม Anonymous เลย นอกจากนี้กลุ่ม Anonymous ยังแสดงความเสียใจต่อครอบครัวผู้สูญเสียในเหตุการณ์ครั้งนี้\nกลุ่ม Anonymous เคยประกาศสงครามกับกลุ่ม IS หลังจากการโจมตีสำนักพิมพ์ Charlie Hebdo ที่ฝรั่งเศสเมื่อต้นปีที่ผ่านมา ซึ่งครั้งนั้นกลุ่ม Anonymous อ้างว่าได้ระงับบัญชีผู้ใช้งานที่เกี่ยวข้องกับ IS ไปหลายพันบัญชี (อ่านรายละเอียดเพิ่มเติม จากBlognone ที่\xa0\xa0กลุ่มแฮคเกอร์ Anonymous ประกาศสงครามไซเบอร์ขอกวาดล้างพวก ISIS [2])', 'culture': 0, 'date': '2015-11-17 18:14', 'economics': 0, 'education': 0, 'environment': 0, 'human_rights': 0, 'ict': 1, 'international': 1, 'labor': 0, 'national_security': 0, 'politics': 0, 'quality_of_life': 0, 'social': 0, 'title': 'แฮคเกอร์ Anonymous ลั่นทำสงครามไซเบอร์ครั้งใหญ่สุดกับกลุ่ม IS', 'url': 'https://prachatai.com/print/62490'} {'body_text': 'แถลงการณ์\n\n\xa0\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์\n\n\xa0\n\nมหาวิทยาลัยธรรมศาสตร์ก่อตั้งขึ้นภายใต้แนวคิดการให้การศึกษากับประชาชนเพื่อสนับสนุนการปกครองระบอบประชาธิปไตย อีกทั้งยังเป็นสถาบันหนึ่งที่อยู่เคียงข้างประชาชนมาโดยตลอด\n\n\xa0\n\nสถานการณ์สังคมไทยปัจจุบันได้เกิดความขัดแย้งทางการเมือง ทางแนวคิด จนลุกลามเป็นวิกฤตการณ์อันหาทางออกได้ยากยิ่ง องค์กรนักศึกษามหาวิทยาลัยธรรมศาสตร์ขอร้องเรียนและเสนอแนะต่อทุกฝ่าย โดยยึดหลักแนวทางตามรัฐธรรมนูญแห่งราชอาณาจักรไทย พ.ศ. ๒๕๕๐ อันเป็นกฎหมายสูงสุดในการจัดการปกครองรัฐ ที่มีผลบังคับใช้อยู่ในปัจจุบันซึ่งผ่านการประชามติจากปวงชนชาวไทยเมื่อวันที่ ๑๙ สิงหาคม พ.ศ. ๒๕๕๐ แล้วดังต่อนี้\n\n\xa0\n\n๑.การชุมชมโดยสงบและปราศจากอาวุธย่อมได้รับการคุ้มครองตามรัฐธรรมนูญ แต่หากการชุมนุมและเคลื่อนไหวของกลุ่มใดๆ มีการละเมิดสิทธิและเสรีภาพของผู้อื่นหรือก่อให้เกิดความเสียหายต่อชีวิตและทรัพย์สินของบุคคลและส่วนรวมนั้น ไม่สามารถกระทำได้ การใช้ความรุนแรง การกระทำอุกอาจต่างๆ ทั้งต่อบุคคลและทรัพย์สิน การยั่วยุ ปลุกระดมเพื่อหวังผลในการปะทะต่อสู้ จึงควรได้รับการกล่าวโทษ\n\n\xa0\n\nดังนั้นทั้งกลุ่มพันธมิตรประชาชนเพื่อประชาธิปไตย (พธม.) และกลุ่มแนวร่วมประชาธิปไตยไม่เอาเผด็จการแห่งชาติ (นปช.) จึงควรยอมรับกระบวนการตามกฎหมาย และหากถูกกล่าวหาไม่ว่ากรณีใดๆ ก็ควรพิสูจน์ความบริสุทธิ์โดยใช้กระบวนการยุติธรรม และหากจะยังชุมนุมต่อไปก็ยังคงทำได้ภายใต้บทบัญญัติแห่งกฎหมาย\n\n\xa0\n\nองค์กรนักศึกษามหาวิทยาลัยธรรมศาสตร์ จึงร้องขอให้หน่วยงานต่างๆ ที่เกี่ยวข้องดำเนินการตามกระบวนการทางกฎหมายกับการกระทำที่ผิดบทบัญญัติแห่งกฎหมายที่ทุกฝ่ายได้กระทำไป\n\n\xa0\n\n๒.นายสมัคร สุนทรเวช นายกรัฐมนตรี ไม่มีความเหมาะสมในการบริหารราชการแผ่นดินขาดหลักธรรมาภิบาล แต่ทั้งนี้นายสมัคร สุนทรเวช ยังคงยืนยันและกล่าวอ้างความชอบธรรมตามระบอบประชาธิปไตยภายใต้รัฐธรรมนูญ โดยไม่คำนึงถึงกระแสเรียกร้องใดๆ อันส่งผลให้ความขัดแย้งทางสังคมยิ่งบานปลายจนกลายเป็นวิกฤตการณ์เช่นปัจจุบัน ซึ่งก่อให้เกิดความเสียหายต่อประเทศแนวโน้มจะคลี่คลาย\n\n\xa0\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์ จึงเห็นว่า ควรใช้สิทธิตามรัฐธรรมนูญแห่งราชอาณาจักรไทย พุทธศักราช ๒๕๕๐ มาตรา ๑๖๔ โดยการเข้าชื่อเพื่อร้องต่อประธานวุฒิสภาเพื่อให้มีมติตามมาตรา ๒๗๔ ให้ถอดถอนนายสมัคร สุนทรเวช ออกจากตำแหน่งนายกรัฐมนตรีตามมาตรา ๒๗๐ ณ ลานโพ มหาวิทยาลัยธรรมศาสตร์ ท่าพระจันทร์ อาคารเรียนรวมสังคมศาสตร์ อาคารปิยชาติ และตึกกิจกรรมนักศึกษา มหาวิทยาลัยธรรมศาสตร์ ศูนย์รังสิต\n\n\xa0\n\n\xa0\n\nด้วยความสมานฉันท์\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์', 'culture': 0, 'date': '2008-09-06 03:36', 'economics': 0, 'education': 0, 'environment': 0, 'human_rights': 0, 'ict': 0, 'international': 0, 'labor': 0, 'national_security': 0, 'politics': 1, 'quality_of_life': 0, 'social': 0, 'title': 'แถลงการณ์ อมธ.แนะใช้สิทธิ ตาม รธน.เข้าชื่อร้องต่อประธานวุฒิสภาถอดถอน "สมัคร" จากตำแหน่งนายกฯ', 'url': 'https://prachatai.com/print/18038'} ### Data Fields - `url`: url of the article - `date`: date the article was published - `title`: title of the article - `body_text`: body text of the article - `politics`: 1 if sample has this tag else 0 - `human_rights`: 1 if sample has this tag else 0 - `quality_of_life`: 1 if sample has this tag else 0 - `international`: 1 if sample has this tag else 0 - `social`: 1 if sample has this tag else 0 - `environment`: 1 if sample has this tag else 0 - `economics`: 1 if sample has this tag else 0 - `culture`: 1 if sample has this tag else 0 - `labor`: 1 if sample has this tag else 0 - `national_security`: 1 if sample has this tag else 0 - `ict`: 1 if sample has this tag else 0 - `education`: 1 if sample has this tag else 0 ### Data Splits \| \| train \| valid \| test \| \|-------------------\|-------\|--------\|------\| \| # articles \| 54379 \| 6721 \| 6789 \| \| politics \| 31401 \| 3852 \| 3842 \| \| human_rights \| 12061 \| 1458 \| 1511 \| \| quality_of_life \| 9037 \| 1144 \| 1127 \| \| international \| 6432 \| 828 \| 834 \| \| social \| 6321 \| 782 \| 789 \| \| environment \| 6157 \| 764 \| 772 \| \| economics \| 3994 \| 487 \| 519 \| \| culture \| 3279 \| 388 \| 398 \| \| labor \| 2905 \| 375 \| 350 \| \| national_security \| 2865 \| 339 \| 338 \| \| ict \| 2326 \| 285 \| 292 \| \| education \| 2093 \| 248 \| 255 \| ## Dataset Creation ### Curation Rationale The data was scraped from the news site [Prachathai](prachathai.com) from August 24, 2004 to November 15, 2018. The initial intention was to use the dataset as a benchmark for Thai text classification. Due to the size of the dataset, it can also be used for language modeling. ### Source Data #### Initial Data Collection and Normalization 67,889 articles wtih 51,797 tags were scraped from the news site [Prachathai](prachathai.com) from August 24, 2004 to November 15, 2018. We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. #### Who are the source language producers? Prachathai.com ### Annotations #### Annotation process Tags are annotated for the news website Prachathai.com #### Who are the annotators? We assume that the reporters who wrote the articles or other Prachathai staff gave each article its tags. ### Personal and Sensitive Information We do not expect any personal and sensitive information to be present since all data are public news articles. ## Considerations for Using the Data ### Social Impact of Dataset - classification benchmark for multi-label Thai text classification ### Discussion of Biases Prachathai.com is a left-leaning, human-right-focused news site, and thus unusual news labels such as human rights and quality of life. The news articles are expected to be left-leaning in contents. ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators PyThaiNLP ### Licensing Information CC-BY-NC ### Citation Information @misc{prachathai67k, author = {cstorm125, lukkiddd }, title = {prachathai67k}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, howpublished={\\url{https://github.com/PyThaiNLP/prachathai-67k}}, } ### Contributions Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.
Rycharllyson/CallYou	--- license: openrail ---
mfa13/test1	--- license: mit ---
liuyanchen1015/MULTI_VALUE_qqp_who_at	--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 121227 num_examples: 597 - name: test num_bytes: 1338416 num_examples: 6621 - name: train num_bytes: 1231970 num_examples: 5952 download_size: 1647252 dataset_size: 2691613 --- # Dataset Card for "MULTI_VALUE_qqp_who_at" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jtatman/cosmopedia-100k-sharegpt	--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 542800124 num_examples: 100000 download_size: 308483304 dataset_size: 542800124 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "cosmopedia-100k-sharegpt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Moneyshots/Asdf	--- license: unknown ---
CyberHarem/brooklyn_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of brooklyn/ブルックリン/布鲁克林 (Azur Lane) This is the dataset of brooklyn/ブルックリン/布鲁克林 (Azur Lane), containing 43 images and their tags. The core tags of this character are `breasts, long_hair, dark-skinned_female, dark_skin, blonde_hair, large_breasts, red_eyes, ahoge, bow, very_long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:----------\|:-------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 43 \| 42.17 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/brooklyn_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 43 \| 27.89 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/brooklyn_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 100 \| 58.41 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/brooklyn_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 43 \| 38.23 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/brooklyn_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 100 \| 73.82 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/brooklyn_azurlane/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/brooklyn_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 29 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, looking_at_viewer, solo, elbow_gloves, black_gloves, thighhighs, blush, bare_shoulders, cleavage, garter_straps, simple_background, hair_bow, smile, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| solo \| elbow_gloves \| black_gloves \| thighhighs \| blush \| bare_shoulders \| cleavage \| garter_straps \| simple_background \| hair_bow \| smile \| white_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------\|:---------------\|:---------------\|:-------------\|:--------\|:-----------------\|:-----------\|:----------------\|:--------------------\|:-----------\|:--------\|:-------------------\| \| 0 \| 29 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
Falah/fish_eye_overlooking_industrial_site_prompts	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 1803324 num_examples: 10000 download_size: 90166 dataset_size: 1803324 --- # Dataset Card for "fish_eye_overlooking_industrial_site_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
juancopi81/orca-math-word-problems-180036_190038	--- language: - en dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 15860347 num_examples: 10002 download_size: 7112305 dataset_size: 15860347 configs: - config_name: default data_files: - split: train path: data/train-* ---
tulip4attoo/qa_pairs_3rd	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 5757757 num_examples: 15596 download_size: 3660410 dataset_size: 5757757 --- # Dataset Card for "qa_pairs_3rd" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
andersonbcdefg/supernatural-instructions-2m	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 1859403487.079275 num_examples: 1990915 download_size: 521457643 dataset_size: 1859403487.079275 --- # Dataset Card for "supernatural-instructions-2m" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Gabriel1322/tes	--- license: openrail ---
lmvasque/kwiziq	--- license: cc-by-4.0 --- ## About this dataset This dataset was collected from [kwiziq.com](https://www.kwiziq.com/), a website dedicated to aid Spanish learning through automated methods. It also provides articles in different CEFR-based levels. We have scraped the freely available articles from its original [website](https://www.kwiziq.com/) to make it available to the community. If you use this data, please credit the original [website]((https://www.kwiziq.com/) and our work as well. ## Citation If you use our splits in your research, please cite our work: "[A Benchmark for Neural Readability Assessment of Texts in Spanish](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link)". ``` @inproceedings{vasquez-rodriguez-etal-2022-benchmarking, title = "A Benchmark for Neural Readability Assessment of Texts in Spanish", author = "V{\'a}squez-Rodr{\'\i}guez, Laura and Cuenca-Jim{\'\e}nez, Pedro-Manuel and Morales-Esquivel, Sergio Esteban and Alva-Manchego, Fernando", booktitle = "Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022", month = dec, year = "2022", } ``` You can also find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark).
open-llm-leaderboard/details_TFLai__SpeechlessV1-Nova-13B	--- pretty_name: Evaluation run of TFLai/SpeechlessV1-Nova-13B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TFLai/SpeechlessV1-Nova-13B](https://huggingface.co/TFLai/SpeechlessV1-Nova-13B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TFLai__SpeechlessV1-Nova-13B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-22T07:37:37.459766](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__SpeechlessV1-Nova-13B/blob/main/results_2023-10-22T07-37-37.459766.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.006396812080536913,\n\ \ \"em_stderr\": 0.0008164468837432435,\n \"f1\": 0.0904718959731546,\n\ \ \"f1_stderr\": 0.0018774631078676703,\n \"acc\": 0.41594466880448644,\n\ \ \"acc_stderr\": 0.00908410979036271\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.006396812080536913,\n \"em_stderr\": 0.0008164468837432435,\n\ \ \"f1\": 0.0904718959731546,\n \"f1_stderr\": 0.0018774631078676703\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0576194086429113,\n \ \ \"acc_stderr\": 0.006418593319822863\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7742699289660616,\n \"acc_stderr\": 0.011749626260902554\n\ \ }\n}\n```" repo_url: https://huggingface.co/TFLai/SpeechlessV1-Nova-13B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|arc:challenge\|25_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-05T14:12:12.910236.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_22T07_37_37.459766 path: - '/details_harness\|drop\|3_2023-10-22T07-37-37.459766.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-22T07-37-37.459766.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_22T07_37_37.459766 path: - '/details_harness\|gsm8k\|5_2023-10-22T07-37-37.459766.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-22T07-37-37.459766.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hellaswag\|10_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-05T14:12:12.910236.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_05T14_12_12.910236 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-05T14:12:12.910236.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_22T07_37_37.459766 path: - '/details_harness\|winogrande\|5_2023-10-22T07-37-37.459766.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-22T07-37-37.459766.parquet' - config_name: results data_files: - split: 2023_09_05T14_12_12.910236 path: - results_2023-09-05T14:12:12.910236.parquet - split: 2023_10_22T07_37_37.459766 path: - results_2023-10-22T07-37-37.459766.parquet - split: latest path: - results_2023-10-22T07-37-37.459766.parquet --- # Dataset Card for Evaluation run of TFLai/SpeechlessV1-Nova-13B ## Dataset Description - Homepage: - Repository: https://huggingface.co/TFLai/SpeechlessV1-Nova-13B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [TFLai/SpeechlessV1-Nova-13B](https://huggingface.co/TFLai/SpeechlessV1-Nova-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_TFLai__SpeechlessV1-Nova-13B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-22T07:37:37.459766](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__SpeechlessV1-Nova-13B/blob/main/results_2023-10-22T07-37-37.459766.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.006396812080536913, "em_stderr": 0.0008164468837432435, "f1": 0.0904718959731546, "f1_stderr": 0.0018774631078676703, "acc": 0.41594466880448644, "acc_stderr": 0.00908410979036271 }, "harness\|drop\|3": { "em": 0.006396812080536913, "em_stderr": 0.0008164468837432435, "f1": 0.0904718959731546, "f1_stderr": 0.0018774631078676703 }, "harness\|gsm8k\|5": { "acc": 0.0576194086429113, "acc_stderr": 0.006418593319822863 }, "harness\|winogrande\|5": { "acc": 0.7742699289660616, "acc_stderr": 0.011749626260902554 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

ovior/twitter_dataset_1713157533

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 2306647 num_examples: 7035 download_size: 1317830 dataset_size: 2306647 configs: - config_name: default data_files: - split: train path: data/train-* ---

FelipeBandeiraPoatek/invoices-donut-data-v2

--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 234466949.0 num_examples: 425 - name: test num_bytes: 15053216.0 num_examples: 26 - name: validation num_bytes: 26678659.0 num_examples: 50 download_size: 197788456 dataset_size: 276198824.0 --- # Dataset Card for "invoices-donut-data-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Mike0307/nli-zh-tw-crosslingual

--- dataset_info: features: - name: text1 dtype: string - name: text2 dtype: string - name: label dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 57716492 num_examples: 141844 - name: test num_bytes: 6397485 num_examples: 15958 - name: validate num_bytes: 7902555 num_examples: 19503 download_size: 56543046 dataset_size: 72016532 --- # Dataset Card for "nli-zh-tw-crosslingual" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nateraw/fuego-20230208-175631-15666f

--- tags: - fuego fuego: id: 20230208-175631-15666f status: done script: main.py requirements_file: requirements.txt space_id: nateraw/fuego-20230208-175631-15666f space_hardware: cpu-basic github_repo_id: pytorch/examples github_repo_branch: main github_repo_sha: d8456a36d1bbb22f72b003f59406a19a0a0547c3 ---

explodinggradients/hindi-wikipedia

--- license: apache-2.0 ---

CyberHarem/sue_fireemblem

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sue (Fire Emblem) This is the dataset of sue (Fire Emblem), containing 28 images and their tags. The core tags of this character are `headband, long_hair, green_hair, green_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 28 | 23.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sue_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 28 | 16.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sue_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 45 | 26.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sue_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 28 | 21.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sue_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 45 | 32.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sue_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/sue_fireemblem', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------| | 0 | 28 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, fingerless_gloves, bow_(weapon), holding_weapon | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | fingerless_gloves | bow_(weapon) | holding_weapon | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:---------------|:-----------------| | 0 | 28 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X |

coreybrady/blackwoods

--- license: cc-by-nc-4.0 ---

fengtc/para-train-1.1M

--- dataset_info: features: - name: kind dtype: string - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1116096350 num_examples: 1649399 download_size: 797767383 dataset_size: 1116096350 configs: - config_name: default data_files: - split: train path: data/train-* ---

Multimodal-Fatima/OxfordFlowers_test_facebook_opt_350m_Attributes_Caption_ns_6149

--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_3_bs_16 num_bytes: 272760304.375 num_examples: 6149 - name: fewshot_0_bs_16 num_bytes: 267303635.375 num_examples: 6149 - name: fewshot_1_bs_16 num_bytes: 269129477.375 num_examples: 6149 download_size: 796845849 dataset_size: 809193417.125 --- # Dataset Card for "OxfordFlowers_test_facebook_opt_350m_Attributes_Caption_ns_6149" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

alvarobartt/ultrafeedback-binarized-preferences-clean

--- dataset_info: features: - name: source dtype: string - name: instruction dtype: string - name: models sequence: string - name: completions list: - name: annotations struct: - name: instruction_following struct: - name: Rating dtype: string - name: Rationale dtype: string - name: honesty struct: - name: Rating dtype: string - name: Rationale dtype: string - name: truthfulness struct: - name: Type sequence: string - name: Rationale dtype: string - name: Rating dtype: string - name: Rationale For Rating dtype: string - name: helpfulness struct: - name: Type sequence: string - name: Rationale dtype: string - name: Rating dtype: string - name: Rationale For Rating dtype: string - name: custom_system_prompt dtype: string - name: model dtype: string - name: principle dtype: string - name: response dtype: string - name: critique dtype: string - name: overall_score dtype: float64 - name: correct_answers sequence: string - name: incorrect_answers sequence: string splits: - name: train num_bytes: 831088377.5421702 num_examples: 63136 download_size: 318279041 dataset_size: 831088377.5421702 configs: - config_name: default data_files: - split: train path: data/train-* ---

AlekseyKorshuk/lmeh-chai-synthetic

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: test num_bytes: 47839521 num_examples: 4570 download_size: 15451629 dataset_size: 47839521 --- # Dataset Card for "lmeh-chai-synthetic" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Treelar/plant_village

--- license: cc0-1.0 dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Apple___Apple_scab '1': Apple___Black_rot '2': Apple___Cedar_apple_rust '3': Apple___healthy '4': Background_without_leaves '5': Blueberry___healthy '6': Cherry___Powdery_mildew '7': Cherry___healthy '8': Corn___Cercospora_leaf_spot Gray_leaf_spot '9': Corn___Common_rust '10': Corn___Northern_Leaf_Blight '11': Corn___healthy '12': Grape___Black_rot '13': Grape___Esca_(Black_Measles) '14': Grape___Leaf_blight_(Isariopsis_Leaf_Spot) '15': Grape___healthy '16': Orange___Haunglongbing_(Citrus_greening) '17': Peach___Bacterial_spot '18': Peach___healthy '19': Pepper,_bell___Bacterial_spot '20': Pepper,_bell___healthy '21': Potato___Early_blight '22': Potato___Late_blight '23': Potato___healthy '24': Raspberry___healthy '25': Soybean___healthy '26': Squash___Powdery_mildew '27': Strawberry___Leaf_scorch '28': Strawberry___healthy '29': Tomato___Bacterial_spot '30': Tomato___Early_blight '31': Tomato___Late_blight '32': Tomato___Leaf_Mold '33': Tomato___Septoria_leaf_spot '34': Tomato___Spider_mites Two-spotted_spider_mite '35': Tomato___Target_Spot '36': Tomato___Tomato_Yellow_Leaf_Curl_Virus '37': Tomato___Tomato_mosaic_virus '38': Tomato___healthy splits: - name: train num_bytes: 872351166.322 num_examples: 55447 download_size: 860727476 dataset_size: 872351166.322 configs: - config_name: default data_files: - split: train path: data/train-* ---

autoevaluate/autoeval-staging-eval-big_patent-y-7d0862-15806178

--- type: predictions tags: - autotrain - evaluation datasets: - big_patent eval_info: task: summarization model: pszemraj/led-base-book-summary metrics: [] dataset_name: big_patent dataset_config: y dataset_split: test col_mapping: text: description target: abstract --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: pszemraj/led-base-book-summary * Dataset: big_patent * Config: y * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.

toloka/TolokerGraph

--- license: cc-by-4.0 tags: - toloka - graph - node-classification pretty_name: Graph of Crowd Annotators size_categories: - 10K<n<100K task_categories: - graph-ml dataset_info: features: - name: id dtype: int32 - name: approved_rate dtype: float - name: skipped_rate dtype: float - name: expired_rate dtype: float - name: rejected_rate dtype: float - name: education dtype: string - name: english_profile dtype: bool - name: english_tested dtype: bool - name: banned dtype: bool --- # Toloker Graph: Interaction of Crowd Annotators ## Dataset Description - **Repository:** https://github.com/Toloka/TolokerGraph - **Point of Contact:** research@toloka.ai ### Dataset Summary This repository contains a graph representing interactions between crowd annotators on a project labeled on the [Toloka](https://toloka.ai/) crowdsourcing platform (see the [Toloka overview](https://toloka.ai/en/docs/guide/concepts/overview) for the details on the used terminology). The graph contains 11,758 nodes and 519,000 edges. Each node represents an individual annotator; nodes are provided with four numerical and three categorical features. An edge is drawn between a pair of annotators if they annotated the same task. Also, each node is provided with a label showing whether the annotator was banned on this project, or not. ### Nodes Nodes are stored in the [nodes.tsv](nodes.tsv) file in the TSV format of the following structure: - `id`: unique identifier of the annotator - `approved_rate`: percentage of the approved labels of this annotator - `skipped_rate`: percentage of the skipped tasks of this annotator - `expired_rate`: percentage of the expired tasks of this annotator - `rejected_rate`: percentage of the rejected labels of this annotator - `education`: level of education as self-reported by this annotator (`none`, `basic`, `middle`, `high`) - `english_profile`: knowledge of English as self-reported by this annotator (`0` for no, `1` for yes) - `english_tested`: whether the annotator passed the Toloka language test for English (`0` for no, `1` for yes) - `banned`: whether the annotator was banned on this project (`0` for no, `1` for yes) The `*_rate` attributes should sum up to 1. ### Edges Edges are stored in the [edges.tsv](edges.tsv) file in the TSV format of the following structure: - `source`: source identifier of the annotator - `target`: target identifier of the annotator As the graph is undirected, `source` and `target` can be interchanged for the given pair of nodes. ### Citation * Likhobaba, D., Pavlichenko, N., Ustalov, D. (2023). [Toloker Graph: Interaction of Crowd Annotators](https://doi.org/10.5281/zenodo.7620795). Zenodo. <https://doi.org/10.5281/zenodo.7620795> ```bibtex @dataset{Tolokers, author = {Likhobaba, Daniil and Pavlichenko, Nikita and Ustalov, Dmitry}, title = {{Toloker Graph: Interaction of Crowd Annotators}}, year = {2023}, publisher = {Zenodo}, doi = {10.5281/zenodo.7620795}, url = {https://github.com/Toloka/TolokerGraph}, language = {english}, } ``` ### Copyright Licensed under the Creative Commons Attribution 4.0 License. See LICENSE file for more details.

EduAlmeida/brendataset

--- license: openrail++ ---

Phando/vision-flan_191-task_1k

--- dataset_info: features: - name: id dtype: string - name: image dtype: image - name: task_name dtype: string - name: instruction dtype: string - name: response dtype: string splits: - name: train num_bytes: 33215298748.003 num_examples: 186103 download_size: 36889036585 dataset_size: 33215298748.003 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "vision-flan_191-task_1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberCoder

--- pretty_name: Evaluation run of LeroyDyer/Mixtral_AI_CyberCoder dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [LeroyDyer/Mixtral_AI_CyberCoder](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberCoder)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberCoder\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-08T15:32:36.734199](https://huggingface.co/datasets/open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberCoder/blob/main/results_2024-04-08T15-32-36.734199.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.49404669002437496,\n\ \ \"acc_stderr\": 0.03438132143312601,\n \"acc_norm\": 0.5005202795269466,\n\ \ \"acc_norm_stderr\": 0.03515083188087076,\n \"mc1\": 0.2802937576499388,\n\ \ \"mc1_stderr\": 0.015723139524608763,\n \"mc2\": 0.40415611292084586,\n\ \ \"mc2_stderr\": 0.014695681144184423\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5008532423208191,\n \"acc_stderr\": 0.014611369529813276,\n\ \ \"acc_norm\": 0.560580204778157,\n \"acc_norm_stderr\": 0.014503747823580122\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.54690300736905,\n \ \ \"acc_stderr\": 0.004967778940011932,\n \"acc_norm\": 0.7484564827723561,\n\ \ \"acc_norm_stderr\": 0.004330134219762844\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.42962962962962964,\n\ \ \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.42962962962962964,\n\ \ \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5394736842105263,\n \"acc_stderr\": 0.04056242252249033,\n\ \ \"acc_norm\": 0.5394736842105263,\n \"acc_norm_stderr\": 0.04056242252249033\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.47,\n\ \ \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \ \ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5735849056603773,\n \"acc_stderr\": 0.03043779434298305,\n\ \ \"acc_norm\": 0.5735849056603773,\n \"acc_norm_stderr\": 0.03043779434298305\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5416666666666666,\n\ \ \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.5416666666666666,\n\ \ \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.44,\n\ \ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \ \ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5202312138728323,\n\ \ \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.5202312138728323,\n\ \ \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207763,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207763\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n\ \ \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.43829787234042555,\n \"acc_stderr\": 0.03243618636108102,\n\ \ \"acc_norm\": 0.43829787234042555,\n \"acc_norm_stderr\": 0.03243618636108102\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.34210526315789475,\n\ \ \"acc_stderr\": 0.04462917535336936,\n \"acc_norm\": 0.34210526315789475,\n\ \ \"acc_norm_stderr\": 0.04462917535336936\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3492063492063492,\n \"acc_stderr\": 0.024552292209342665,\n \"\ acc_norm\": 0.3492063492063492,\n \"acc_norm_stderr\": 0.024552292209342665\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30158730158730157,\n\ \ \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n\ \ \"acc_norm_stderr\": 0.04104947269903394\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3225806451612903,\n\ \ \"acc_stderr\": 0.02659308451657228,\n \"acc_norm\": 0.3225806451612903,\n\ \ \"acc_norm_stderr\": 0.02659308451657228\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.2955665024630542,\n \"acc_stderr\": 0.032104944337514575,\n\ \ \"acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.032104944337514575\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\"\ : 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6363636363636364,\n \"acc_stderr\": 0.037563357751878974,\n\ \ \"acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.037563357751878974\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6616161616161617,\n \"acc_stderr\": 0.03371124142626303,\n \"\ acc_norm\": 0.6616161616161617,\n \"acc_norm_stderr\": 0.03371124142626303\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7305699481865285,\n \"acc_stderr\": 0.032018671228777947,\n\ \ \"acc_norm\": 0.7305699481865285,\n \"acc_norm_stderr\": 0.032018671228777947\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.45384615384615384,\n \"acc_stderr\": 0.025242770987126177,\n\ \ \"acc_norm\": 0.45384615384615384,\n \"acc_norm_stderr\": 0.025242770987126177\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275794,\n \ \ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275794\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5252100840336135,\n \"acc_stderr\": 0.03243718055137411,\n \ \ \"acc_norm\": 0.5252100840336135,\n \"acc_norm_stderr\": 0.03243718055137411\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\ acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.6495412844036698,\n \"acc_stderr\": 0.02045607759982446,\n \"\ acc_norm\": 0.6495412844036698,\n \"acc_norm_stderr\": 0.02045607759982446\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.39814814814814814,\n \"acc_stderr\": 0.033384734032074016,\n \"\ acc_norm\": 0.39814814814814814,\n \"acc_norm_stderr\": 0.033384734032074016\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.5833333333333334,\n \"acc_stderr\": 0.03460228327239172,\n \"\ acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.03460228327239172\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.6371308016877637,\n \"acc_stderr\": 0.03129920825530213,\n \ \ \"acc_norm\": 0.6371308016877637,\n \"acc_norm_stderr\": 0.03129920825530213\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5829596412556054,\n\ \ \"acc_stderr\": 0.03309266936071721,\n \"acc_norm\": 0.5829596412556054,\n\ \ \"acc_norm_stderr\": 0.03309266936071721\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5877862595419847,\n \"acc_stderr\": 0.04317171194870254,\n\ \ \"acc_norm\": 0.5877862595419847,\n \"acc_norm_stderr\": 0.04317171194870254\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7024793388429752,\n \"acc_stderr\": 0.04173349148083499,\n \"\ acc_norm\": 0.7024793388429752,\n \"acc_norm_stderr\": 0.04173349148083499\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6111111111111112,\n\ \ \"acc_stderr\": 0.04712821257426769,\n \"acc_norm\": 0.6111111111111112,\n\ \ \"acc_norm_stderr\": 0.04712821257426769\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.558282208588957,\n \"acc_stderr\": 0.03901591825836185,\n\ \ \"acc_norm\": 0.558282208588957,\n \"acc_norm_stderr\": 0.03901591825836185\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6407766990291263,\n \"acc_stderr\": 0.04750458399041695,\n\ \ \"acc_norm\": 0.6407766990291263,\n \"acc_norm_stderr\": 0.04750458399041695\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7905982905982906,\n\ \ \"acc_stderr\": 0.026655699653922754,\n \"acc_norm\": 0.7905982905982906,\n\ \ \"acc_norm_stderr\": 0.026655699653922754\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6475095785440613,\n\ \ \"acc_stderr\": 0.01708415024408138,\n \"acc_norm\": 0.6475095785440613,\n\ \ \"acc_norm_stderr\": 0.01708415024408138\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5867052023121387,\n \"acc_stderr\": 0.026511261369409244,\n\ \ \"acc_norm\": 0.5867052023121387,\n \"acc_norm_stderr\": 0.026511261369409244\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.22569832402234638,\n\ \ \"acc_stderr\": 0.013981395058455052,\n \"acc_norm\": 0.22569832402234638,\n\ \ \"acc_norm_stderr\": 0.013981395058455052\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5326797385620915,\n \"acc_stderr\": 0.028568699752225875,\n\ \ \"acc_norm\": 0.5326797385620915,\n \"acc_norm_stderr\": 0.028568699752225875\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5659163987138264,\n\ \ \"acc_stderr\": 0.028150232244535597,\n \"acc_norm\": 0.5659163987138264,\n\ \ \"acc_norm_stderr\": 0.028150232244535597\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.5308641975308642,\n \"acc_stderr\": 0.02776768960683393,\n\ \ \"acc_norm\": 0.5308641975308642,\n \"acc_norm_stderr\": 0.02776768960683393\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.38652482269503546,\n \"acc_stderr\": 0.029049190342543465,\n \ \ \"acc_norm\": 0.38652482269503546,\n \"acc_norm_stderr\": 0.029049190342543465\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3305084745762712,\n\ \ \"acc_stderr\": 0.012014142101842965,\n \"acc_norm\": 0.3305084745762712,\n\ \ \"acc_norm_stderr\": 0.012014142101842965\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.4007352941176471,\n \"acc_stderr\": 0.029768263528933116,\n\ \ \"acc_norm\": 0.4007352941176471,\n \"acc_norm_stderr\": 0.029768263528933116\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5,\n \"acc_stderr\": 0.020227834851568375,\n \"acc_norm\"\ : 0.5,\n \"acc_norm_stderr\": 0.020227834851568375\n },\n \"harness|hendrycksTest-public_relations|5\"\ : {\n \"acc\": 0.5545454545454546,\n \"acc_stderr\": 0.047605488214603246,\n\ \ \"acc_norm\": 0.5545454545454546,\n \"acc_norm_stderr\": 0.047605488214603246\n\ \ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6693877551020408,\n\ \ \"acc_stderr\": 0.03011642629654059,\n \"acc_norm\": 0.6693877551020408,\n\ \ \"acc_norm_stderr\": 0.03011642629654059\n },\n \"harness|hendrycksTest-sociology|5\"\ : {\n \"acc\": 0.5024875621890548,\n \"acc_stderr\": 0.03535490150137289,\n\ \ \"acc_norm\": 0.5024875621890548,\n \"acc_norm_stderr\": 0.03535490150137289\n\ \ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\ \ 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.68,\n\ \ \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-virology|5\"\ : {\n \"acc\": 0.42168674698795183,\n \"acc_stderr\": 0.03844453181770917,\n\ \ \"acc_norm\": 0.42168674698795183,\n \"acc_norm_stderr\": 0.03844453181770917\n\ \ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.672514619883041,\n\ \ \"acc_stderr\": 0.035993357714560276,\n \"acc_norm\": 0.672514619883041,\n\ \ \"acc_norm_stderr\": 0.035993357714560276\n },\n \"harness|truthfulqa:mc|0\"\ : {\n \"mc1\": 0.2802937576499388,\n \"mc1_stderr\": 0.015723139524608763,\n\ \ \"mc2\": 0.40415611292084586,\n \"mc2_stderr\": 0.014695681144184423\n\ \ },\n \"harness|winogrande|5\": {\n \"acc\": 0.7142857142857143,\n\ \ \"acc_stderr\": 0.012696531870038621\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.15314632297194844,\n \"acc_stderr\": 0.009919728152791484\n\ \ }\n}\n```" repo_url: https://huggingface.co/LeroyDyer/Mixtral_AI_CyberCoder leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|arc:challenge|25_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-08T15-32-36.734199.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|gsm8k|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hellaswag|10_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-08T15-32-36.734199.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-management|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T15-32-36.734199.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|truthfulqa:mc|0_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-08T15-32-36.734199.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_08T15_32_36.734199 path: - '**/details_harness|winogrande|5_2024-04-08T15-32-36.734199.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-08T15-32-36.734199.parquet' - config_name: results data_files: - split: 2024_04_08T15_32_36.734199 path: - results_2024-04-08T15-32-36.734199.parquet - split: latest path: - results_2024-04-08T15-32-36.734199.parquet --- # Dataset Card for Evaluation run of LeroyDyer/Mixtral_AI_CyberCoder  Dataset automatically created during the evaluation run of model [LeroyDyer/Mixtral_AI_CyberCoder](https://huggingface.co/LeroyDyer/Mixtral_AI_CyberCoder) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberCoder", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-08T15:32:36.734199](https://huggingface.co/datasets/open-llm-leaderboard/details_LeroyDyer__Mixtral_AI_CyberCoder/blob/main/results_2024-04-08T15-32-36.734199.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.49404669002437496, "acc_stderr": 0.03438132143312601, "acc_norm": 0.5005202795269466, "acc_norm_stderr": 0.03515083188087076, "mc1": 0.2802937576499388, "mc1_stderr": 0.015723139524608763, "mc2": 0.40415611292084586, "mc2_stderr": 0.014695681144184423 }, "harness|arc:challenge|25": { "acc": 0.5008532423208191, "acc_stderr": 0.014611369529813276, "acc_norm": 0.560580204778157, "acc_norm_stderr": 0.014503747823580122 }, "harness|hellaswag|10": { "acc": 0.54690300736905, "acc_stderr": 0.004967778940011932, "acc_norm": 0.7484564827723561, "acc_norm_stderr": 0.004330134219762844 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.42962962962962964, "acc_stderr": 0.04276349494376599, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.04276349494376599 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5394736842105263, "acc_stderr": 0.04056242252249033, "acc_norm": 0.5394736842105263, "acc_norm_stderr": 0.04056242252249033 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5735849056603773, "acc_stderr": 0.03043779434298305, "acc_norm": 0.5735849056603773, "acc_norm_stderr": 0.03043779434298305 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5416666666666666, "acc_stderr": 0.04166666666666665, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.04166666666666665 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5202312138728323, "acc_stderr": 0.03809342081273957, "acc_norm": 0.5202312138728323, "acc_norm_stderr": 0.03809342081273957 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.30392156862745096, "acc_stderr": 0.04576665403207763, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.04576665403207763 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.43829787234042555, "acc_stderr": 0.03243618636108102, "acc_norm": 0.43829787234042555, "acc_norm_stderr": 0.03243618636108102 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.34210526315789475, "acc_stderr": 0.04462917535336936, "acc_norm": 0.34210526315789475, "acc_norm_stderr": 0.04462917535336936 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4689655172413793, "acc_stderr": 0.04158632762097828, "acc_norm": 0.4689655172413793, "acc_norm_stderr": 0.04158632762097828 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3492063492063492, "acc_stderr": 0.024552292209342665, "acc_norm": 0.3492063492063492, "acc_norm_stderr": 0.024552292209342665 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.30158730158730157, "acc_stderr": 0.04104947269903394, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.04104947269903394 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.3225806451612903, "acc_stderr": 0.02659308451657228, "acc_norm": 0.3225806451612903, "acc_norm_stderr": 0.02659308451657228 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2955665024630542, "acc_stderr": 0.032104944337514575, "acc_norm": 0.2955665024630542, "acc_norm_stderr": 0.032104944337514575 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.43, "acc_stderr": 0.04975698519562428, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6363636363636364, "acc_stderr": 0.037563357751878974, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.037563357751878974 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6616161616161617, "acc_stderr": 0.03371124142626303, "acc_norm": 0.6616161616161617, "acc_norm_stderr": 0.03371124142626303 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7305699481865285, "acc_stderr": 0.032018671228777947, "acc_norm": 0.7305699481865285, "acc_norm_stderr": 0.032018671228777947 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.45384615384615384, "acc_stderr": 0.025242770987126177, "acc_norm": 0.45384615384615384, "acc_norm_stderr": 0.025242770987126177 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24074074074074073, "acc_stderr": 0.026067159222275794, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.026067159222275794 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5252100840336135, "acc_stderr": 0.03243718055137411, "acc_norm": 0.5252100840336135, "acc_norm_stderr": 0.03243718055137411 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2980132450331126, "acc_stderr": 0.037345356767871984, "acc_norm": 0.2980132450331126, "acc_norm_stderr": 0.037345356767871984 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.6495412844036698, "acc_stderr": 0.02045607759982446, "acc_norm": 0.6495412844036698, "acc_norm_stderr": 0.02045607759982446 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.39814814814814814, "acc_stderr": 0.033384734032074016, "acc_norm": 0.39814814814814814, "acc_norm_stderr": 0.033384734032074016 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5833333333333334, "acc_stderr": 0.03460228327239172, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.03460228327239172 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6371308016877637, "acc_stderr": 0.03129920825530213, "acc_norm": 0.6371308016877637, "acc_norm_stderr": 0.03129920825530213 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.5829596412556054, "acc_stderr": 0.03309266936071721, "acc_norm": 0.5829596412556054, "acc_norm_stderr": 0.03309266936071721 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5877862595419847, "acc_stderr": 0.04317171194870254, "acc_norm": 0.5877862595419847, "acc_norm_stderr": 0.04317171194870254 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7024793388429752, "acc_stderr": 0.04173349148083499, "acc_norm": 0.7024793388429752, "acc_norm_stderr": 0.04173349148083499 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6111111111111112, "acc_stderr": 0.04712821257426769, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.04712821257426769 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.558282208588957, "acc_stderr": 0.03901591825836185, "acc_norm": 0.558282208588957, "acc_norm_stderr": 0.03901591825836185 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness|hendrycksTest-management|5": { "acc": 0.6407766990291263, "acc_stderr": 0.04750458399041695, "acc_norm": 0.6407766990291263, "acc_norm_stderr": 0.04750458399041695 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7905982905982906, "acc_stderr": 0.026655699653922754, "acc_norm": 0.7905982905982906, "acc_norm_stderr": 0.026655699653922754 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.6475095785440613, "acc_stderr": 0.01708415024408138, "acc_norm": 0.6475095785440613, "acc_norm_stderr": 0.01708415024408138 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5867052023121387, "acc_stderr": 0.026511261369409244, "acc_norm": 0.5867052023121387, "acc_norm_stderr": 0.026511261369409244 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.22569832402234638, "acc_stderr": 0.013981395058455052, "acc_norm": 0.22569832402234638, "acc_norm_stderr": 0.013981395058455052 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5326797385620915, "acc_stderr": 0.028568699752225875, "acc_norm": 0.5326797385620915, "acc_norm_stderr": 0.028568699752225875 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5659163987138264, "acc_stderr": 0.028150232244535597, "acc_norm": 0.5659163987138264, "acc_norm_stderr": 0.028150232244535597 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.5308641975308642, "acc_stderr": 0.02776768960683393, "acc_norm": 0.5308641975308642, "acc_norm_stderr": 0.02776768960683393 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.38652482269503546, "acc_stderr": 0.029049190342543465, "acc_norm": 0.38652482269503546, "acc_norm_stderr": 0.029049190342543465 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3305084745762712, "acc_stderr": 0.012014142101842965, "acc_norm": 0.3305084745762712, "acc_norm_stderr": 0.012014142101842965 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.4007352941176471, "acc_stderr": 0.029768263528933116, "acc_norm": 0.4007352941176471, "acc_norm_stderr": 0.029768263528933116 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5, "acc_stderr": 0.020227834851568375, "acc_norm": 0.5, "acc_norm_stderr": 0.020227834851568375 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5545454545454546, "acc_stderr": 0.047605488214603246, "acc_norm": 0.5545454545454546, "acc_norm_stderr": 0.047605488214603246 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6693877551020408, "acc_stderr": 0.03011642629654059, "acc_norm": 0.6693877551020408, "acc_norm_stderr": 0.03011642629654059 }, "harness|hendrycksTest-sociology|5": { "acc": 0.5024875621890548, "acc_stderr": 0.03535490150137289, "acc_norm": 0.5024875621890548, "acc_norm_stderr": 0.03535490150137289 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-virology|5": { "acc": 0.42168674698795183, "acc_stderr": 0.03844453181770917, "acc_norm": 0.42168674698795183, "acc_norm_stderr": 0.03844453181770917 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.672514619883041, "acc_stderr": 0.035993357714560276, "acc_norm": 0.672514619883041, "acc_norm_stderr": 0.035993357714560276 }, "harness|truthfulqa:mc|0": { "mc1": 0.2802937576499388, "mc1_stderr": 0.015723139524608763, "mc2": 0.40415611292084586, "mc2_stderr": 0.014695681144184423 }, "harness|winogrande|5": { "acc": 0.7142857142857143, "acc_stderr": 0.012696531870038621 }, "harness|gsm8k|5": { "acc": 0.15314632297194844, "acc_stderr": 0.009919728152791484 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

biglam/atypical_animacy

--- annotations_creators: - expert-generated language_creators: - machine-generated language: - en license: - cc0-1.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification - intent-classification pretty_name: Atypical Animacy dataset_info: features: - name: id dtype: string - name: sentence dtype: string - name: context dtype: string - name: target dtype: string - name: animacy dtype: float32 - name: humanness dtype: float32 - name: offsets list: int32 - name: date dtype: string splits: - name: train num_bytes: 442217 num_examples: 594 download_size: 299650 dataset_size: 442217 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for atypical_animacy ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** https://bl.iro.bl.uk/concern/datasets/323177af-6081-4e93-8aaf-7932ca4a390a?locale=en - **Repository:** https://github.com/Living-with-machines/AtypicalAnimacy - **Paper:** https://arxiv.org/abs/2005.11140 - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Mariona Coll Ardanuy](mailto:mcollardanuy@turing.ac.uk), [Daniel CS Wilson](mailto:dwilson@turing.ac.uk) ### Dataset Summary Atypical animacy detection dataset, based on nineteenth-century sentences in English extracted from an open dataset of nineteenth-century books digitized by the British Library. This dataset contains 598 sentences containing mentions of machines. Each sentence has been annotated according to the animacy and humanness of the machine in the sentence. ### Supported Tasks and Leaderboards - `text-classification` - This dataset can be used to determine if a mention of an entity in a document was humanlike or not - `entity-recognition` - The dataset can be used to fine tune large models for NER, albeit for a very specific use case ### Languages The text in the dataset is in English, as written by authors of books digitized by the British Library. The associated BCP-47 code in `en` ## Dataset Structure The dataset has a single configuration ### Data Instances An example data point ``` {'id': '002757962_01_184_16', 'sentence': '100 shows a Cornish boiler improperly seated with one small side flue and a bottom flue.', 'context': 'Fig. 100 shows a Cornish boiler improperly seated with one small side flue and a bottom flue. The effect of this on a long boiler is to cause springing and leakage of the seams from the heat being applied to one side of the boiler only.', 'target': 'boiler', 'animacy': 0.0, 'humanness': 1.0, 'offsets': [20, 26], 'date': '1893'} ``` ### Data Fields - id: sentence identifier according to internal Living with Machines BL books indexing. - sentence: sentence where target expression occurs. - context: sentence where target expression occurs, plus one sentence to the left and one sentence to the right. - target: target expression - animacy: animacy of the target expression - humanness: humanness of the target expression ### Data Splits Train | 598 ## Dataset Creation The dataset was created by manually annotating books that had been digitized by the British Library. According to the paper's authors, > "we provide a basis for examining how machines were imagined during the nineteenth century as everything from lifeless mechanical objects to living beings, or even human-like agents that feel, think, and love. We focus on texts from nineteenth-century Britain, a society being transformed by industrialization, as a good candidate for studying the broader issue" ### Curation Rationale From the paper: > The Stories dataset is largely composed of target expressions that correspond to either typically animate or typically inanimate entities. Even though some cases of unconventional animacy can be found(folktales, in particular, are richer in typically inanimate entities that become animate), these accountfor a very small proportion of the data.6 We decided to create our own dataset (henceforth 19thC Machines dataset) to gain a better sense of the suitability of our method to the problem of atypical animacy detection, with particular attention to the case of animacy of machines in nineteenth-century texts. ### Source Data #### Initial Data Collection and Normalization The dataset was generated by manually annotating books that have been digitized by the British Library #### Who are the source language producers? The data was originally produced by British authors in the 19th century. The books were then digitzed whcih produces some noise due to the OCR method. The annotators are from The Alan Turing Institute, The British Library, University of Cambridge, University of Exeter and Queen Mary University of London ### Annotations #### Annotation process Annotation was carried out in two parts. For the intial annotation process, from the paper: > "For human annotators, even history and literature experts, language subtleties made this task extremely subjective. In the first task, we masked the target word (i.e. the machine) in each sentence and asked the annotator to fill the slot with the most likely entity between ‘human’, ‘horse’, and ‘machine’, representing three levels in the animacy hierarchy: human, animal, and object (Comrie, 1989, 185). We asked annotators to stick to the most literal meaning and avoid metaphorical interpretations when possible. The second task was more straightforwardly related to determining the animacy of the target entity, given the same 100 sentences. We asked annotators to provide a score between -2 and 2, with -2 being definitely inanimate, -1 possibly inanimate, 1 possibly animate, and 2 definitely animate. Neutral judgements were not allowed. " For the final annotations, from the paper: > A subgroup of five annotators collaboratively wrote the guidelines based on their experience annotating the first batch of sentences, taking into account common discrepancies. After discussion, it was decided that a machine would be tagged as animate if it is described as having traits distinctive of biologically animate beings or human-specific skills, or portrayed as having feelings, emotions, or a soul. Sentences like the ones in example 2 would be considered animate, but an additional annotation layer would be provided to capture the notion of humanness, which would be true if the machine is portrayed as sentient and capable of specifically human emotions, and false if it used to suggest some degree of dehumanization. #### Who are the annotators? Annotations were carried out by the following people - Giorgia Tolfo - Ruth Ahnert - Kaspar Beelen - Mariona Coll Ardanuy - Jon Lawrence - Katherine McDonough - Federico Nanni - Daniel CS Wilson ### Personal and Sensitive Information This dataset does not have any personal information since they are digitizations of books from the 19th century. Some passages might be sensitive, but it is not explicitly mentioned in the paper. ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators The curators for this dataset are: - Kaspar Beelen - Mariona Coll Ardanuy - Federico Nanni - Giorgia Tolfo ### Licensing Information CC0 1.0 Universal Public Domain ### Citation Information ``` @article{DBLP:journals/corr/abs-2005-11140, author = {Mariona Coll Ardanuy and Federico Nanni and Kaspar Beelen and Kasra Hosseini and Ruth Ahnert and Jon Lawrence and Katherine McDonough and Giorgia Tolfo and Daniel C. S. Wilson and Barbara McGillivray}, title = {Living Machines: {A} study of atypical animacy}, journal = {CoRR}, volume = {abs/2005.11140}, year = {2020}, url = {https://arxiv.org/abs/2005.11140}, eprinttype = {arXiv}, eprint = {2005.11140}, timestamp = {Sat, 23 Jan 2021 01:12:25 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2005-11140.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

GGital/Signal_Test04

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' splits: - name: train num_bytes: 23704098.0 num_examples: 647 download_size: 23733105 dataset_size: 23704098.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_field2437__phi-2-platypus-Commercial-lora

--- pretty_name: Evaluation run of field2437/phi-2-platypus-Commercial-lora dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [field2437/phi-2-platypus-Commercial-lora](https://huggingface.co/field2437/phi-2-platypus-Commercial-lora)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_field2437__phi-2-platypus-Commercial-lora\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-09T18:21:39.850976](https://huggingface.co/datasets/open-llm-leaderboard/details_field2437__phi-2-platypus-Commercial-lora/blob/main/results_2024-03-09T18-21-39.850976.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5822377157170969,\n\ \ \"acc_stderr\": 0.03381280917335049,\n \"acc_norm\": 0.5836335693694722,\n\ \ \"acc_norm_stderr\": 0.03450596737878276,\n \"mc1\": 0.31701346389228885,\n\ \ \"mc1_stderr\": 0.016289203374403385,\n \"mc2\": 0.4545655863854703,\n\ \ \"mc2_stderr\": 0.0151216566833299\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5716723549488054,\n \"acc_stderr\": 0.014460496367599012,\n\ \ \"acc_norm\": 0.6040955631399317,\n \"acc_norm_stderr\": 0.014291228393536592\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5657239593706433,\n\ \ \"acc_stderr\": 0.0049464854665446254,\n \"acc_norm\": 0.7512447719577773,\n\ \ \"acc_norm_stderr\": 0.0043140816086246455\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4666666666666667,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5789473684210527,\n \"acc_stderr\": 0.040179012759817494,\n\ \ \"acc_norm\": 0.5789473684210527,\n \"acc_norm_stderr\": 0.040179012759817494\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\ \ \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \ \ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6075471698113207,\n \"acc_stderr\": 0.030052580579557845,\n\ \ \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.030052580579557845\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6111111111111112,\n\ \ \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n\ \ \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n\ \ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562426,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562426\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5780346820809249,\n\ \ \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.5780346820809249,\n\ \ \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943,\n\ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201943\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\ \ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467381,\n\ \ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467381\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.38596491228070173,\n\ \ \"acc_stderr\": 0.04579639422070434,\n \"acc_norm\": 0.38596491228070173,\n\ \ \"acc_norm_stderr\": 0.04579639422070434\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.041618085035015295,\n\ \ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.041618085035015295\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4576719576719577,\n \"acc_stderr\": 0.02565886886205833,\n \"\ acc_norm\": 0.4576719576719577,\n \"acc_norm_stderr\": 0.02565886886205833\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\ \ \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n\ \ \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6935483870967742,\n\ \ \"acc_stderr\": 0.02622648565255388,\n \"acc_norm\": 0.6935483870967742,\n\ \ \"acc_norm_stderr\": 0.02622648565255388\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\ \ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\ : 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6606060606060606,\n \"acc_stderr\": 0.036974422050315967,\n\ \ \"acc_norm\": 0.6606060606060606,\n \"acc_norm_stderr\": 0.036974422050315967\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7424242424242424,\n \"acc_stderr\": 0.031156269519646836,\n \"\ acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.031156269519646836\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7875647668393783,\n \"acc_stderr\": 0.02951928261681723,\n\ \ \"acc_norm\": 0.7875647668393783,\n \"acc_norm_stderr\": 0.02951928261681723\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5974358974358974,\n \"acc_stderr\": 0.024864995159767755,\n\ \ \"acc_norm\": 0.5974358974358974,\n \"acc_norm_stderr\": 0.024864995159767755\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948503,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948503\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5966386554621849,\n \"acc_stderr\": 0.031866081214088314,\n\ \ \"acc_norm\": 0.5966386554621849,\n \"acc_norm_stderr\": 0.031866081214088314\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.41721854304635764,\n \"acc_stderr\": 0.040261414976346104,\n \"\ acc_norm\": 0.41721854304635764,\n \"acc_norm_stderr\": 0.040261414976346104\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8036697247706422,\n \"acc_stderr\": 0.017030719339154336,\n \"\ acc_norm\": 0.8036697247706422,\n \"acc_norm_stderr\": 0.017030719339154336\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\ \ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.6568627450980392,\n\ \ \"acc_stderr\": 0.03332139944668086,\n \"acc_norm\": 0.6568627450980392,\n\ \ \"acc_norm_stderr\": 0.03332139944668086\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.7426160337552743,\n \"acc_stderr\": 0.028458820991460285,\n\ \ \"acc_norm\": 0.7426160337552743,\n \"acc_norm_stderr\": 0.028458820991460285\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n\ \ \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n\ \ \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6717557251908397,\n \"acc_stderr\": 0.041184385658062976,\n\ \ \"acc_norm\": 0.6717557251908397,\n \"acc_norm_stderr\": 0.041184385658062976\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096994,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096994\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7037037037037037,\n\ \ \"acc_stderr\": 0.04414343666854933,\n \"acc_norm\": 0.7037037037037037,\n\ \ \"acc_norm_stderr\": 0.04414343666854933\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8076923076923077,\n\ \ \"acc_stderr\": 0.025819233256483706,\n \"acc_norm\": 0.8076923076923077,\n\ \ \"acc_norm_stderr\": 0.025819233256483706\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.685823754789272,\n\ \ \"acc_stderr\": 0.016599291735884897,\n \"acc_norm\": 0.685823754789272,\n\ \ \"acc_norm_stderr\": 0.016599291735884897\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.653179190751445,\n \"acc_stderr\": 0.025624723994030454,\n\ \ \"acc_norm\": 0.653179190751445,\n \"acc_norm_stderr\": 0.025624723994030454\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2212290502793296,\n\ \ \"acc_stderr\": 0.013882164598887265,\n \"acc_norm\": 0.2212290502793296,\n\ \ \"acc_norm_stderr\": 0.013882164598887265\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6045751633986928,\n \"acc_stderr\": 0.027996723180631455,\n\ \ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.027996723180631455\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6559485530546624,\n\ \ \"acc_stderr\": 0.026981478043648026,\n \"acc_norm\": 0.6559485530546624,\n\ \ \"acc_norm_stderr\": 0.026981478043648026\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.654320987654321,\n \"acc_stderr\": 0.02646248777700187,\n\ \ \"acc_norm\": 0.654320987654321,\n \"acc_norm_stderr\": 0.02646248777700187\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4432624113475177,\n \"acc_stderr\": 0.029634838473766002,\n \ \ \"acc_norm\": 0.4432624113475177,\n \"acc_norm_stderr\": 0.029634838473766002\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41916558018252936,\n\ \ \"acc_stderr\": 0.012602244505788238,\n \"acc_norm\": 0.41916558018252936,\n\ \ \"acc_norm_stderr\": 0.012602244505788238\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.49264705882352944,\n \"acc_stderr\": 0.030369552523902173,\n\ \ \"acc_norm\": 0.49264705882352944,\n \"acc_norm_stderr\": 0.030369552523902173\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5522875816993464,\n \"acc_stderr\": 0.020116925347422425,\n \ \ \"acc_norm\": 0.5522875816993464,\n \"acc_norm_stderr\": 0.020116925347422425\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.028920583220675592,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.028920583220675592\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7661691542288557,\n\ \ \"acc_stderr\": 0.029929415408348384,\n \"acc_norm\": 0.7661691542288557,\n\ \ \"acc_norm_stderr\": 0.029929415408348384\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \ \ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4819277108433735,\n\ \ \"acc_stderr\": 0.038899512528272166,\n \"acc_norm\": 0.4819277108433735,\n\ \ \"acc_norm_stderr\": 0.038899512528272166\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7017543859649122,\n \"acc_stderr\": 0.03508771929824563,\n\ \ \"acc_norm\": 0.7017543859649122,\n \"acc_norm_stderr\": 0.03508771929824563\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.31701346389228885,\n\ \ \"mc1_stderr\": 0.016289203374403385,\n \"mc2\": 0.4545655863854703,\n\ \ \"mc2_stderr\": 0.0151216566833299\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7458563535911602,\n \"acc_stderr\": 0.012236307219708262\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.55420773313116,\n \ \ \"acc_stderr\": 0.013691305174506686\n }\n}\n```" repo_url: https://huggingface.co/field2437/phi-2-platypus-Commercial-lora leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|arc:challenge|25_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-09T18-21-39.850976.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|gsm8k|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hellaswag|10_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-09T18-21-39.850976.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-management|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T18-21-39.850976.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|truthfulqa:mc|0_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-09T18-21-39.850976.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_09T18_21_39.850976 path: - '**/details_harness|winogrande|5_2024-03-09T18-21-39.850976.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-09T18-21-39.850976.parquet' - config_name: results data_files: - split: 2024_03_09T18_21_39.850976 path: - results_2024-03-09T18-21-39.850976.parquet - split: latest path: - results_2024-03-09T18-21-39.850976.parquet --- # Dataset Card for Evaluation run of field2437/phi-2-platypus-Commercial-lora  Dataset automatically created during the evaluation run of model [field2437/phi-2-platypus-Commercial-lora](https://huggingface.co/field2437/phi-2-platypus-Commercial-lora) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_field2437__phi-2-platypus-Commercial-lora", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-09T18:21:39.850976](https://huggingface.co/datasets/open-llm-leaderboard/details_field2437__phi-2-platypus-Commercial-lora/blob/main/results_2024-03-09T18-21-39.850976.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.5822377157170969, "acc_stderr": 0.03381280917335049, "acc_norm": 0.5836335693694722, "acc_norm_stderr": 0.03450596737878276, "mc1": 0.31701346389228885, "mc1_stderr": 0.016289203374403385, "mc2": 0.4545655863854703, "mc2_stderr": 0.0151216566833299 }, "harness|arc:challenge|25": { "acc": 0.5716723549488054, "acc_stderr": 0.014460496367599012, "acc_norm": 0.6040955631399317, "acc_norm_stderr": 0.014291228393536592 }, "harness|hellaswag|10": { "acc": 0.5657239593706433, "acc_stderr": 0.0049464854665446254, "acc_norm": 0.7512447719577773, "acc_norm_stderr": 0.0043140816086246455 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4666666666666667, "acc_stderr": 0.043097329010363554, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.043097329010363554 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5789473684210527, "acc_stderr": 0.040179012759817494, "acc_norm": 0.5789473684210527, "acc_norm_stderr": 0.040179012759817494 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6075471698113207, "acc_stderr": 0.030052580579557845, "acc_norm": 0.6075471698113207, "acc_norm_stderr": 0.030052580579557845 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6111111111111112, "acc_stderr": 0.04076663253918567, "acc_norm": 0.6111111111111112, "acc_norm_stderr": 0.04076663253918567 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.43, "acc_stderr": 0.04975698519562426, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562426 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5780346820809249, "acc_stderr": 0.0376574669386515, "acc_norm": 0.5780346820809249, "acc_norm_stderr": 0.0376574669386515 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201943, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201943 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467381, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467381 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.38596491228070173, "acc_stderr": 0.04579639422070434, "acc_norm": 0.38596491228070173, "acc_norm_stderr": 0.04579639422070434 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5241379310344828, "acc_stderr": 0.041618085035015295, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.041618085035015295 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4576719576719577, "acc_stderr": 0.02565886886205833, "acc_norm": 0.4576719576719577, "acc_norm_stderr": 0.02565886886205833 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3968253968253968, "acc_stderr": 0.04375888492727061, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.04375888492727061 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6935483870967742, "acc_stderr": 0.02622648565255388, "acc_norm": 0.6935483870967742, "acc_norm_stderr": 0.02622648565255388 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511656986, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511656986 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6606060606060606, "acc_stderr": 0.036974422050315967, "acc_norm": 0.6606060606060606, "acc_norm_stderr": 0.036974422050315967 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7424242424242424, "acc_stderr": 0.031156269519646836, "acc_norm": 0.7424242424242424, "acc_norm_stderr": 0.031156269519646836 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7875647668393783, "acc_stderr": 0.02951928261681723, "acc_norm": 0.7875647668393783, "acc_norm_stderr": 0.02951928261681723 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5974358974358974, "acc_stderr": 0.024864995159767755, "acc_norm": 0.5974358974358974, "acc_norm_stderr": 0.024864995159767755 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948503, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948503 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5966386554621849, "acc_stderr": 0.031866081214088314, "acc_norm": 0.5966386554621849, "acc_norm_stderr": 0.031866081214088314 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.41721854304635764, "acc_stderr": 0.040261414976346104, "acc_norm": 0.41721854304635764, "acc_norm_stderr": 0.040261414976346104 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8036697247706422, "acc_stderr": 0.017030719339154336, "acc_norm": 0.8036697247706422, "acc_norm_stderr": 0.017030719339154336 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.6568627450980392, "acc_stderr": 0.03332139944668086, "acc_norm": 0.6568627450980392, "acc_norm_stderr": 0.03332139944668086 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7426160337552743, "acc_stderr": 0.028458820991460285, "acc_norm": 0.7426160337552743, "acc_norm_stderr": 0.028458820991460285 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6636771300448431, "acc_stderr": 0.031708824268455, "acc_norm": 0.6636771300448431, "acc_norm_stderr": 0.031708824268455 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6717557251908397, "acc_stderr": 0.041184385658062976, "acc_norm": 0.6717557251908397, "acc_norm_stderr": 0.041184385658062976 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096994, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096994 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7037037037037037, "acc_stderr": 0.04414343666854933, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.04414343666854933 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7607361963190185, "acc_stderr": 0.033519538795212696, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.033519538795212696 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5089285714285714, "acc_stderr": 0.04745033255489123, "acc_norm": 0.5089285714285714, "acc_norm_stderr": 0.04745033255489123 }, "harness|hendrycksTest-management|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8076923076923077, "acc_stderr": 0.025819233256483706, "acc_norm": 0.8076923076923077, "acc_norm_stderr": 0.025819233256483706 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.685823754789272, "acc_stderr": 0.016599291735884897, "acc_norm": 0.685823754789272, "acc_norm_stderr": 0.016599291735884897 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.653179190751445, "acc_stderr": 0.025624723994030454, "acc_norm": 0.653179190751445, "acc_norm_stderr": 0.025624723994030454 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2212290502793296, "acc_stderr": 0.013882164598887265, "acc_norm": 0.2212290502793296, "acc_norm_stderr": 0.013882164598887265 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6045751633986928, "acc_stderr": 0.027996723180631455, "acc_norm": 0.6045751633986928, "acc_norm_stderr": 0.027996723180631455 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6559485530546624, "acc_stderr": 0.026981478043648026, "acc_norm": 0.6559485530546624, "acc_norm_stderr": 0.026981478043648026 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.654320987654321, "acc_stderr": 0.02646248777700187, "acc_norm": 0.654320987654321, "acc_norm_stderr": 0.02646248777700187 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4432624113475177, "acc_stderr": 0.029634838473766002, "acc_norm": 0.4432624113475177, "acc_norm_stderr": 0.029634838473766002 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.41916558018252936, "acc_stderr": 0.012602244505788238, "acc_norm": 0.41916558018252936, "acc_norm_stderr": 0.012602244505788238 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.49264705882352944, "acc_stderr": 0.030369552523902173, "acc_norm": 0.49264705882352944, "acc_norm_stderr": 0.030369552523902173 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5522875816993464, "acc_stderr": 0.020116925347422425, "acc_norm": 0.5522875816993464, "acc_norm_stderr": 0.020116925347422425 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302505, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302505 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7142857142857143, "acc_stderr": 0.028920583220675592, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.028920583220675592 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7661691542288557, "acc_stderr": 0.029929415408348384, "acc_norm": 0.7661691542288557, "acc_norm_stderr": 0.029929415408348384 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-virology|5": { "acc": 0.4819277108433735, "acc_stderr": 0.038899512528272166, "acc_norm": 0.4819277108433735, "acc_norm_stderr": 0.038899512528272166 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7017543859649122, "acc_stderr": 0.03508771929824563, "acc_norm": 0.7017543859649122, "acc_norm_stderr": 0.03508771929824563 }, "harness|truthfulqa:mc|0": { "mc1": 0.31701346389228885, "mc1_stderr": 0.016289203374403385, "mc2": 0.4545655863854703, "mc2_stderr": 0.0151216566833299 }, "harness|winogrande|5": { "acc": 0.7458563535911602, "acc_stderr": 0.012236307219708262 }, "harness|gsm8k|5": { "acc": 0.55420773313116, "acc_stderr": 0.013691305174506686 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

Jellywibble/dalio-conversations-hackathon-dataset

--- dataset_info: features: - name: input_text dtype: string - name: scores dtype: int64 splits: - name: train num_bytes: 5026 num_examples: 8 download_size: 8422 dataset_size: 5026 --- # Dataset Card for "dalio-conversations-hackathon-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

vishruthnath/Calc-mawps-Tagged

--- dataset_info: features: - name: chain dtype: string - name: equation dtype: string - name: expression dtype: string - name: id dtype: string - name: num_unique_ops dtype: int64 - name: operand sequence: float64 - name: operand_tags sequence: int64 - name: operation dtype: string - name: question dtype: string - name: question_split sequence: string - name: result dtype: string - name: result_float dtype: float64 - name: valid dtype: bool - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 680417 num_examples: 1039 - name: validation num_bytes: 656088 num_examples: 1010 - name: test num_bytes: 330740 num_examples: 505 download_size: 463338 dataset_size: 1667245 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

fathyshalab/reklamation24_wasser-strom-gas-intent

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 203230 num_examples: 383 - name: test num_bytes: 52516 num_examples: 96 download_size: 142247 dataset_size: 255746 --- # Dataset Card for "reklamation24_wasser-strom-gas-intent" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

skrishna/filtered_toxic_samples

--- license: mit ---

sergeipetrov/transformers-diffusers-docs-chunked

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2482616 num_examples: 3833 download_size: 1155504 dataset_size: 2482616 configs: - config_name: default data_files: - split: train path: data/train-* ---

huggingartists/dj-artem-artemov

--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/dj-artem-artemov" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of the generated dataset:** 0.105061 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/7499a229de60cdfb23ce61f5924c401d.416x416x1.png')"> </div> </div> <a href="https://huggingface.co/huggingartists/dj-artem-artemov"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">DJ Artem Artemov</div> <a href="https://genius.com/artists/dj-artem-artemov"> <div style="text-align: center; font-size: 14px;">@dj-artem-artemov</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/dj-artem-artemov). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/dj-artem-artemov") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits | train |validation|test| |------:|---------:|---:| |56| -| -| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/dj-artem-artemov") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. 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fathyshalab/massive_news-de-DE

--- dataset_info: features: - name: id dtype: string - name: locale dtype: string - name: partition dtype: string - name: scenario dtype: class_label: names: '0': social '1': transport '2': calendar '3': play '4': news '5': datetime '6': recommendation '7': email '8': iot '9': general '10': audio '11': lists '12': qa '13': cooking '14': takeaway '15': music '16': alarm '17': weather - name: intent dtype: class_label: names: '0': datetime_query '1': iot_hue_lightchange '2': transport_ticket '3': takeaway_query '4': qa_stock '5': general_greet '6': recommendation_events '7': music_dislikeness '8': iot_wemo_off '9': cooking_recipe '10': qa_currency '11': transport_traffic '12': general_quirky '13': weather_query '14': audio_volume_up '15': email_addcontact '16': takeaway_order '17': email_querycontact '18': iot_hue_lightup '19': recommendation_locations '20': play_audiobook '21': lists_createoradd '22': news_query '23': alarm_query '24': iot_wemo_on '25': general_joke '26': qa_definition '27': social_query '28': music_settings '29': audio_volume_other '30': calendar_remove '31': iot_hue_lightdim '32': calendar_query '33': email_sendemail '34': iot_cleaning '35': audio_volume_down '36': play_radio '37': cooking_query '38': datetime_convert '39': qa_maths '40': iot_hue_lightoff '41': iot_hue_lighton '42': transport_query '43': music_likeness '44': email_query '45': play_music '46': audio_volume_mute '47': social_post '48': alarm_set '49': qa_factoid '50': calendar_set '51': play_game '52': alarm_remove '53': lists_remove '54': transport_taxi '55': recommendation_movies '56': iot_coffee '57': music_query '58': play_podcasts '59': lists_query - name: text dtype: string - name: annot_utt dtype: string - name: worker_id dtype: string - name: slot_method sequence: - name: slot dtype: string - name: method dtype: string - name: judgments sequence: - name: worker_id dtype: string - name: intent_score dtype: int8 - name: slots_score dtype: int8 - name: grammar_score dtype: int8 - name: spelling_score dtype: int8 - name: language_identification dtype: string - name: label_name dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 147499 num_examples: 503 - name: validation num_bytes: 25026 num_examples: 82 - name: test num_bytes: 36859 num_examples: 124 download_size: 69773 dataset_size: 209384 --- # Dataset Card for "massive_news-de-DE" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ekrombouts/GenCareAI

--- license: gpl task_categories: - text-classification language: - nl tags: - medical - synthetic pretty_name: Synthetic client notes size_categories: - 1K<n<10K --- This dataset comprises synthetic client notes from nursing care residents, specifically created for natural language processing (NLP) projects. Its primary application is in the development of models aimed at predicting agitation scores in healthcare settings, such as nursing homes. While the dataset serves well for training purposes, it is important to note that it lacks the variation and nuance typically found in real client notes. Therefore, while beneficial for initial model training, it may not fully represent the complexities encountered in actual clinical environments. ### Data Collection - **Data type:** Text (synthetic notes) - **Source:** Generated by OpenAI API - **Language:** Dutch - **Period:** January 2024 - **Method:** Generated by OpenAI's language model 'gpt-3.5-turbo' ### Dataset Characteristics - **Size:** 8,699 rows (train: 16 clients, 5788 notes, valid: 4 clients, 1455 notes, test: 4 clients, 1456 notes) - **Format:** csv - **Fields:** - ct_id: Client ID - datum: Date - discipline: Role of the author of the note - rapportage: Note text - onrust: Boolean value set to True if the agitation score exceeds 50 - onrustscore: Level of agitation described in the nursing care note. ## Intended Use - **Primary Use:** Fine-tuning LLMs for applications in healthcare, focusing on nursing homes. - **Use Cases:** Predicting agitation scores. ## Ethical Considerations - **Bias:** The dataset, generated by AI, may exhibit language and contextual biases, potentially influencing model predictions and affecting their fairness and accuracy ## Versioning - **Version:** 1.0 - **Date:** 20-01-2024

CyberHarem/meigo_arisa_toarumajutsunoindex

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Meigo Arisa This is the dataset of Meigo Arisa, containing 73 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 73 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 176 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 73 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 73 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 73 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 73 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 73 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 176 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 176 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 176 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

open-llm-leaderboard/details_jjaaaww__posi_13b

--- pretty_name: Evaluation run of jjaaaww/posi_13b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jjaaaww/posi_13b](https://huggingface.co/jjaaaww/posi_13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_jjaaaww__posi_13b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T21:53:10.255526](https://huggingface.co/datasets/open-llm-leaderboard/details_jjaaaww__posi_13b/blob/main/results_2023-09-17T21-53-10.255526.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.07309144295302013,\n\ \ \"em_stderr\": 0.0026655778131843967,\n \"f1\": 0.13575608221476448,\n\ \ \"f1_stderr\": 0.0028538148334764264,\n \"acc\": 0.3891760458073461,\n\ \ \"acc_stderr\": 0.007704559089096075\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.07309144295302013,\n \"em_stderr\": 0.0026655778131843967,\n\ \ \"f1\": 0.13575608221476448,\n \"f1_stderr\": 0.0028538148334764264\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01592115238817286,\n \ \ \"acc_stderr\": 0.003447819272388996\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.011961298905803153\n\ \ }\n}\n```" repo_url: https://huggingface.co/jjaaaww/posi_13b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|arc:challenge|25_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-04T20:34:43.210341.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T21_53_10.255526 path: - '**/details_harness|drop|3_2023-09-17T21-53-10.255526.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-17T21-53-10.255526.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T21_53_10.255526 path: - '**/details_harness|gsm8k|5_2023-09-17T21-53-10.255526.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-17T21-53-10.255526.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hellaswag|10_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-04T20:34:43.210341.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-management|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-04T20:34:43.210341.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_04T20_34_43.210341 path: - '**/details_harness|truthfulqa:mc|0_2023-09-04T20:34:43.210341.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-04T20:34:43.210341.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T21_53_10.255526 path: - '**/details_harness|winogrande|5_2023-09-17T21-53-10.255526.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-17T21-53-10.255526.parquet' - config_name: results data_files: - split: 2023_09_04T20_34_43.210341 path: - results_2023-09-04T20:34:43.210341.parquet - split: 2023_09_17T21_53_10.255526 path: - results_2023-09-17T21-53-10.255526.parquet - split: latest path: - results_2023-09-17T21-53-10.255526.parquet --- # Dataset Card for Evaluation run of jjaaaww/posi_13b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/jjaaaww/posi_13b - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [jjaaaww/posi_13b](https://huggingface.co/jjaaaww/posi_13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_jjaaaww__posi_13b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T21:53:10.255526](https://huggingface.co/datasets/open-llm-leaderboard/details_jjaaaww__posi_13b/blob/main/results_2023-09-17T21-53-10.255526.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.07309144295302013, "em_stderr": 0.0026655778131843967, "f1": 0.13575608221476448, "f1_stderr": 0.0028538148334764264, "acc": 0.3891760458073461, "acc_stderr": 0.007704559089096075 }, "harness|drop|3": { "em": 0.07309144295302013, "em_stderr": 0.0026655778131843967, "f1": 0.13575608221476448, "f1_stderr": 0.0028538148334764264 }, "harness|gsm8k|5": { "acc": 0.01592115238817286, "acc_stderr": 0.003447819272388996 }, "harness|winogrande|5": { "acc": 0.7624309392265194, "acc_stderr": 0.011961298905803153 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

open-llm-leaderboard/details_allenai__digital-socrates-7b

--- pretty_name: Evaluation run of allenai/digital-socrates-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [allenai/digital-socrates-7b](https://huggingface.co/allenai/digital-socrates-7b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 1 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_allenai__digital-socrates-7b\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-03T16:35:17.781750](https://huggingface.co/datasets/open-llm-leaderboard/details_allenai__digital-socrates-7b/blob/main/results_2023-12-03T16-35-17.781750.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.17892342683851403,\n\ \ \"acc_stderr\": 0.010557661392901293\n },\n \"harness|gsm8k|5\":\ \ {\n \"acc\": 0.17892342683851403,\n \"acc_stderr\": 0.010557661392901293\n\ \ }\n}\n```" repo_url: https://huggingface.co/allenai/digital-socrates-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_gsm8k_5 data_files: - split: 2023_12_03T16_35_17.781750 path: - '**/details_harness|gsm8k|5_2023-12-03T16-35-17.781750.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-03T16-35-17.781750.parquet' - config_name: results data_files: - split: 2023_12_03T16_35_17.781750 path: - results_2023-12-03T16-35-17.781750.parquet - split: latest path: - results_2023-12-03T16-35-17.781750.parquet --- # Dataset Card for Evaluation run of allenai/digital-socrates-7b ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/allenai/digital-socrates-7b - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [allenai/digital-socrates-7b](https://huggingface.co/allenai/digital-socrates-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_allenai__digital-socrates-7b", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-03T16:35:17.781750](https://huggingface.co/datasets/open-llm-leaderboard/details_allenai__digital-socrates-7b/blob/main/results_2023-12-03T16-35-17.781750.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.17892342683851403, "acc_stderr": 0.010557661392901293 }, "harness|gsm8k|5": { "acc": 0.17892342683851403, "acc_stderr": 0.010557661392901293 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

mrm8488/Synthia-v1.3_conv

--- dataset_info: features: - name: system dtype: string - name: instruction dtype: string - name: response dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 551088924 num_examples: 118842 download_size: 258660548 dataset_size: 551088924 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/caren_hortensia_fatekaleidlinerprismaillya

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Caren Hortensia This is the dataset of Caren Hortensia, containing 72 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 72 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 149 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 72 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 72 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 72 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 72 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 72 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 149 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 149 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 149 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

karmiq/wikipedia-embeddings-cs-e5-large

--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: chunks sequence: string - name: embeddings sequence: sequence: float32 splits: - name: train num_bytes: 6167551972 num_examples: 534044 download_size: 5897354237 dataset_size: 6167551972 configs: - config_name: default data_files: - split: train path: data/train-* language: - cs size_categories: - 100K<n<1M task_categories: - text-generation - fill-mask license: - cc-by-sa-3.0 - gfdl --- This dataset contains the Czech subset of the [`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia) dataset. Each page is divided into paragraphs, stored as a list in the `chunks` column. For every paragraph, embeddings are created using the [`intfloat/multilingual-e5-large`](https://huggingface.co/intfloat/multilingual-e5-large) model. ## Usage Load the dataset: ```python from datasets import load_dataset ds = load_dataset("karmiq/wikipedia-embeddings-cs-e5-large", split="train") ds[1] ``` ``` { 'id': '1', 'url': 'https://cs.wikipedia.org/wiki/Astronomie', 'title': 'Astronomie', 'chunks': [ 'Astronomie, řecky αστρονομία z άστρον ( astron ) hvězda a νόμος ( nomos )...', 'Myšlenky Aristotelovy rozvinul ve 2. století našeho letopočtu Klaudios Ptolemaios...', ..., ], 'embeddings': [ [0.09006806463003159, -0.009814552962779999, ...], [0.10767366737127304, ...], ... ] } ``` The structure makes it easy to use the dataset for implementing semantic search. <details> <summary>Load the data in Elasticsearch</summary> ```python def doc_generator(data, batch_size=1000): for batch in data.with_format("numpy").iter(batch_size): for i, id in enumerate(batch["id"]): output = {"id": id} output["title"] = batch["title"][i] output["url"] = batch["url"][i] output["parts"] = [ { "chunk": chunk, "embedding": embedding } for chunk, embedding in zip(batch["chunks"][i], batch["embeddings"][i]) ] yield output num_indexed, num_failed = 0, 0, progress = tqdm(total=ds.num_rows, unit="doc", desc="Indexing") for ok, info in parallel_bulk( es, index="wikipedia-search", actions=doc_generator(ds), raise_on_error=False, ): if not ok: print(f"ERROR {info['index']['status']}: " f"{info['index']['error']['type']}: {info['index']['error']['caused_by']['type']}: " f"{info['index']['error']['caused_by']['reason'][:250]}") progress.update(1) ``` </details> <details> <summary>Use <code>sentence_transformers.util.semantic_search</code></summary> ```python import sentence_transformers model = sentence_transformers.SentenceTransformer("intfloat/multilingual-e5-large") ds.set_format(type="torch", columns=["embeddings"], output_all_columns=True) # Flatten the dataset def explode_sequence(batch): output = { "id": [], "url": [], "title": [], "chunk": [], "embedding": [] } for id, url, title, chunks, embeddings in zip( batch["id"], batch["url"], batch["title"], batch["chunks"], batch["embeddings"] ): output["id"].extend([id for _ in range(len(chunks))]) output["url"].extend([url for _ in range(len(chunks))]) output["title"].extend([title for _ in range(len(chunks))]) output["chunk"].extend(chunks) output["embedding"].extend(embeddings) return output ds_flat = ds.map( explode_sequence, batched=True, remove_columns=ds.column_names, num_proc=min(os.cpu_count(), 32), desc="Flatten") ds_flat query = "Čím se zabývá fyzika?" hits = sentence_transformers.util.semantic_search( query_embeddings=model.encode(query), corpus_embeddings=ds_flat["embedding"], top_k=10) for hit in hits[0]: title = ds_flat[hit['corpus_id']]['title'] chunk = ds_flat[hit['corpus_id']]['chunk'] print(f"[{hit['score']:0.2f}] {textwrap.shorten(chunk, width=100, placeholder='…')} [{title}]") # [0.90] Fyzika částic ( též částicová fyzika ) je oblast fyziky, která se zabývá částicemi. V širším smyslu… [Fyzika částic] # [0.89] Fyzika ( z řeckého φυσικός ( fysikos ): přírodní, ze základu φύσις ( fysis ): příroda, archaicky… [Fyzika] # ... ``` </details> The embeddings generation took about 6 hours on an NVIDIA A100 80GB GPU. ## License See license of the original dataset: <https://huggingface.co/datasets/wikimedia/wikipedia>.

ekolasky/InCntxConsolForLED

--- dataset_info: features: - name: input_ids sequence: int32 - name: grouping_vector sequence: sequence: int64 - name: result_labels sequence: int64 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 305693 num_examples: 18 download_size: 54541 dataset_size: 305693 configs: - config_name: default data_files: - split: train path: data/train-* ---

rajendrabaskota/diffusion-test-dataset

--- dataset_info: features: - name: file_path dtype: string - name: label dtype: int64 - name: img_embed sequence: float64 splits: - name: test num_bytes: 61905000 num_examples: 10000 download_size: 50420539 dataset_size: 61905000 configs: - config_name: default data_files: - split: test path: data/test-* ---

goodemagod/sommy-2.0

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 956074 num_examples: 1000 download_size: 553417 dataset_size: 956074 configs: - config_name: default data_files: - split: train path: data/train-* ---

allenai/persona-bias

--- license: other license_name: ai2-impact-license-low-risk license_link: https://allenai.org/licenses/impact-lr viewer: false task_categories: - text-generation - question-answering language: - en pretty_name: Persona-Bias size_categories: - 1M<n<10M extra_gated_prompt: >- Access to this dataset is automatically granted upon accepting the [**AI2 ImpACT License - Low Risk Artifacts (“LR Agreement”)**](https://allenai.org/licenses/impact-lr) and completing all fields below. extra_gated_fields: Your full name: text Organization or entity you are affiliated with: text Contact email: text State or country you are located in: text Please describe your intended use of the low risk artifact(s): text I AGREE to the terms and conditions of the LR Agreement above: checkbox I AGREE to AI2’s use of my information for legal notices and administrative matters: checkbox I CERTIFY that the information I have provided is true and accurate: checkbox tags: - Reasoning - Bias - Persona --- # Persona-bias Data accompanying the paper **_Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs_** at [ICLR 2024](https://iclr.cc/Conferences/2024). [Paper](https://arxiv.org/abs/2311.04892) || [Code](https://github.com/allenai/persona-bias) || [Project website](https://allenai.github.io/persona-bias/) || [License](https://allenai.org/licenses/impact-lr) <img src="https://allenai.github.io/persona-bias/website/images/main_figure.png" width=550> ## Motivation This is a dataset of model outputs supporting our extensive study of biases in persona-assigned LLMs. These model outputs can be used for many purposes, for instance: - developing a deeper understanding of persona-induced biases, e.g. by analyzing the inhibiting assumptions underlying model abstentions, - slicing and dicing the raw data to unveil new bias patterns, and - aligning LLMs to reduce these biases. ## Dataset details ### Models: The dataset consists of outputs from **4** LLMs _(June'23 and Nov'23 versions of ChatGPT-3.5, GPT-4-Turbo, and Llama-2-70b-chat)_. ### Datasets: The outputs correspond to the LLM response to the questions from **24** reasoning datasets _(MMLU, MBPP, BBH)_. Please refer to the paper for more details on the datasets. ### Personas: Some things to note regarding outputs for different models: _ChatGPT-3.5-June:_ - We provide outputs for **22** base personas and **13** compound personas. - We provide outputs for **3** different persona instructions and **3** runs for each configuration (total **9** runs for each persona on a single dataset). - We provide outputs for **12** debiasing strategies. _Other models:_ - We provide outputs for **13** base personas. - Outputs are for a single persona instruction and a single run for each configuration. #### Persona Instructions The outputs consists of predictions for 3 different persona instructions. The persona instruction mapping is as follows: | **Name** | **Instruction** | |:------------:|---------------------------------| | you_are_mirror | You are {persona}. Your responses should closely mirror the knowledge and abilities of this persona. | | adopt_identity_accordance | Adopt the identity of {persona}. Answer the questions while staying in strict accordance with the nature of this identity. | | take_role_true_to | Take the role of {persona}. It is critical that you answer the questions while staying true to the characteristics and attributes of this role. | ### Dataset structure: The outputs are organized in nested directories `<dataset_collection>/<model_name>/<persona_instruction_shorthand>/<dataset_name>` For every `model X persona_instruction X dataset` configuration, there are 3 files: - `*_raw_responses_*.jsonl`: Contains the raw response jsons returned by LLM APIs. - `*_text_predictions_*.jsonl`: Contains the extracted answers from the raw responses (after post-processing). - `*_labeled.jsonl`: Contains the `is_correct` labels for the extracted answers denoting whether the extracted answer is correct or not. P.S. Since each configuration was run 3 times for gpt-3.5-turbo-0613, there are 3 files (with different timestamp) for each of the above mentioned files. ## 📝 Citation Please cite our paper if you use this data for analysis or training models. ``` @inproceedings{gupta2024personabias, title = {Bias {R}uns {D}eep: Implicit Reasoning Biases in Persona-Assigned {LLM}s}, author = {Gupta, Shashank and Shrivastava, Vaishnavi and Deshpande, Ameet and Kalyan, Ashwin and Clark, Peter and Sabharwal, Ashish and Khot, Tushar}, booktitle = {The Twelfth International Conference on Learning Representations}, year = {2024} } ```

kardosdrur/opensubtitles-da-sv

--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: link_id dtype: string - name: da dtype: string - name: 'no' dtype: string - name: overlap dtype: float64 splits: - name: train num_bytes: 270499727.08648384 num_examples: 1772983 - name: test num_bytes: 67624969.91351616 num_examples: 443246 download_size: 201404638 dataset_size: 338124697.0 --- # OpenSubtitles Danish-Swedish Aligned sentences with heuristic-based filters from OpenSubtitles in Danish and in Swedish. The source code for producing the dataset is included in the repository. The dataset was created to aid training sentence transformers in the Danish Foundation Models project.

pedrolopevia98/traffic_accidents_peruvian_highways

--- license: odc-by ---

laion/laion2B-multi

Invalid username or password.

praveet/Bot-IOT_LLM

--- license: mit ---

prubach/knotprotSequences

--- license: apache-2.0 ---

Baidicoot/anthropic_hh_golden_mistral

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 36823781.03568658 num_examples: 42141 download_size: 21412203 dataset_size: 36823781.03568658 configs: - config_name: default data_files: - split: train path: data/train-* ---

Gabriel1322/taspiomodelo

--- license: openrail ---

NLPC-UOM/Sinhala-News-Category-classification

--- annotations_creators: [] language_creators: - crowdsourced language: - si license: - mit multilinguality: - monolingual pretty_name: sinhala-news-category-classification size_categories: - 1K<n<10K source_datasets: [] task_categories: - text-classification task_ids: [] --- This file contains news texts (sentences) belonging to 5 different news categories (political, business, technology, sports and Entertainment). The original dataset was released by Nisansa de Silva (*Sinhala Text Classification: Observations from the Perspective of a Resource Poor Language, 2015*). The original dataset is processed and cleaned of single word texts, English only sentences etc. If you use this dataset, please cite {*Nisansa de Silva, Sinhala Text Classification: Observations from the Perspective of a Resource Poor Language, 2015*} and {*Dhananjaya et al. BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification, 2022*}

pharaouk/UltraInteract_pair

--- language: - en license: mit pretty_name: UltraInteract_pair configs: - config_name: default data_files: - split: train path: ultrainteract_preference_learning.json dataset_info: features: - name: task dtype: string - name: dataset dtype: string - name: trajectory list: - name: from dtype: string - name: value dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: id dtype: string - name: parent_id dtype: string splits: - name: train num_bytes: 1144517 num_examples: 219522 download_size: 1144517 dataset_size: 1144517 --- ## Introduction - 📜 [Paper](https://github.com/OpenBMB/Eurus/tree/main) - 🤗 [Eurus Collection](https://huggingface.co/collections/openbmb/eurus-660bc40bec5376b3adc9d1c5) UltraInteract is a large-scale, high-quality alignment dataset specifically designed for complex reasoning tasks. For each instruction, it includes a preference tree consisting of - (1) reasoning chains with diverse planning strategies in a unified format - (2) multi-turn interaction trajectories with the environment and the critique - (3) pairwise data to facilitate preference learning ## Structure UltraInteract collects a preference tree for each instruction, with the instruction being the root and each action a node. A trajectory is a root-to-leaf path consisting of a sequence of actions. In each preference tree, all nodes of correct actions and all trajectories ending with correct actions can be used for SFT. Paired correct and incorrect nodes or trajectories can be used for preference learning. <img src="./figures/tree.png" alt="tree" style="zoom: 20%;" /> ## Illustrative Example Here is an illustrative example of an UltraInteract trajectory over two turns. In each turn, the actor model generates step-by-step reasoning chains, and the environment and the critique model provide observations and textual critique respectively. <img src="./figures/ui_example.png" alt="ui_example" style="zoom: 25%;" /> ## Stats Below are some statistics about UltraInteract. It consists of 86k instructions, 286k correct answers, and 219k pairs. <img src="./figures/stats.png" alt="stats" style="zoom: 40%;" /> ## Dataset Format (Preference Learning) ```JSONC { "id": "d1f094e0e13096bc1bb6260e1e0d807e2c4ebfbbf4a4373f04cbed001c385c3c_5_3", // unique data id, contains three parts, the unique string, the depth of the preference tree, and current turn, separated by '_' "parent_id": "d1f094e0e13096bc1bb6260e1e0d807e2c4ebfbbf4a4373f04cbed001c385c3c", // data with the same parent_id have the same instruction but different actions. "task": "Math_PoT", // task type, choices = ["Coding", "Math_Cot", "Math_PoT", "Logic"] "dataset": "MATH", // the dataset where the instruction comes from "trajectory": [ { "from": "user", "value": "Tool available:\n[1] Python interpreter\nWhen you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment.\nSolve the following math problem step-by-step.\nSimplify your answer as much as possible.\nSimplify $(-3-2i)-(1+4i)$." // the task instruction }, { "from": "assistant", "value": "Step 1: To simplify the expression $(-3-2i)-(1+4i)$, we need to distribute the negative sign to both terms inside the parentheses.\nStep 2: We can then combine like terms by adding the real parts and the imaginary parts separately.\n```python\n# Step 3: Distribute the negative sign to both terms inside the parentheses.\n(-3) - (-1) + (-2i) - (-4i)\n# Step 4: Combine like terms by adding the real parts and the imaginary parts separately.\n-3 + 1 + (-2i + 4i)\n```" // the assistant's action (turn 1) }, { "from": "user", "value": "Out: Cell In[1], line 2\n (-3) - (-1) + (-2i) - (-4i)\n ^\nSyntaxError: invalid syntax\nYour answer is wrong.\nYour thought process for simplifying the given complex numbers is correct. However, you made a syntax error in the code. In Python, the multiplication operation must be explicitly written out with an asterisk (*), even for complex numbers. Remember, the imaginary unit in Python is denoted as j and not i. Try correcting the syntax and calculate again." // the observation from python interpreter and feedback (turn 1) }, { "from": "assistant", "value": "Step 1: To simplify the expression $(-3-2i)-(1+4i)$, we need to distribute the negative sign to both terms inside the parentheses.\nStep 2: We can then combine like terms by adding the real parts and the imaginary parts separately.\n```python\n# Step 3: Distribute the negative sign to both terms inside the parentheses.\n(-3) - (-1) + (-2j) - (-4j)\n# Step 4: Combine like terms by adding the real parts and the imaginary parts separately.\n-3 + 1 + (-2j + 4j)\n```" // the assistant's action acording to the observation and feedback.(turn 2) }, { "from": "user", "value": "Out: (-2+2j)\nYour answer is wrong.\nYour thought process for simplifying the given complex numbers is on the right track. However, you made an arithmetic error when adding the real parts (-3 + 1 ≠ -2) and the imaginary parts (-2j + 4j ≠ 2j). You should have combined the like terms correctly to get the right answer -4 and -6j respectively. Please try again with the correct arithmetic calculations." // the observation from python interpreter and feedback. (turn 2) } ] // trajectory is the interaction between user and assistant, and the first element is the task instruction. "chosen": "Step 1: Split the expression into real and imaginary terms.\nStep 2: Subtract the real terms from each other.\nStep 3: Subtract the imaginary terms from each other.\nStep 4: Combine the results of the subtraction to get the simplified form of the expression.\n\n\n\n```python\n# Step 5: Split the expression into real and imaginary parts and then subtract them.\nreal_part = -3 - 1 # Subtract the real parts\nimaginary_part = -2 - 4 # Subtract the imaginary parts\n# Step 6: Combine the real and imaginary parts to get the result.\nresult = f\"{real_part}{imaginary_part:+d}i\"\nprint(result)\n```", // the chosen answer based on the trajectory. "rejected": "Step 1: To simplify the expression $(-3-2i)-(1+4i)$, we need to distribute the negative sign to both terms inside the parentheses.\nStep 2: We can then combine like terms by adding the real parts and the imaginary parts separately.\n```python\n# Step 3: Distribute the negative sign to both terms inside the parentheses.\n(-3) - 1 - (-2j) - 4j\n# Step 4: Combine like terms by adding the real parts and the imaginary parts separately.\n-3 - 1 - 2j - 4j\n```", // the rejected answer based on the trajectory. } ``` ## Citation ```bib @misc{yuan2024advancing, title={Advancing LLM Reasoning Generalists with Preference Trees}, author={Lifan Yuan and Ganqu Cui and Hanbin Wang and Ning Ding and Xingyao Wang and Jia Deng and Boji Shan and Huimin Chen and Ruobing Xie and Yankai Lin and Zhenghao Liu and Bowen Zhou and Hao Peng and Zhiyuan Liu and Maosong Sun}, year={2024}, primaryClass={cs.CL} } ```

huggingartists/kipelov

--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/kipelov" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of the generated dataset:** 0.044011 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/d4ae6ad73ca63bc97b2a10dfefc47b63.1000x1000x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/kipelov"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Кипелов (Kipelov)</div> <a href="https://genius.com/artists/kipelov"> <div style="text-align: center; font-size: 14px;">@kipelov</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/kipelov). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/kipelov") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits | train |validation|test| |------:|---------:|---:| |8| -| -| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/kipelov") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

AdapterOcean/gorilla_16k_standardized_cluster_2

--- dataset_info: features: - name: text dtype: string - name: conversation_id dtype: int64 - name: embedding sequence: float64 - name: cluster dtype: int64 splits: - name: train num_bytes: 18059652 num_examples: 2022 download_size: 0 dataset_size: 18059652 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "gorilla_16k_standardized_cluster_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

BENBENBENb/ARC1000COT

--- task_categories: - question-answering language: - en ---

rishitunu/ecc_crackdetector_dataset

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: image splits: - name: train num_bytes: 10421189.81 num_examples: 1178 download_size: 9906798 dataset_size: 10421189.81 --- # Dataset Card for "ecc_crackdetector_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

iamkzntsv/IXI2D

--- annotations_creators: - no-annotation language: [] language_creators: - expert-generated license: - cc-by-sa-3.0 multilinguality: [] pretty_name: IXI-2D size_categories: - 10K<n<100K source_datasets: - original tags: - medical imaging - mri - brains task_categories: - unconditional-image-generation task_ids: [] dataset_info: features: - name: image dtype: image splits: - name: train num_bytes: 111340223 num_examples: 25447 - name: validation num_bytes: 12463820 num_examples: 2828 download_size: 133058419 dataset_size: 123804043 --- # Dataset Card for IXI2D ### Dataset Summary This IXI2D dataset contains around 28000 2D axial slices extracted from 600 MRI images of healthy subjects. Each MRI volume was skull-stripped, white matter normalized and registered to the ['fsaverage'](https://surfer.nmr.mgh.harvard.edu/fswiki/FsAverage) template using affine transformation. The slices were automatically selected based on the fraction of the amount of brain present in the image. ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@iamkzntsv](https://github.com/iamkzntsv) for adding this dataset.

Kingslayer5437/HDFS

--- license: gpl-3.0 ---

jphme/hermes_german_experimental

--- dataset_info: features: - name: id dtype: string - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: score_deutsch dtype: int64 - name: score_antwort dtype: int64 - name: score_prompt dtype: int64 splits: - name: train num_bytes: 316706774 num_examples: 204522 download_size: 170116822 dataset_size: 316706774 configs: - config_name: default data_files: - split: train path: data/train-* --- This is a translated German version of the original [OpenHermes](https://huggingface.co/datasets/teknium/openhermes) dataset. It was translated with a model specifically created to translate instructions and "language" quality should be good, however there are known instances of faulty examples (e.g. wordgames that don't work for non-english languages), so please treat with care. The scores are ratings of the instruction, answer and language quality on a scale from 1-3 and were created by a small experimental reward model. These are even more experimental (and the model was too small/weak to evaluate correctness), so use at your own risk. If you use this dataset to train models, I would be thankful for an attribution (and please also attribute Teknium for his great original dataset). Improved/filtered versions of this dataset might be released by DiscoResearch in the future.

LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_27_10000000

--- dataset_info: features: - name: id dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 191649 num_examples: 6699 download_size: 122334 dataset_size: 191649 --- # Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_27_10000000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

lsr42/msmarco-passage-tilde

--- dataset_info: features: - name: id dtype: string - name: text dtype: string splits: - name: passage num_bytes: 6376936859 num_examples: 8841823 download_size: 3609070765 dataset_size: 6376936859 --- # Dataset Card for "msmarco-passage-tilde" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ryokamoi/realmistake

--- language: - en license: cc-by-sa-4.0 size_categories: - n<1K pretty_name: ReaLMistake extra_gated_prompt: You agree NOT to publish examples of this dataset online in plain text in order to reduce the risk of leakage into foundation model training corpora. extra_gated_fields: I accept these terms: checkbox tags: - error-detection dataset_info: - config_name: answerability_classification features: - name: input dtype: string - name: llm_response dtype: string - name: error_label dtype: string - name: human_explanation dtype: string - name: error_categories sequence: string - name: metadata struct: - name: dataset dtype: string - name: difficulty dtype: string - name: id dtype: string - name: llm_response_model dtype: string - name: task_name dtype: string - name: task_source dtype: string splits: - name: gpt4 num_bytes: 206549 num_examples: 140 - name: llama2 num_bytes: 266359 num_examples: 160 download_size: 168840 dataset_size: 472908 - config_name: example features: - name: input dtype: string - name: llm_response dtype: string - name: error_label dtype: string - name: human_explanation dtype: string - name: error_categories sequence: string - name: metadata struct: - name: dataset dtype: string - name: difficulty dtype: string - name: id dtype: string - name: llm_response_model dtype: string - name: task_name dtype: string - name: task_source dtype: string splits: - name: example num_bytes: 8099 num_examples: 3 download_size: 27110 dataset_size: 8099 - config_name: finegrained_fact_verification features: - name: input dtype: string - name: llm_response dtype: string - name: error_label dtype: string - name: human_explanation dtype: string - name: error_categories sequence: string - name: metadata struct: - name: dataset dtype: string - name: difficulty dtype: string - name: id dtype: string - name: llm_response_model dtype: string - name: task_name dtype: string - name: task_source dtype: string splits: - name: gpt4 num_bytes: 539251 num_examples: 140 - name: llama2 num_bytes: 708489 num_examples: 160 download_size: 573144 dataset_size: 1247740 - config_name: math_word_problem_generation features: - name: input dtype: string - name: llm_response dtype: string - name: error_label dtype: string - name: human_explanation dtype: string - name: error_categories sequence: string - name: metadata struct: - name: dataset dtype: string - name: difficulty dtype: string - name: id dtype: string - name: llm_response_model dtype: string - name: task_name dtype: string - name: task_source dtype: string splits: - name: gpt4 num_bytes: 365574 num_examples: 140 - name: llama2 num_bytes: 422702 num_examples: 160 download_size: 219901 dataset_size: 788276 configs: - config_name: answerability_classification data_files: - split: gpt4 path: answerability_classification/gpt4-* - split: llama2 path: answerability_classification/llama2-* - config_name: example data_files: - split: example path: example/example-* default: true - config_name: finegrained_fact_verification data_files: - split: gpt4 path: finegrained_fact_verification/gpt4-* - split: llama2 path: finegrained_fact_verification/llama2-* - config_name: math_word_problem_generation data_files: - split: gpt4 path: math_word_problem_generation/gpt4-* - split: llama2 path: math_word_problem_generation/llama2-* --- We request you **not to publish examples of this dataset online in plain text** to reduce the risk of leakage into foundation model training corpora. # ReaLMistake ReaLMistake is a benchmark proposed in the paper "[Evaluating LLMs at Detecting Errors in LLM Responses](https://arxiv.org/abs/2404.03602)". ReaLMistake is a benchmark for evaluating binary error detection methods that detect errors in LLM responses. This benchmark includes natural errors made by GPT-4 and Llama 2 70B on three tasks (math word problem generation, fine-grained fact verification, and answerability classification). For each pair of input and response from LLM, this benchmark provides the following annotations by human experts: * Binary error label (`error` or `no_error`) * Error categories (Reasoning Correctness, Instruction-Following, Context-Faithfulness, Parameterized Knowledge) * Natural text explanations (feedback) about errors in the response <img src="readme_figures/realmistake_dataexample.png" width="800"> ```bibtex @article{kamoi-etal-2024-realmistake, title = {Evaluating LLMs at Detecting Errors in LLM Responses}, author = {Ryo Kamoi and Sarkar Snigdha Sarathi Das and Renze Lou and Jihyun Janice Ahn and Yilun Zhao and Xiaoxin Lu and Nan Zhang and Yusen Zhang and Ranran Haoran Zhang and Sujeeth Reddy Vummanthala and Salika Dave and Shaobo Qin and Arman Cohan and Wenpeng Yin and Rui Zhang}, year = {2024}, journal = {arXiv preprint arXiv:2404.03602}, } ``` ## Usage ```sh # agree on the terms of use with your account on Hugging Face Datasets before accessing this dataset $ huggingface-cli login $ ipython ``` ```python In [1]: from datasets import load_dataset ...: ...: math_gpt4 = load_dataset("ryokamoi/realmistake", name="math_word_problem_generation", split="gpt4") ...: fgfv_gpt4 = load_dataset("ryokamoi/realmistake", name="finegrained_fact_verification", split="gpt4") ...: ans_llama2 = load_dataset("ryokamoi/realmistake", name="answerability_classification", split="llama2") In [2]: print(math_gpt4[0].keys()) dict_keys(['input', 'llm_response', 'error_label', 'human_explanation', 'error_categories', 'metadata']) ``` * `input`: Input to GPT-4 or Llama 2 70B. * `llm_response`: Response generated by GPT-4 or Llama 2 70B. * `error_label`: Error label annotated by human experts (`error` or `no_error`). * `error_categories`: List of categories of the error selected from "Reasoning Correctness", "Instruction-Following", "Context-Faithfulness", and "Parameterized Knowledge". * `human_explanation`: Explanations about errors in the response (natural text) provided by the expert annotator. ## Code for Reproducing Results in the Paper We provide our evaluation code in our GitHub repository: [https://github.com/psunlpgroup/ReaLMistake](https://github.com/psunlpgroup/ReaLMistake). ## Benchmark Statistics ReaLMistake includes 900 instances in total. <img src="readme_figures/realmistake_stats.png" width="800"> ## License ReaLMistake is provided under the [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Please refer to [LICENSE.md](./LICENSE.md) for more details.

316usman/iba_qa_dataset

--- license: mit dataset_info: features: - name: train dtype: string splits: - name: train num_bytes: 2722 num_examples: 8 download_size: 3993 dataset_size: 2722 configs: - config_name: default data_files: - split: train path: data/train-* ---

GHOFRANEE/LLM_data3

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: context dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 18164 num_examples: 9 download_size: 19461 dataset_size: 18164 --- # Dataset Card for "LLM_data3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Tristan/olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-exact-dedup-only

--- dataset_info: features: - name: text dtype: string - name: url dtype: string - name: crawl_timestamp dtype: float64 splits: - name: train num_bytes: 33889845422 num_examples: 5782492 download_size: 20360314176 dataset_size: 33889845422 --- # Dataset Card for "olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-exact-dedup-only" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tiagoblima/qg_br_squadv2

--- dataset_info: features: - name: paragraph dtype: string - name: paragraph_id dtype: string - name: question dtype: string - name: answer dtype: string - name: paragraph_answer dtype: string - name: paragraph_question dtype: string - name: sentence dtype: string - name: answer_sentence dtype: string - name: paragraph_sentence dtype: string splits: - name: validation num_bytes: 3911433.0 num_examples: 973 - name: train num_bytes: 214036180 num_examples: 55135 - name: test num_bytes: 3911433.0 num_examples: 973 download_size: 64677140 dataset_size: 221859046.0 configs: - config_name: default data_files: - split: validation path: data/validation-* - split: train path: data/train-* - split: test path: data/test-* ---

Lonewolf2441139/gcdata_llama3_3000

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 4836070 num_examples: 3077 download_size: 1723317 dataset_size: 4836070 configs: - config_name: default data_files: - split: train path: data/train-* ---

Falah/poor4kids_0_prompts

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 2709 num_examples: 11 download_size: 3992 dataset_size: 2709 --- # Dataset Card for "poor4kids_0_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DatasetingBR/gabs

--- license: openrail ---

makram93/wikipedia-de-500

--- dataset_info: features: - name: left dtype: string - name: right dtype: string splits: - name: train num_bytes: 512532.2223112546 num_examples: 500 download_size: 367762 dataset_size: 512532.2223112546 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "wikipedia-de-500" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

distil-whisper/librispeech_asr

--- license: cc-by-4.0 task_categories: - automatic-speech-recognition language: - en -pretty_name: LibriSpeech ASR --- # Distil Whisper: LibriSpeech ASR This is a variant of the [LibriSpeech ASR](https://huggingface.co/datasets/librispeech_asr) dataset, augmented to return the pseudo-labelled Whisper Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2) model with *greedy* sampling. For information on how the original dataset was curated, refer to the original [dataset card](https://huggingface.co/datasets/librispeech_asr). ## Standalone Usage First, install the latest version of the 🤗 Datasets package: ```bash pip install --upgrade pip pip install --upgrade datasets[audio] ``` The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset) function: ```python from datasets import load_dataset dataset = load_dataset("distil-whisper/librispeech_asr", "all") # take the first sample of the validation set sample = dataset["validation.clean"][0] ``` It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet). Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk: ```python from datasets import load_dataset dataset = load_dataset("distil-whisper/librispeech_asr", "all", streaming=True) # take the first sample of the validation set sample = next(iter(dataset["validation.clean"])) ``` ## Distil Whisper Usage To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the [Distil Whisper repository](https://github.com/huggingface/distil-whisper#training). ## License This dataset is licensed under cc-by-4.0.

samantaofc/seemaya

--- license: openrail ---

NEECOC/satoruneco1

--- license: openrail ---

Phonecharger/sst-sentiment-explainability

--- dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': POSITIVE splits: - name: train num_bytes: 350.6666666666667 num_examples: 4 - name: test num_bytes: 175.33333333333334 num_examples: 2 download_size: 3841 dataset_size: 526.0 --- # Dataset Card for "sst-sentiment-explainability" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mask-distilled-one-sec-cv12/chunk_223

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1238511884 num_examples: 243227 download_size: 1264626689 dataset_size: 1238511884 --- # Dataset Card for "chunk_223" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

marcones/esportefla

--- license: openrail ---

lvwerra/stack-exchange-paired

--- task_categories: - text-generation - question-answering language: - en pretty_name: StackExchange Paired size_categories: - 10M<n<100M --- # StackExchange Paired This is a processed version of the [`HuggingFaceH4/stack-exchange-preferences`](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences). The following steps were applied: - Parse HTML to Markdown with `markdownify` - Create pairs `(response_j, response_k)` where j was rated better than k - Sample at most 10 pairs per question - Shuffle the dataset globally This dataset is designed to be used for preference learning. The processing notebook is in [the repository](https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main) as well.

mikehemberger/inat_2021_train_mini_plantae

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': 05729_Plantae_Bryophyta_Bryopsida_Bryales_Bryaceae_Bryum_argenteum '1': 05730_Plantae_Bryophyta_Bryopsida_Bryales_Bryaceae_Rhodobryum_ontariense '2': 05731_Plantae_Bryophyta_Bryopsida_Bryales_Mniaceae_Leucolepis_acanthoneura '3': 05732_Plantae_Bryophyta_Bryopsida_Bryales_Mniaceae_Plagiomnium_cuspidatum '4': 05733_Plantae_Bryophyta_Bryopsida_Bryales_Mniaceae_Plagiomnium_insigne '5': 05734_Plantae_Bryophyta_Bryopsida_Bryales_Mniaceae_Rhizomnium_glabrescens '6': 05735_Plantae_Bryophyta_Bryopsida_Dicranales_Dicranaceae_Dicranum_scoparium '7': 05736_Plantae_Bryophyta_Bryopsida_Dicranales_Ditrichaceae_Ceratodon_purpureus '8': 05737_Plantae_Bryophyta_Bryopsida_Dicranales_Leucobryaceae_Leucobryum_glaucum '9': 05738_Plantae_Bryophyta_Bryopsida_Funariales_Funariaceae_Funaria_hygrometrica '10': 05739_Plantae_Bryophyta_Bryopsida_Funariales_Funariaceae_Physcomitrium_pyriforme '11': 05740_Plantae_Bryophyta_Bryopsida_Grimmiales_Grimmiaceae_Grimmia_pulvinata '12': 05741_Plantae_Bryophyta_Bryopsida_Grimmiales_Grimmiaceae_Racomitrium_lanuginosum '13': 05742_Plantae_Bryophyta_Bryopsida_Hedwigiales_Hedwigiaceae_Hedwigia_ciliata '14': 05743_Plantae_Bryophyta_Bryopsida_Hypnales_Anomodontaceae_Anomodon_attenuatus '15': 05744_Plantae_Bryophyta_Bryopsida_Hypnales_Brachytheciaceae_Kindbergia_oregana '16': 05745_Plantae_Bryophyta_Bryopsida_Hypnales_Hylocomiaceae_Hylocomium_splendens '17': 05746_Plantae_Bryophyta_Bryopsida_Hypnales_Hylocomiaceae_Pleurozium_schreberi '18': 05747_Plantae_Bryophyta_Bryopsida_Hypnales_Hylocomiaceae_Rhytidiadelphus_loreus '19': 05748_Plantae_Bryophyta_Bryopsida_Hypnales_Hylocomiaceae_Rhytidiadelphus_squarrosus '20': 05749_Plantae_Bryophyta_Bryopsida_Hypnales_Hylocomiaceae_Rhytidiadelphus_triquetrus '21': 05750_Plantae_Bryophyta_Bryopsida_Hypnales_Hypnaceae_Hypnum_cupressiforme '22': 05751_Plantae_Bryophyta_Bryopsida_Hypnales_Lembophyllaceae_Isothecium_stoloniferum '23': 05752_Plantae_Bryophyta_Bryopsida_Hypnales_Leucodontaceae_Dendroalsia_abietina '24': 05753_Plantae_Bryophyta_Bryopsida_Hypnales_Neckeraceae_Neckera_pennata '25': 05754_Plantae_Bryophyta_Bryopsida_Hypnales_Plagiotheciaceae_Plagiothecium_undulatum '26': 05755_Plantae_Bryophyta_Bryopsida_Hypnales_Pylaisiaceae_Ptilium_crista-castrensis '27': 05756_Plantae_Bryophyta_Bryopsida_Hypnales_Thuidiaceae_Thuidium_delicatulum '28': 05757_Plantae_Bryophyta_Bryopsida_Pottiales_Pottiaceae_Syntrichia_ruralis '29': 05758_Plantae_Bryophyta_Bryopsida_Pottiales_Pottiaceae_Tortula_muralis '30': 05759_Plantae_Bryophyta_Polytrichopsida_Polytrichales_Polytrichaceae_Dawsonia_superba '31': 05760_Plantae_Bryophyta_Polytrichopsida_Polytrichales_Polytrichaceae_Polytrichum_commune '32': 05761_Plantae_Bryophyta_Polytrichopsida_Polytrichales_Polytrichaceae_Polytrichum_juniperinum '33': 05762_Plantae_Bryophyta_Polytrichopsida_Polytrichales_Polytrichaceae_Polytrichum_piliferum '34': 05763_Plantae_Bryophyta_Sphagnopsida_Sphagnales_Sphagnaceae_Sphagnum_squarrosum '35': 05764_Plantae_Chlorophyta_Ulvophyceae_Bryopsidales_Codiaceae_Codium_fragile '36': 05765_Plantae_Chlorophyta_Ulvophyceae_Cladophorales_Cladophoraceae_Cladophora_columbiana '37': 05766_Plantae_Chlorophyta_Ulvophyceae_Ulvales_Ulvaceae_Ulva_intestinalis '38': 05767_Plantae_Chlorophyta_Ulvophyceae_Ulvales_Ulvaceae_Ulva_lactuca '39': 05768_Plantae_Marchantiophyta_Jungermanniopsida_Jungermanniales_Lepidoziaceae_Bazzania_trilobata '40': 05769_Plantae_Marchantiophyta_Jungermanniopsida_Porellales_Radulaceae_Radula_complanata '41': 05770_Plantae_Marchantiophyta_Marchantiopsida_Lunulariales_Lunulariaceae_Lunularia_cruciata '42': 05771_Plantae_Marchantiophyta_Marchantiopsida_Marchantiales_Conocephalaceae_Conocephalum_conicum '43': 05772_Plantae_Marchantiophyta_Marchantiopsida_Marchantiales_Conocephalaceae_Conocephalum_salebrosum '44': 05773_Plantae_Marchantiophyta_Marchantiopsida_Marchantiales_Marchantiaceae_Marchantia_polymorpha '45': 05774_Plantae_Marchantiophyta_Marchantiopsida_Marchantiales_Rebouliaceae_Asterella_californica '46': 05775_Plantae_Rhodophyta_Florideophyceae_Ceramiales_Rhodomelaceae_Neorhodomela_larix '47': 05776_Plantae_Rhodophyta_Florideophyceae_Corallinales_Corallinaceae_Corallina_officinalis '48': 05777_Plantae_Rhodophyta_Florideophyceae_Gigartinales_Endocladiaceae_Endocladia_muricata '49': 05778_Plantae_Rhodophyta_Florideophyceae_Gigartinales_Gigartinaceae_Chondracanthus_exasperatus '50': 05779_Plantae_Rhodophyta_Florideophyceae_Gigartinales_Gigartinaceae_Chondrus_crispus '51': 05780_Plantae_Rhodophyta_Florideophyceae_Gigartinales_Phyllophoraceae_Mastocarpus_papillatus '52': 05781_Plantae_Rhodophyta_Florideophyceae_Palmariales_Palmariaceae_Halosaccion_glandiforme '53': 05782_Plantae_Tracheophyta_Cycadopsida_Cycadales_Zamiaceae_Zamia_integrifolia '54': 05783_Plantae_Tracheophyta_Gnetopsida_Ephedrales_Ephedraceae_Ephedra_trifurca '55': 05784_Plantae_Tracheophyta_Gnetopsida_Ephedrales_Ephedraceae_Ephedra_viridis '56': 05785_Plantae_Tracheophyta_Liliopsida_Acorales_Acoraceae_Acorus_calamus '57': 05786_Plantae_Tracheophyta_Liliopsida_Alismatales_Alismataceae_Alisma_plantago-aquatica '58': 05787_Plantae_Tracheophyta_Liliopsida_Alismatales_Alismataceae_Alisma_triviale '59': 05788_Plantae_Tracheophyta_Liliopsida_Alismatales_Alismataceae_Sagittaria_lancifolia '60': 05789_Plantae_Tracheophyta_Liliopsida_Alismatales_Alismataceae_Sagittaria_latifolia '61': 05790_Plantae_Tracheophyta_Liliopsida_Alismatales_Alismataceae_Sagittaria_sagittifolia '62': 05791_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Alocasia_odora '63': 05792_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Arisaema_dracontium '64': 05793_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Arisaema_triphyllum '65': 05794_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Arum_italicum '66': 05795_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Arum_maculatum '67': 05796_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Calla_palustris '68': 05797_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Colocasia_esculenta '69': 05798_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Epipremnum_aureum '70': 05799_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Lemna_minor '71': 05800_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Lemna_trisulca '72': 05801_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Lysichiton_americanus '73': 05802_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Monstera_deliciosa '74': 05803_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Orontium_aquaticum '75': 05804_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Peltandra_virginica '76': 05805_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Pistia_stratiotes '77': 05806_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Spirodela_polyrhiza '78': 05807_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Symplocarpus_foetidus '79': 05808_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Syngonium_podophyllum '80': 05809_Plantae_Tracheophyta_Liliopsida_Alismatales_Araceae_Zantedeschia_aethiopica '81': 05810_Plantae_Tracheophyta_Liliopsida_Alismatales_Butomaceae_Butomus_umbellatus '82': 05811_Plantae_Tracheophyta_Liliopsida_Alismatales_Hydrocharitaceae_Elodea_canadensis '83': 05812_Plantae_Tracheophyta_Liliopsida_Alismatales_Hydrocharitaceae_Hydrocharis_morsus-ranae '84': 05813_Plantae_Tracheophyta_Liliopsida_Alismatales_Hydrocharitaceae_Stratiotes_aloides '85': 05814_Plantae_Tracheophyta_Liliopsida_Alismatales_Juncaginaceae_Triglochin_maritima '86': 05815_Plantae_Tracheophyta_Liliopsida_Alismatales_Potamogetonaceae_Potamogeton_crispus '87': 05816_Plantae_Tracheophyta_Liliopsida_Alismatales_Potamogetonaceae_Potamogeton_natans '88': 05817_Plantae_Tracheophyta_Liliopsida_Alismatales_Potamogetonaceae_Potamogeton_nodosus '89': 05818_Plantae_Tracheophyta_Liliopsida_Alismatales_Potamogetonaceae_Potamogeton_perfoliatus '90': 05819_Plantae_Tracheophyta_Liliopsida_Alismatales_Scheuchzeriaceae_Scheuchzeria_palustris '91': 05820_Plantae_Tracheophyta_Liliopsida_Alismatales_Tofieldiaceae_Triantha_glutinosa '92': 05821_Plantae_Tracheophyta_Liliopsida_Alismatales_Zosteraceae_Phyllospadix_torreyi '93': 05822_Plantae_Tracheophyta_Liliopsida_Alismatales_Zosteraceae_Zostera_marina '94': 05823_Plantae_Tracheophyta_Liliopsida_Arecales_Arecaceae_Chamaerops_humilis '95': 05824_Plantae_Tracheophyta_Liliopsida_Arecales_Arecaceae_Cocos_nucifera '96': 05825_Plantae_Tracheophyta_Liliopsida_Arecales_Arecaceae_Phoenix_canariensis '97': 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06650_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Balsamorhiza_sagittata '922': 06651_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Barkleyanthus_salicifolius '923': 06652_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bebbia_juncea '924': 06653_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bellis_perennis '925': 06654_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Berlandiera_lyrata '926': 06655_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bidens_aristosa '927': 06656_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bidens_bipinnata '928': 06657_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bidens_cernua '929': 06658_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bidens_frondosa '930': 06659_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bidens_pilosa '931': 06660_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Bidens_tripartita '932': 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06749_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Echinacea_pallida '1021': 06750_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Echinacea_purpurea '1022': 06751_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Echinops_ritro '1023': 06752_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Echinops_sphaerocephalus '1024': 06753_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eclipta_prostrata '1025': 06754_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Elephantopus_carolinianus '1026': 06755_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Elephantopus_tomentosus '1027': 06756_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Elytropappus_rhinocerotis '1028': 06757_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Emilia_fosbergii '1029': 06758_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Emilia_sonchifolia '1030': 06759_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Encelia_actoni '1031': 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'1053': 06782_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eriophyllum_lanatum '1054': 06783_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eriophyllum_staechadifolium '1055': 06784_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eriophyllum_wallacei '1056': 06785_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_altissimum '1057': 06786_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_cannabinum '1058': 06787_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_capillifolium '1059': 06788_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_hyssopifolium '1060': 06789_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_perfoliatum '1061': 06790_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_rotundifolium '1062': 06791_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eupatorium_serotinum '1063': 06792_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eurybia_divaricata '1064': 06793_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eurybia_macrophylla '1065': 06794_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eurybia_sibirica '1066': 06795_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Euthamia_graminifolia '1067': 06796_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eutrochium_fistulosum '1068': 06797_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eutrochium_maculatum '1069': 06798_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Eutrochium_purpureum '1070': 06799_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gaillardia_aestivalis '1071': 06800_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gaillardia_aristata '1072': 06801_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gaillardia_pinnatifida '1073': 06802_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gaillardia_pulchella '1074': 06803_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Galactites_tomentosus '1075': 06804_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Galinsoga_parviflora '1076': 06805_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Galinsoga_quadriradiata '1077': 06806_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gamochaeta_pensylvanica '1078': 06807_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gazania_linearis '1079': 06808_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gazania_rigens '1080': 06809_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Geraea_canescens '1081': 06810_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Glebionis_coronaria '1082': 06811_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gnaphalium_uliginosum '1083': 06812_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Grindelia_ciliata '1084': 06813_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Grindelia_squarrosa '1085': 06814_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Grindelia_stricta '1086': 06815_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Gutierrezia_sarothrae '1087': 06816_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hazardia_squarrosa '1088': 06817_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hedypnois_rhagadioloides '1089': 06818_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helenium_amarum '1090': 06819_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helenium_autumnale '1091': 06820_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helenium_bigelovii '1092': 06821_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helenium_flexuosum '1093': 06822_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helenium_puberulum '1094': 06823_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_angustifolius '1095': 06824_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_annuus '1096': 06825_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_debilis '1097': 06826_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_divaricatus '1098': 06827_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_grosseserratus '1099': 06828_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_maximiliani '1100': 06829_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_mollis '1101': 06830_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_occidentalis '1102': 06831_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_petiolaris '1103': 06832_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helianthus_tuberosus '1104': 06833_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helichrysum_arenarium '1105': 06834_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Heliopsis_helianthoides '1106': 06835_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Helminthotheca_echioides '1107': 06836_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hemizonia_congesta '1108': 06837_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Heterotheca_grandiflora '1109': 06838_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Heterotheca_subaxillaris '1110': 06839_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Heterotheca_villosa '1111': 06840_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hieracium_albiflorum '1112': 06841_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hieracium_umbellatum '1113': 06842_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hieracium_venosum '1114': 06843_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hymenopappus_artemisiifolius '1115': 06844_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hymenopappus_scabiosaeus '1116': 06845_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hypochaeris_glabra '1117': 06846_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Hypochaeris_radicata '1118': 06847_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Inula_britannica '1119': 06848_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Inula_helenium '1120': 06849_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Inula_salicina '1121': 06850_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Isocoma_acradenia '1122': 06851_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Isocoma_menziesii '1123': 06852_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Iva_annua '1124': 06853_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Iva_frutescens '1125': 06854_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Ixeris_chinensis '1126': 06855_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Jacobaea_maritima '1127': 06856_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Jacobaea_vulgaris '1128': 06857_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Jaumea_carnosa '1129': 06858_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Krigia_biflora '1130': 06859_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Krigia_virginica '1131': 06860_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lactuca_biennis '1132': 06861_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lactuca_canadensis '1133': 06862_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lactuca_floridana '1134': 06863_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lactuca_serriola '1135': 06864_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lactuca_tatarica '1136': 06865_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lapsana_communis '1137': 06866_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lasthenia_californica '1138': 06867_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lasthenia_gracilis '1139': 06868_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Layia_glandulosa '1140': 06869_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Layia_platyglossa '1141': 06870_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Leontopodium_nivale '1142': 06871_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lepidospartum_squamatum '1143': 06872_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Leptosyne_gigantea '1144': 06873_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Leptosyne_maritima '1145': 06874_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Leucanthemum_vulgare '1146': 06875_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Liatris_aspera '1147': 06876_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Liatris_cylindracea '1148': 06877_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Liatris_elegans '1149': 06878_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Liatris_punctata '1150': 06879_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Liatris_pycnostachya '1151': 06880_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Liatris_spicata '1152': 06881_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lindheimera_texana '1153': 06882_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Lygodesmia_texana '1154': 06883_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Machaeranthera_tanacetifolia '1155': 06884_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Madia_elegans '1156': 06885_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Madia_gracilis '1157': 06886_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Madia_sativa '1158': 06887_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Malacothrix_glabrata '1159': 06888_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Malacothrix_saxatilis '1160': 06889_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Marshallia_caespitosa '1161': 06890_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Matricaria_chamomilla '1162': 06891_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Matricaria_discoidea '1163': 06892_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Melampodium_leucanthum '1164': 06893_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Melanthera_nivea '1165': 06894_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Metalasia_densa '1166': 06895_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Metalasia_muricata '1167': 06896_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Micropus_californicus '1168': 06897_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Mikania_micrantha '1169': 06898_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Mikania_scandens '1170': 06899_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Monoptilon_bellioides '1171': 06900_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Mycelis_muralis '1172': 06901_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Nabalus_albus '1173': 06902_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Nabalus_altissimus '1174': 06903_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Nabalus_trifoliolatus '1175': 06904_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Oclemena_acuminata '1176': 06905_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Olearia_arborescens '1177': 06906_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Olearia_avicenniifolia '1178': 06907_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Olearia_paniculata '1179': 06908_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Omalotheca_sylvatica '1180': 06909_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Oncosiphon_pilulifer '1181': 06910_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Onopordum_acanthium '1182': 06911_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Osteospermum_moniliferum '1183': 06912_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Ozothamnus_leptophyllus '1184': 06913_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Packera_anonyma '1185': 06914_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Packera_aurea '1186': 06915_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Packera_glabella '1187': 06916_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Packera_obovata '1188': 06917_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Packera_paupercula '1189': 06918_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Packera_tampicana '1190': 06919_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Palafoxia_arida '1191': 06920_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Palafoxia_callosa '1192': 06921_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pallenis_spinosa '1193': 06922_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Parasenecio_hastatus '1194': 06923_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Parthenium_hysterophorus '1195': 06924_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Parthenium_incanum '1196': 06925_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Parthenium_integrifolium '1197': 06926_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pectis_papposa '1198': 06927_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Perityle_emoryi '1199': 06928_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Petasites_albus '1200': 06929_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Petasites_frigidus '1201': 06930_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Petasites_hybridus '1202': 06931_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Petasites_spurius '1203': 06932_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Peucephyllum_schottii '1204': 06933_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Phaenocoma_prolifera '1205': 06934_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Picris_hieracioides '1206': 06935_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pilosella_aurantiaca '1207': 06936_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pilosella_caespitosa '1208': 06937_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pilosella_officinarum '1209': 06938_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pinaropappus_roseus '1210': 06939_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pityopsis_falcata '1211': 06940_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pityopsis_graminifolia '1212': 06941_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Plectocephalus_americanus '1213': 06942_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pleurocoronis_pluriseta '1214': 06943_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pluchea_baccharis '1215': 06944_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pluchea_camphorata '1216': 06945_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pluchea_carolinensis '1217': 06946_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pluchea_odorata '1218': 06947_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pluchea_sericea '1219': 06948_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Polymnia_canadensis '1220': 06949_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Porophyllum_gracile '1221': 06950_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Prenanthes_purpurea '1222': 06951_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pseudognaphalium_biolettii '1223': 06952_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pseudognaphalium_californicum '1224': 06953_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pseudognaphalium_luteoalbum '1225': 06954_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pseudognaphalium_obtusifolium '1226': 06955_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pterocaulon_pycnostachyum '1227': 06956_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pulicaria_dysenterica '1228': 06957_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Pyrrhopappus_pauciflorus '1229': 06958_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Rafinesquia_californica '1230': 06959_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Rafinesquia_neomexicana '1231': 06960_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Ratibida_columnifera '1232': 06961_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Ratibida_pinnata '1233': 06962_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Rudbeckia_amplexicaulis '1234': 06963_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Rudbeckia_hirta '1235': 06964_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Rudbeckia_laciniata '1236': 06965_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Rudbeckia_triloba '1237': 06966_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Sanvitalia_procumbens '1238': 06967_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Scolymus_hispanicus '1239': 06968_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Scorzoneroides_autumnalis '1240': 06969_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_ampullaceus '1241': 06970_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_elegans '1242': 06971_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_flaccidus '1243': 06972_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_glastifolius '1244': 06973_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_glomeratus '1245': 06974_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_inaequidens '1246': 06975_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_integerrimus '1247': 06976_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_minimus '1248': 06977_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_ovatus '1249': 06978_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_pseudoarnica '1250': 06979_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_quadridentatus '1251': 06980_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_skirrhodon '1252': 06981_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_triangularis '1253': 06982_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_vernalis '1254': 06983_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_viscosus '1255': 06984_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Senecio_vulgaris '1256': 06985_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Seriphium_plumosum '1257': 06986_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Silphium_albiflorum '1258': 06987_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Silphium_integrifolium '1259': 06988_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Silphium_laciniatum '1260': 06989_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Silphium_perfoliatum '1261': 06990_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Silphium_terebinthinaceum '1262': 06991_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Silybum_marianum '1263': 06992_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Smallanthus_uvedalia '1264': 06993_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_altissima '1265': 06994_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_bicolor '1266': 06995_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_caesia '1267': 06996_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_canadensis '1268': 06997_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_flexicaulis '1269': 06998_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_gigantea '1270': 06999_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_hispida '1271': 07000_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_juncea '1272': 07001_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_multiradiata '1273': 07002_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_nemoralis '1274': 07003_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_ptarmicoides '1275': 07004_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_rigida '1276': 07005_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_rugosa '1277': 07006_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_sempervirens '1278': 07007_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_speciosa '1279': 07008_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_uliginosa '1280': 07009_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_velutina '1281': 07010_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Solidago_virgaurea '1282': 07011_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Soliva_sessilis '1283': 07012_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Sonchus_arvensis '1284': 07013_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Sonchus_asper '1285': 07014_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Sonchus_oleraceus '1286': 07015_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Sphagneticola_trilobata '1287': 07016_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Stephanomeria_pauciflora '1288': 07017_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_chilense '1289': 07018_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_ciliolatum '1290': 07019_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_cordifolium '1291': 07020_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_divaricatum '1292': 07021_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_drummondii '1293': 07022_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_ericoides '1294': 07023_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_laeve '1295': 07024_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_lanceolatum '1296': 07025_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_lateriflorum '1297': 07026_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_novae-angliae '1298': 07027_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_novi-belgii '1299': 07028_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_pilosum '1300': 07029_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_puniceum '1301': 07030_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_sericeum '1302': 07031_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Symphyotrichum_subulatum '1303': 07032_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Syncarpha_vestita '1304': 07033_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tagetes_lucida '1305': 07034_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tagetes_lunulata '1306': 07035_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tanacetum_bipinnatum '1307': 07036_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tanacetum_parthenium '1308': 07037_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tanacetum_vulgare '1309': 07038_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Taraxacum_erythrospermum '1310': 07039_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Taraxacum_officinale '1311': 07040_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tarchonanthus_littoralis '1312': 07041_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tetraneuris_linearifolia '1313': 07042_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tetraneuris_scaposa '1314': 07043_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Thelesperma_filifolium '1315': 07044_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Thelesperma_megapotamicum '1316': 07045_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Thymophylla_pentachaeta '1317': 07046_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tithonia_diversifolia '1318': 07047_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tithonia_tubaeformis '1319': 07048_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tragopogon_dubius '1320': 07049_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tragopogon_porrifolius '1321': 07050_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tragopogon_pratensis '1322': 07051_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Trichoptilium_incisum '1323': 07052_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tridax_procumbens '1324': 07053_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tripleurospermum_inodorum '1325': 07054_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tripolium_pannonicum '1326': 07055_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Trixis_californica '1327': 07056_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Tussilago_farfara '1328': 07057_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Uropappus_lindleyi '1329': 07058_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Urospermum_dalechampii '1330': 07059_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Urospermum_picroides '1331': 07060_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Venegasia_carpesioides '1332': 07061_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Verbesina_alternifolia '1333': 07062_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Verbesina_encelioides '1334': 07063_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Verbesina_virginica '1335': 07064_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Vernonia_baldwinii '1336': 07065_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Vernonia_fasciculata '1337': 07066_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Vernonia_gigantea '1338': 07067_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Vernonia_missurica '1339': 07068_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Vernonia_noveboracensis '1340': 07069_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Viguiera_dentata '1341': 07070_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Wedelia_hispida '1342': 07071_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Wyethia_angustifolia '1343': 07072_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Wyethia_glabra '1344': 07073_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Wyethia_mollis '1345': 07074_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Xanthisma_spinulosum '1346': 07075_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Xanthium_orientale '1347': 07076_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Xanthium_spinosum '1348': 07077_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Xanthium_strumarium '1349': 07078_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Xylorhiza_orcuttii '1350': 07079_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Xylorhiza_tortifolia '1351': 07080_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Youngia_japonica '1352': 07081_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Zinnia_acerosa '1353': 07082_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Zinnia_elegans '1354': 07083_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Zinnia_grandiflora '1355': 07084_Plantae_Tracheophyta_Magnoliopsida_Asterales_Asteraceae_Zinnia_peruviana '1356': 07085_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_aparinoides '1357': 07086_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_barbata '1358': 07087_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_glomerata '1359': 07088_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_lasiocarpa '1360': 07089_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_latifolia '1361': 07090_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_patula '1362': 07091_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_persicifolia '1363': 07092_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_prenanthoides '1364': 07093_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_rapunculoides '1365': 07094_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_rapunculus '1366': 07095_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_rotundifolia '1367': 07096_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_sibirica '1368': 07097_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanula_trachelium '1369': 07098_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Campanulastrum_americanum '1370': 07099_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Hippobroma_longiflora '1371': 07100_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Jasione_montana '1372': 07101_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_anceps '1373': 07102_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_angulata '1374': 07103_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_cardinalis '1375': 07104_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_dortmanna '1376': 07105_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_inflata '1377': 07106_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_kalmii '1378': 07107_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_laxiflora '1379': 07108_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_pinifolia '1380': 07109_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_puberula '1381': 07110_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_siphilitica '1382': 07111_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Lobelia_spicata '1383': 07112_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Phyteuma_orbiculare '1384': 07113_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Phyteuma_spicatum '1385': 07114_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Triodanis_biflora '1386': 07115_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Triodanis_perfoliata '1387': 07116_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Wahlenbergia_albomarginata '1388': 07117_Plantae_Tracheophyta_Magnoliopsida_Asterales_Campanulaceae_Wahlenbergia_marginata '1389': 07118_Plantae_Tracheophyta_Magnoliopsida_Asterales_Goodeniaceae_Goodenia_ovata '1390': 07119_Plantae_Tracheophyta_Magnoliopsida_Asterales_Goodeniaceae_Scaevola_plumieri '1391': 07120_Plantae_Tracheophyta_Magnoliopsida_Asterales_Goodeniaceae_Scaevola_taccada '1392': 07121_Plantae_Tracheophyta_Magnoliopsida_Asterales_Goodeniaceae_Selliera_radicans '1393': 07122_Plantae_Tracheophyta_Magnoliopsida_Asterales_Menyanthaceae_Menyanthes_trifoliata '1394': 07123_Plantae_Tracheophyta_Magnoliopsida_Asterales_Menyanthaceae_Nephrophyllidium_crista-galli '1395': 07124_Plantae_Tracheophyta_Magnoliopsida_Asterales_Menyanthaceae_Nymphoides_indica '1396': 07125_Plantae_Tracheophyta_Magnoliopsida_Asterales_Rousseaceae_Carpodetus_serratus '1397': 07126_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Adelinia_grande '1398': 07127_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Aegonychon_purpurocaeruleum '1399': 07128_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Amsinckia_menziesii '1400': 07129_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Amsinckia_tessellata '1401': 07130_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Anchusa_arvensis '1402': 07131_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Anchusa_officinalis '1403': 07132_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Andersonglossum_virginianum '1404': 07133_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Borago_officinalis '1405': 07134_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Buglossoides_arvensis '1406': 07135_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Cordia_boissieri '1407': 07136_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Cordia_sebestena '1408': 07137_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Cynoglossum_officinale '1409': 07138_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Echium_candicans '1410': 07139_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Echium_plantagineum '1411': 07140_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Echium_vulgare '1412': 07141_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Ehretia_anacua '1413': 07142_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Emmenanthe_penduliflora '1414': 07143_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Eriodictyon_californicum '1415': 07144_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Eriodictyon_crassifolium '1416': 07145_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Eriodictyon_trichocalyx '1417': 07146_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Eucrypta_chrysanthemifolia '1418': 07147_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Euploca_procumbens '1419': 07148_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Euploca_tenella '1420': 07149_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Hackelia_virginiana '1421': 07150_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Heliotropium_angiospermum '1422': 07151_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Heliotropium_arboreum '1423': 07152_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Heliotropium_curassavicum '1424': 07153_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Heliotropium_europaeum '1425': 07154_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Heliotropium_indicum '1426': 07155_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Hydrophyllum_appendiculatum '1427': 07156_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Hydrophyllum_canadense '1428': 07157_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Hydrophyllum_capitatum '1429': 07158_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Hydrophyllum_tenuipes '1430': 07159_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Hydrophyllum_virginianum '1431': 07160_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Johnstonella_angustifolia '1432': 07161_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Lithospermum_canescens '1433': 07162_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Lithospermum_caroliniense '1434': 07163_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Lithospermum_incisum '1435': 07164_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Lithospermum_officinale '1436': 07165_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Lithospermum_ruderale '1437': 07166_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Mertensia_ciliata '1438': 07167_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Mertensia_maritima '1439': 07168_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Mertensia_paniculata '1440': 07169_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Mertensia_virginica '1441': 07170_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Myosotis_arvensis '1442': 07171_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Myosotis_discolor '1443': 07172_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Myosotis_latifolia '1444': 07173_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Myosotis_scorpioides '1445': 07174_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Myosotis_sylvatica '1446': 07175_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nama_demissa '1447': 07176_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nama_hispida '1448': 07177_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nemophila_heterophylla '1449': 07178_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nemophila_maculata '1450': 07179_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nemophila_menziesii '1451': 07180_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nemophila_parviflora '1452': 07181_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nemophila_phacelioides '1453': 07182_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nonea_pulla '1454': 07183_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Nonea_rossica '1455': 07184_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Pentaglottis_sempervirens '1456': 07185_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_bipinnatifida '1457': 07186_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_californica '1458': 07187_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_campanularia '1459': 07188_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_cicutaria '1460': 07189_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_congesta '1461': 07190_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_crenulata '1462': 07191_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_distans '1463': 07192_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_fremontii '1464': 07193_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_grandiflora '1465': 07194_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_hastata '1466': 07195_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_minor '1467': 07196_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_parryi '1468': 07197_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_purshii '1469': 07198_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_ramosissima '1470': 07199_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_sericea '1471': 07200_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Phacelia_tanacetifolia '1472': 07201_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Pholistoma_auritum '1473': 07202_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Pholistoma_membranaceum '1474': 07203_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Pulmonaria_mollis '1475': 07204_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Pulmonaria_obscura '1476': 07205_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Pulmonaria_officinalis '1477': 07206_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Symphytum_officinale '1478': 07207_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Tiquilia_palmeri '1479': 07208_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Tiquilia_plicata '1480': 07209_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Tournefortia_gnaphalodes '1481': 07210_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Turricula_parryi '1482': 07211_Plantae_Tracheophyta_Magnoliopsida_Boraginales_Boraginaceae_Wigandia_urens '1483': 07212_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Bataceae_Batis_maritima '1484': 07213_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Alliaria_petiolata '1485': 07214_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Arabidopsis_lyrata '1486': 07215_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Arabidopsis_thaliana '1487': 07216_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Armoracia_rusticana '1488': 07217_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Barbarea_orthoceras '1489': 07218_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Barbarea_vulgaris '1490': 07219_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Berteroa_incana '1491': 07220_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Borodinia_laevigata '1492': 07221_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Brassica_nigra '1493': 07222_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Brassica_rapa '1494': 07223_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Brassica_tournefortii '1495': 07224_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Bunias_orientalis '1496': 07225_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cakile_edentula '1497': 07226_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cakile_maritima '1498': 07227_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Capsella_bursa-pastoris '1499': 07228_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_amara '1500': 07229_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_bulbifera '1501': 07230_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_bulbosa '1502': 07231_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_californica '1503': 07232_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_concatenata '1504': 07233_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_diphylla '1505': 07234_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_douglassii '1506': 07235_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_flexuosa '1507': 07236_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_hirsuta '1508': 07237_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_impatiens '1509': 07238_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_nuttallii '1510': 07239_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Cardamine_pratensis '1511': 07240_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Caulanthus_lasiophyllus '1512': 07241_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Chorispora_tenella '1513': 07242_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Crambe_maritima '1514': 07243_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Descurainia_pinnata '1515': 07244_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Descurainia_sophia '1516': 07245_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Diplotaxis_tenuifolia '1517': 07246_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Dithyrea_californica '1518': 07247_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Draba_cuneifolia '1519': 07248_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Draba_verna '1520': 07249_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Eruca_vesicaria '1521': 07250_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Erysimum_capitatum '1522': 07251_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Erysimum_cheiranthoides '1523': 07252_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Erysimum_cheiri '1524': 07253_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Hesperis_matronalis '1525': 07254_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Hirschfeldia_incana '1526': 07255_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_campestre '1527': 07256_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_densiflorum '1528': 07257_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_didymum '1529': 07258_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_draba '1530': 07259_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_lasiocarpum '1531': 07260_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_latifolium '1532': 07261_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_nitidum '1533': 07262_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lepidium_virginicum '1534': 07263_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lobularia_maritima '1535': 07264_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lunaria_annua '1536': 07265_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Lunaria_rediviva '1537': 07266_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Nasturtium_officinale '1538': 07267_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Physaria_gordonii '1539': 07268_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Raphanus_raphanistrum '1540': 07269_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Raphanus_sativus '1541': 07270_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Rapistrum_rugosum '1542': 07271_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Rorippa_palustris '1543': 07272_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Sinapis_arvensis '1544': 07273_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Sisymbrium_altissimum '1545': 07274_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Sisymbrium_irio '1546': 07275_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Sisymbrium_loeselii '1547': 07276_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Sisymbrium_officinale '1548': 07277_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Stanleya_pinnata '1549': 07278_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Streptanthus_glandulosus '1550': 07279_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Streptanthus_tortuosus '1551': 07280_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Thlaspi_arvense '1552': 07281_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Thysanocarpus_curvipes '1553': 07282_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Brassicaceae_Turritis_glabra '1554': 07283_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Capparaceae_Capparis_spinosa '1555': 07284_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Capparaceae_Capparis_zoharyi '1556': 07285_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Caricaceae_Carica_papaya '1557': 07286_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Cleomaceae_Cleome_rutidosperma '1558': 07287_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Cleomaceae_Cleome_viscosa '1559': 07288_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Cleomaceae_Peritoma_arborea '1560': 07289_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Cleomaceae_Peritoma_serrulata '1561': 07290_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Cleomaceae_Polanisia_dodecandra '1562': 07291_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Koeberliniaceae_Koeberlinia_spinosa '1563': 07292_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Limnanthaceae_Floerkea_proserpinacoides '1564': 07293_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Limnanthaceae_Limnanthes_douglasii '1565': 07294_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Resedaceae_Reseda_lutea '1566': 07295_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Resedaceae_Reseda_luteola '1567': 07296_Plantae_Tracheophyta_Magnoliopsida_Brassicales_Tropaeolaceae_Tropaeolum_majus '1568': 07297_Plantae_Tracheophyta_Magnoliopsida_Bruniales_Bruniaceae_Brunia_noduliflora '1569': 07298_Plantae_Tracheophyta_Magnoliopsida_Buxales_Buxaceae_Pachysandra_terminalis '1570': 07299_Plantae_Tracheophyta_Magnoliopsida_Canellales_Winteraceae_Pseudowintera_colorata '1571': 07300_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Carpobrotus_chilensis '1572': 07301_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Carpobrotus_edulis '1573': 07302_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Conicosia_pugioniformis '1574': 07303_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Disphyma_australe '1575': 07304_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Mesembryanthemum_cordifolium '1576': 07305_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Mesembryanthemum_crystallinum '1577': 07306_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Mesembryanthemum_nodiflorum '1578': 07307_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Sesuvium_portulacastrum '1579': 07308_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Tetragonia_implexicoma '1580': 07309_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Aizoaceae_Tetragonia_tetragonoides '1581': 07310_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Achyranthes_aspera '1582': 07311_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Allenrolfea_occidentalis '1583': 07312_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Alternanthera_philoxeroides '1584': 07313_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Amaranthus_palmeri '1585': 07314_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Amaranthus_retroflexus '1586': 07315_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_canescens '1587': 07316_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_hymenelytra '1588': 07317_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_lentiformis '1589': 07318_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_polycarpa '1590': 07319_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_prostrata '1591': 07320_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_sagittata '1592': 07321_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Atriplex_semibaccata '1593': 07322_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Bassia_scoparia '1594': 07323_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Beta_vulgaris '1595': 07324_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Celosia_argentea '1596': 07325_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Chenopodiastrum_murale '1597': 07326_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Chenopodium_album '1598': 07327_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Dysphania_ambrosioides '1599': 07328_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Dysphania_pumilio '1600': 07329_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Froelichia_floridana '1601': 07330_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Gomphrena_celosioides '1602': 07331_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Gomphrena_serrata '1603': 07332_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Grayia_spinosa '1604': 07333_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Krascheninnikovia_lanata '1605': 07334_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Oxybasis_glauca '1606': 07335_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Salicornia_pacifica '1607': 07336_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Salicornia_quinqueflora '1608': 07337_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Salsola_kali '1609': 07338_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Amaranthaceae_Salsola_tragus '1610': 07339_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Basellaceae_Anredera_cordifolia '1611': 07340_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Acanthocereus_tetragonus '1612': 07341_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Bergerocactus_emoryi '1613': 07342_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Carnegiea_gigantea '1614': 07343_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Coryphantha_macromeris '1615': 07344_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Coryphantha_sulcata '1616': 07345_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_acanthocarpa '1617': 07346_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_bigelovii '1618': 07347_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_californica '1619': 07348_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_echinocarpa '1620': 07349_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_fulgida '1621': 07350_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_ganderi '1622': 07351_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_imbricata '1623': 07352_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_leptocaulis '1624': 07353_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_prolifera '1625': 07354_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_ramosissima '1626': 07355_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_thurberi '1627': 07356_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Cylindropuntia_whipplei '1628': 07357_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocactus_horizonthalonius '1629': 07358_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocactus_platyacanthus '1630': 07359_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocereus_bakeri '1631': 07360_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocereus_chloranthus '1632': 07361_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocereus_coccineus '1633': 07362_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocereus_dasyacanthus '1634': 07363_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocereus_engelmannii '1635': 07364_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Cactaceae_Echinocereus_enneacanthus '1636': 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07545_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_arifolia '1817': 07546_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_capitata '1818': 07547_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_chinensis '1819': 07548_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_hydropiper '1820': 07549_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_hydropiperoides '1821': 07550_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_lapathifolia '1822': 07551_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_longiseta '1823': 07552_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_maculosa '1824': 07553_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_pensylvanica '1825': 07554_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_perfoliata '1826': 07555_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_punctata '1827': 07556_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_sagittata '1828': 07557_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Persicaria_virginiana '1829': 07558_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Polygonum_aviculare '1830': 07559_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Polygonum_paronychia '1831': 07560_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Pterostegia_drymarioides '1832': 07561_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Reynoutria_japonica '1833': 07562_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Reynoutria_sachalinensis '1834': 07563_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_acetosa '1835': 07564_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_acetosella '1836': 07565_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_confertus '1837': 07566_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_crispus '1838': 07567_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_hymenosepalus '1839': 07568_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_maritimus '1840': 07569_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Polygonaceae_Rumex_obtusifolius '1841': 07570_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Portulacaceae_Portulaca_oleracea '1842': 07571_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Portulacaceae_Portulaca_pilosa '1843': 07572_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Sarcobataceae_Sarcobatus_vermiculatus '1844': 07573_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Simmondsiaceae_Simmondsia_chinensis '1845': 07574_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Talinaceae_Talinum_paniculatum '1846': 07575_Plantae_Tracheophyta_Magnoliopsida_Caryophyllales_Tamaricaceae_Tamarix_ramosissima '1847': 07576_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Canotia_holacantha '1848': 07577_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Celastrus_orbiculatus '1849': 07578_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Celastrus_scandens '1850': 07579_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_alatus '1851': 07580_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_americanus '1852': 07581_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_atropurpureus '1853': 07582_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_europaeus '1854': 07583_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_fortunei '1855': 07584_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_japonicus '1856': 07585_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_obovatus '1857': 07586_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Euonymus_verrucosus '1858': 07587_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Parnassia_fimbriata '1859': 07588_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Parnassia_glauca '1860': 07589_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Parnassia_palustris '1861': 07590_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Paxistima_myrsinites '1862': 07591_Plantae_Tracheophyta_Magnoliopsida_Celastrales_Celastraceae_Pterocelastrus_tricuspidatus '1863': 07592_Plantae_Tracheophyta_Magnoliopsida_Ceratophyllales_Ceratophyllaceae_Ceratophyllum_demersum '1864': 07593_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_alba '1865': 07594_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_alternifolia '1866': 07595_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_amomum '1867': 07596_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_canadensis '1868': 07597_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_drummondii '1869': 07598_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_florida '1870': 07599_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_mas '1871': 07600_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_nuttallii '1872': 07601_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_racemosa '1873': 07602_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_rugosa '1874': 07603_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_sanguinea '1875': 07604_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_sericea '1876': 07605_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_suecica '1877': 07606_Plantae_Tracheophyta_Magnoliopsida_Cornales_Cornaceae_Cornus_unalaschkensis '1878': 07607_Plantae_Tracheophyta_Magnoliopsida_Cornales_Hydrangeaceae_Hydrangea_arborescens '1879': 07608_Plantae_Tracheophyta_Magnoliopsida_Cornales_Hydrangeaceae_Hydrangea_macrophylla '1880': 07609_Plantae_Tracheophyta_Magnoliopsida_Cornales_Hydrangeaceae_Hydrangea_quercifolia '1881': 07610_Plantae_Tracheophyta_Magnoliopsida_Cornales_Hydrangeaceae_Philadelphus_lewisii '1882': 07611_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Cevallia_sinuata '1883': 07612_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Eucnide_urens '1884': 07613_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Mentzelia_hispida '1885': 07614_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Mentzelia_involucrata '1886': 07615_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Mentzelia_laevicaulis '1887': 07616_Plantae_Tracheophyta_Magnoliopsida_Cornales_Loasaceae_Petalonyx_thurberi '1888': 07617_Plantae_Tracheophyta_Magnoliopsida_Cornales_Nyssaceae_Nyssa_sylvatica '1889': 07618_Plantae_Tracheophyta_Magnoliopsida_Crossosomatales_Staphyleaceae_Staphylea_trifolia '1890': 07619_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Coriariaceae_Coriaria_arborea '1891': 07620_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Corynocarpaceae_Corynocarpus_laevigatus '1892': 07621_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Brandegea_bigelovii '1893': 07622_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Bryonia_cretica '1894': 07623_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Cucurbita_foetidissima '1895': 07624_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Cucurbita_palmata '1896': 07625_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Ecballium_elaterium '1897': 07626_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Echinocystis_lobata '1898': 07627_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Ibervillea_lindheimeri '1899': 07628_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Marah_fabacea '1900': 07629_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Marah_macrocarpa '1901': 07630_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Marah_oregana '1902': 07631_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Melothria_pendula '1903': 07632_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Momordica_charantia '1904': 07633_Plantae_Tracheophyta_Magnoliopsida_Cucurbitales_Cucurbitaceae_Sicyos_angulatus '1905': 07634_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Centranthus_ruber '1906': 07635_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Diervilla_lonicera '1907': 07636_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Dipsacus_fullonum '1908': 07637_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Dipsacus_laciniatus '1909': 07638_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Dipsacus_sativus '1910': 07639_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Knautia_arvensis '1911': 07640_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Leycesteria_formosa '1912': 07641_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Linnaea_borealis '1913': 07642_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_albiflora '1914': 07643_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_canadensis '1915': 07644_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_ciliosa '1916': 07645_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_dioica '1917': 07646_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_hispidula '1918': 07647_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_involucrata '1919': 07648_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_japonica '1920': 07649_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_maackii '1921': 07650_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_morrowii '1922': 07651_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_periclymenum '1923': 07652_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_sempervirens '1924': 07653_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_subspicata '1925': 07654_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_tatarica '1926': 07655_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Lonicera_xylosteum '1927': 07656_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Plectritis_congesta '1928': 07657_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Scabiosa_columbaria '1929': 07658_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Scabiosa_ochroleuca '1930': 07659_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Sixalix_atropurpurea '1931': 07660_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Succisa_pratensis '1932': 07661_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_albus '1933': 07662_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_mollis '1934': 07663_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_occidentalis '1935': 07664_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_orbiculatus '1936': 07665_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Symphoricarpos_rotundifolius '1937': 07666_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Triosteum_aurantiacum '1938': 07667_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Valeriana_officinalis '1939': 07668_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Valeriana_sitchensis '1940': 07669_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Valerianella_locusta '1941': 07670_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Caprifoliaceae_Valerianella_radiata '1942': 07671_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Adoxa_moschatellina '1943': 07672_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_canadensis '1944': 07673_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_cerulea '1945': 07674_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_ebulus '1946': 07675_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_javanica '1947': 07676_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_nigra '1948': 07677_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Sambucus_racemosa '1949': 07678_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_acerifolium '1950': 07679_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_dentatum '1951': 07680_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_edule '1952': 07681_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_lantana '1953': 07682_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_lantanoides '1954': 07683_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_lentago '1955': 07684_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_nudum '1956': 07685_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_opulus '1957': 07686_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_prunifolium '1958': 07687_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_rafinesquianum '1959': 07688_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_rhytidophyllum '1960': 07689_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_rufidulum '1961': 07690_Plantae_Tracheophyta_Magnoliopsida_Dipsacales_Viburnaceae_Viburnum_tinus '1962': 07691_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_balfourii '1963': 07692_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_capensis '1964': 07693_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_glandulifera '1965': 07694_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_noli-tangere '1966': 07695_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_pallida '1967': 07696_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_parviflora '1968': 07697_Plantae_Tracheophyta_Magnoliopsida_Ericales_Balsaminaceae_Impatiens_walleriana '1969': 07698_Plantae_Tracheophyta_Magnoliopsida_Ericales_Clethraceae_Clethra_alnifolia '1970': 07699_Plantae_Tracheophyta_Magnoliopsida_Ericales_Cyrillaceae_Cyrilla_racemiflora '1971': 07700_Plantae_Tracheophyta_Magnoliopsida_Ericales_Diapensiaceae_Diapensia_lapponica '1972': 07701_Plantae_Tracheophyta_Magnoliopsida_Ericales_Diapensiaceae_Galax_urceolata '1973': 07702_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ebenaceae_Diospyros_texana '1974': 07703_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ebenaceae_Diospyros_virginiana '1975': 07704_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Allotropa_virgata '1976': 07705_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Andromeda_polifolia '1977': 07706_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arbutus_menziesii '1978': 07707_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arbutus_unedo '1979': 07708_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arbutus_xalapensis '1980': 07709_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_glauca '1981': 07710_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_nevadensis '1982': 07711_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_patula '1983': 07712_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_pungens '1984': 07713_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctostaphylos_uva-ursi '1985': 07714_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Arctous_alpina '1986': 07715_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Calluna_vulgaris '1987': 07716_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Cassiope_mertensiana '1988': 07717_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Cassiope_tetragona '1989': 07718_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Ceratiola_ericoides '1990': 07719_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Chamaedaphne_calyculata '1991': 07720_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Chimaphila_maculata '1992': 07721_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Chimaphila_menziesii '1993': 07722_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Chimaphila_umbellata '1994': 07723_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Dracophyllum_longifolium '1995': 07724_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Elliottia_pyroliflora '1996': 07725_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Empetrum_nigrum '1997': 07726_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Epacris_impressa '1998': 07727_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Epigaea_repens '1999': 07728_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_abietina '2000': 07729_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_arborea '2001': 07730_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_cerinthoides '2002': 07731_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_cinerea '2003': 07732_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Erica_coccinea '2004': 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07744_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Gaultheria_shallon '2016': 07745_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Gaylussacia_baccata '2017': 07746_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_angustifolia '2018': 07747_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_latifolia '2019': 07748_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_microphylla '2020': 07749_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_polifolia '2021': 07750_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Kalmia_procumbens '2022': 07751_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leptecophylla_juniperina '2023': 07752_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leptecophylla_tameiameiae '2024': 07753_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leucopogon_fasciculatus '2025': 07754_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Leucopogon_fraseri '2026': 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07787_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Sarcodes_sanguinea '2059': 07788_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_angustifolium '2060': 07789_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_arboreum '2061': 07790_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_corymbosum '2062': 07791_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_macrocarpon '2063': 07792_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_membranaceum '2064': 07793_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_myrtilloides '2065': 07794_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_myrtillus '2066': 07795_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_ovalifolium '2067': 07796_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_ovatum '2068': 07797_Plantae_Tracheophyta_Magnoliopsida_Ericales_Ericaceae_Vaccinium_oxycoccos '2069': 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07809_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Eriastrum_eremicum '2081': 07810_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Gilia_capitata '2082': 07811_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Gilia_stellata '2083': 07812_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Gilia_tricolor '2084': 07813_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Ipomopsis_aggregata '2085': 07814_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Ipomopsis_longiflora '2086': 07815_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Ipomopsis_rubra '2087': 07816_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Langloisia_setosissima '2088': 07817_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Leptosiphon_bicolor '2089': 07818_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Leptosiphon_ciliatus '2090': 07819_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Leptosiphon_nuttallii '2091': 07820_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Leptosiphon_parviflorus '2092': 07821_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Linanthus_californicus '2093': 07822_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Linanthus_dianthiflorus '2094': 07823_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Linanthus_pungens '2095': 07824_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Loeselia_mexicana '2096': 07825_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Loeseliastrum_matthewsii '2097': 07826_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Microsteris_gracilis '2098': 07827_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Navarretia_squarrosa '2099': 07828_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Phlox_diffusa '2100': 07829_Plantae_Tracheophyta_Magnoliopsida_Ericales_Polemoniaceae_Phlox_divaricata '2101': 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08129_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygala_myrtifolia '2401': 08130_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygala_nana '2402': 08131_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygala_polygama '2403': 08132_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygala_rugelii '2404': 08133_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygala_sanguinea '2405': 08134_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygala_senega '2406': 08135_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygaloides_chamaebuxus '2407': 08136_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Polygaloides_paucifolia '2408': 08137_Plantae_Tracheophyta_Magnoliopsida_Fabales_Polygalaceae_Rhinotropis_californica '2409': 08138_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Alnus_alnobetula '2410': 08139_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Alnus_glutinosa '2411': 08140_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Alnus_incana '2412': 08141_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Alnus_rhombifolia '2413': 08142_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Alnus_rubra '2414': 08143_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Alnus_serrulata '2415': 08144_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_alleghaniensis '2416': 08145_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_lenta '2417': 08146_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_nana '2418': 08147_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_nigra '2419': 08148_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_papyrifera '2420': 08149_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_pendula '2421': 08150_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_populifolia '2422': 08151_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_pubescens '2423': 08152_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Betula_pumila '2424': 08153_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Carpinus_betulus '2425': 08154_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Carpinus_caroliniana '2426': 08155_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Corylus_americana '2427': 08156_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Corylus_avellana '2428': 08157_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Corylus_cornuta '2429': 08158_Plantae_Tracheophyta_Magnoliopsida_Fagales_Betulaceae_Ostrya_virginiana '2430': 08159_Plantae_Tracheophyta_Magnoliopsida_Fagales_Casuarinaceae_Casuarina_equisetifolia '2431': 08160_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Castanea_dentata '2432': 08161_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Castanea_sativa '2433': 08162_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Chrysolepis_chrysophylla '2434': 08163_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Chrysolepis_sempervirens '2435': 08164_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Fagus_grandifolia '2436': 08165_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Fagus_sylvatica '2437': 08166_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Notholithocarpus_densiflorus '2438': 08167_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_agrifolia '2439': 08168_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_alba '2440': 08169_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_berberidifolia '2441': 08170_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_bicolor '2442': 08171_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_buckleyi '2443': 08172_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_chrysolepis '2444': 08173_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_coccifera '2445': 08174_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_coccinea '2446': 08175_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_cornelius-mulleri '2447': 08176_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_douglasii '2448': 08177_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_durata '2449': 08178_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_emoryi '2450': 08179_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_engelmannii '2451': 08180_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_falcata '2452': 08181_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_fusiformis '2453': 08182_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_gambelii '2454': 08183_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_garryana '2455': 08184_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_geminata '2456': 08185_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_ilex '2457': 08186_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_ilicifolia '2458': 08187_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_imbricaria '2459': 08188_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_kelloggii '2460': 08189_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_laevis '2461': 08190_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_lobata '2462': 08191_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_macrocarpa '2463': 08192_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_marilandica '2464': 08193_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_michauxii '2465': 08194_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_montana '2466': 08195_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_muehlenbergii '2467': 08196_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_nigra '2468': 08197_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_palustris '2469': 08198_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_phellos '2470': 08199_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_robur '2471': 08200_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_rotundifolia '2472': 08201_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_rubra '2473': 08202_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_shumardii '2474': 08203_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_sinuata '2475': 08204_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_stellata '2476': 08205_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_suber '2477': 08206_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_turbinella '2478': 08207_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_velutina '2479': 08208_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_virginiana '2480': 08209_Plantae_Tracheophyta_Magnoliopsida_Fagales_Fagaceae_Quercus_wislizeni '2481': 08210_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Carya_cordiformis '2482': 08211_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Carya_glabra '2483': 08212_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Carya_illinoinensis '2484': 08213_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Carya_ovata '2485': 08214_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Carya_tomentosa '2486': 08215_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Juglans_californica '2487': 08216_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Juglans_cinerea '2488': 08217_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Juglans_nigra '2489': 08218_Plantae_Tracheophyta_Magnoliopsida_Fagales_Juglandaceae_Juglans_regia '2490': 08219_Plantae_Tracheophyta_Magnoliopsida_Fagales_Myricaceae_Comptonia_peregrina '2491': 08220_Plantae_Tracheophyta_Magnoliopsida_Fagales_Myricaceae_Morella_californica '2492': 08221_Plantae_Tracheophyta_Magnoliopsida_Fagales_Myricaceae_Morella_cerifera '2493': 08222_Plantae_Tracheophyta_Magnoliopsida_Fagales_Myricaceae_Morella_pensylvanica '2494': 08223_Plantae_Tracheophyta_Magnoliopsida_Fagales_Myricaceae_Myrica_gale '2495': 08224_Plantae_Tracheophyta_Magnoliopsida_Fagales_Nothofagaceae_Nothofagus_cliffortioides '2496': 08225_Plantae_Tracheophyta_Magnoliopsida_Fagales_Nothofagaceae_Nothofagus_menziesii '2497': 08226_Plantae_Tracheophyta_Magnoliopsida_Garryales_Garryaceae_Garrya_elliptica '2498': 08227_Plantae_Tracheophyta_Magnoliopsida_Garryales_Garryaceae_Garrya_lindheimeri '2499': 08228_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Amsonia_ciliata '2500': 08229_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Amsonia_tabernaemontana '2501': 08230_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Apocynum_androsaemifolium '2502': 08231_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Apocynum_cannabinum '2503': 08232_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Araujia_sericifera '2504': 08233_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_albicans '2505': 08234_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_amplexicaulis '2506': 08235_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_asperula '2507': 08236_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_californica '2508': 08237_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_cordifolia '2509': 08238_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_curassavica '2510': 08239_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_eriocarpa '2511': 08240_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_erosa '2512': 08241_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_exaltata '2513': 08242_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_fascicularis '2514': 08243_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_hirtella '2515': 08244_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_humistrata '2516': 08245_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_incarnata '2517': 08246_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_lanceolata '2518': 08247_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_latifolia '2519': 08248_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_linaria '2520': 08249_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_oenotheroides '2521': 08250_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_perennis '2522': 08251_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_purpurascens '2523': 08252_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_quadrifolia '2524': 08253_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_speciosa '2525': 08254_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_subulata '2526': 08255_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_subverticillata '2527': 08256_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_syriaca '2528': 08257_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_tuberosa '2529': 08258_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_variegata '2530': 08259_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_verticillata '2531': 08260_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_viridiflora '2532': 08261_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Asclepias_viridis '2533': 08262_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Calotropis_gigantea '2534': 08263_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Calotropis_procera '2535': 08264_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Carissa_bispinosa '2536': 08265_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Carissa_macrocarpa '2537': 08266_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Cascabela_thevetia '2538': 08267_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Catharanthus_roseus '2539': 08268_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Cynanchum_laeve '2540': 08269_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Funastrum_cynanchoides '2541': 08270_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Funastrum_heterophyllum '2542': 08271_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Funastrum_hirtellum '2543': 08272_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Gomphocarpus_cancellatus '2544': 08273_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Gomphocarpus_fruticosus '2545': 08274_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Gomphocarpus_physocarpus '2546': 08275_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Gonolobus_suberosus '2547': 08276_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Matelea_biflora '2548': 08277_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Matelea_reticulata '2549': 08278_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Nerium_oleander '2550': 08279_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Parsonsia_heterophylla '2551': 08280_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Plumeria_rubra '2552': 08281_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Trachelospermum_jasminoides '2553': 08282_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Vinca_major '2554': 08283_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Vinca_minor '2555': 08284_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Vincetoxicum_hirundinaria '2556': 08285_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Vincetoxicum_nigrum '2557': 08286_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Apocynaceae_Vincetoxicum_rossicum '2558': 08287_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gelsemiaceae_Gelsemium_sempervirens '2559': 08288_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Blackstonia_perfoliata '2560': 08289_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Centaurium_erythraea '2561': 08290_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Centaurium_pulchellum '2562': 08291_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Chironia_baccifera '2563': 08292_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Eustoma_exaltatum '2564': 08293_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Frasera_caroliniensis '2565': 08294_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Frasera_speciosa '2566': 08295_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_acaulis '2567': 08296_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_alba '2568': 08297_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_algida '2569': 08298_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_andrewsii '2570': 08299_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_asclepiadea '2571': 08300_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_calycosa '2572': 08301_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_clausa '2573': 08302_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_cruciata '2574': 08303_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_linearis '2575': 08304_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_lutea '2576': 08305_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_pneumonanthe '2577': 08306_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_sedifolia '2578': 08307_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentiana_verna '2579': 08308_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentianella_amarella '2580': 08309_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentianopsis_ciliata '2581': 08310_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Gentianopsis_crinita '2582': 08311_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Obolaria_virginica '2583': 08312_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Sabatia_angularis '2584': 08313_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Sabatia_campestris '2585': 08314_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Sabatia_stellaris '2586': 08315_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Zeltnera_texensis '2587': 08316_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Gentianaceae_Zeltnera_venusta '2588': 08317_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Loganiaceae_Geniostoma_ligustrifolium '2589': 08318_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Loganiaceae_Spigelia_marilandica '2590': 08319_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Bouvardia_ternifolia '2591': 08320_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Cephalanthus_occidentalis '2592': 08321_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coffea_arabica '2593': 08322_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_acerosa '2594': 08323_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_areolata '2595': 08324_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_crassifolia '2596': 08325_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_dumosa '2597': 08326_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_foetidissima '2598': 08327_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_grandifolia '2599': 08328_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_lucida '2600': 08329_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_propinqua '2601': 08330_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_repens '2602': 08331_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_rhamnoides '2603': 08332_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_robusta '2604': 08333_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Coprosma_rotundifolia '2605': 08334_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Cruciata_laevipes '2606': 08335_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Diodia_virginiana '2607': 08336_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_album '2608': 08337_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_angustifolium '2609': 08338_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_aparine '2610': 08339_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_boreale '2611': 08340_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_circaezans '2612': 08341_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_mollugo '2613': 08342_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_odoratum '2614': 08343_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_palustre '2615': 08344_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_triflorum '2616': 08345_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Galium_verum '2617': 08346_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Hamelia_patens '2618': 08347_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Hexasepalum_teres '2619': 08348_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Houstonia_caerulea '2620': 08349_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Houstonia_longifolia '2621': 08350_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Houstonia_procumbens '2622': 08351_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Houstonia_purpurea '2623': 08352_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Houstonia_pusilla '2624': 08353_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Ixora_coccinea '2625': 08354_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Mitchella_repens '2626': 08355_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Morinda_citrifolia '2627': 08356_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Nertera_depressa '2628': 08357_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Oldenlandia_corymbosa '2629': 08358_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Paederia_foetida '2630': 08359_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Psychotria_asiatica '2631': 08360_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Psychotria_nervosa '2632': 08361_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Psychotria_poeppigiana '2633': 08362_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Richardia_grandiflora '2634': 08363_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Sherardia_arvensis '2635': 08364_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Spermacoce_verticillata '2636': 08365_Plantae_Tracheophyta_Magnoliopsida_Gentianales_Rubiaceae_Stenaria_nigricans '2637': 08366_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Erodium_botrys '2638': 08367_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Erodium_cicutarium '2639': 08368_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Erodium_moschatum '2640': 08369_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Erodium_texanum '2641': 08370_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_caespitosum '2642': 08371_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_carolinianum '2643': 08372_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_dissectum '2644': 08373_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_erianthum '2645': 08374_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_lucidum '2646': 08375_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_maculatum '2647': 08376_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_molle '2648': 08377_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_palustre '2649': 08378_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_phaeum '2650': 08379_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_pratense '2651': 08380_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_purpureum '2652': 08381_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_pusillum '2653': 08382_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_pyrenaicum '2654': 08383_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_richardsonii '2655': 08384_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_robertianum '2656': 08385_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_sanguineum '2657': 08386_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_sibiricum '2658': 08387_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_sylvaticum '2659': 08388_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Geranium_viscosissimum '2660': 08389_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Pelargonium_capitatum '2661': 08390_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Pelargonium_cucullatum '2662': 08391_Plantae_Tracheophyta_Magnoliopsida_Geraniales_Geraniaceae_Pelargonium_triste '2663': 08392_Plantae_Tracheophyta_Magnoliopsida_Gunnerales_Gunneraceae_Gunnera_tinctoria '2664': 08393_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Acanthus_mollis '2665': 08394_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Asystasia_gangetica '2666': 08395_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Avicennia_germinans '2667': 08396_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Avicennia_marina '2668': 08397_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Dicliptera_brachiata '2669': 08398_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Dyschoriste_linearis '2670': 08399_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Hypoestes_phyllostachya '2671': 08400_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Justicia_americana '2672': 08401_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Justicia_californica '2673': 08402_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Justicia_pilosella '2674': 08403_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Pachystachys_lutea '2675': 08404_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Ruellia_blechum '2676': 08405_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Ruellia_caroliniensis '2677': 08406_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Ruellia_humilis '2678': 08407_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Ruellia_nudiflora '2679': 08408_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Ruellia_simplex '2680': 08409_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Ruellia_strepens '2681': 08410_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Thunbergia_alata '2682': 08411_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Acanthaceae_Thunbergia_grandiflora '2683': 08412_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Bignonia_capreolata '2684': 08413_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Campsis_radicans '2685': 08414_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Catalpa_speciosa '2686': 08415_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Chilopsis_linearis '2687': 08416_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Crescentia_cujete '2688': 08417_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Jacaranda_mimosifolia '2689': 08418_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Spathodea_campanulata '2690': 08419_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Tabebuia_rosea '2691': 08420_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Tecoma_stans '2692': 08421_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Bignoniaceae_Tecomaria_capensis '2693': 08422_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Gesneriaceae_Rhabdothamnus_solandri '2694': 08423_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Agastache_foeniculum '2695': 08424_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Agastache_urticifolia '2696': 08425_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Ajuga_chamaepitys '2697': 08426_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Ajuga_genevensis '2698': 08427_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Ajuga_reptans '2699': 08428_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Ballota_nigra '2700': 08429_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Betonica_officinalis '2701': 08430_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Blephilia_hirsuta '2702': 08431_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Callicarpa_americana '2703': 08432_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Callicarpa_formosana '2704': 08433_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Clinopodium_acinos '2705': 08434_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Clinopodium_douglasii '2706': 08435_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Clinopodium_vulgare '2707': 08436_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Collinsonia_canadensis '2708': 08437_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Condea_emoryi '2709': 08438_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Cunila_origanoides '2710': 08439_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Galeopsis_bifida '2711': 08440_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Galeopsis_speciosa '2712': 08441_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Galeopsis_tetrahit '2713': 08442_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Glechoma_hederacea '2714': 08443_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Hedeoma_acinoides '2715': 08444_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Hyptis_alata '2716': 08445_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lamium_album '2717': 08446_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lamium_amplexicaule '2718': 08447_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lamium_galeobdolon '2719': 08448_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lamium_maculatum '2720': 08449_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lamium_purpureum '2721': 08450_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lavandula_stoechas '2722': 08451_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Leonotis_leonurus '2723': 08452_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Leonotis_nepetifolia '2724': 08453_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Leonurus_cardiaca '2725': 08454_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Leonurus_quinquelobatus '2726': 08455_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lepechinia_calycina '2727': 08456_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lycopus_americanus '2728': 08457_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lycopus_europaeus '2729': 08458_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Lycopus_uniflorus '2730': 08459_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Marrubium_vulgare '2731': 08460_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Melissa_officinalis '2732': 08461_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Melittis_melissophyllum '2733': 08462_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Mentha_aquatica '2734': 08463_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Mentha_arvensis '2735': 08464_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Mentha_canadensis '2736': 08465_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Mentha_longifolia '2737': 08466_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Mentha_pulegium '2738': 08467_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Mentha_spicata '2739': 08468_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Monarda_citriodora '2740': 08469_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Monarda_didyma '2741': 08470_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Monarda_fistulosa '2742': 08471_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Monarda_punctata '2743': 08472_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Monardella_odoratissima '2744': 08473_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Monardella_villosa '2745': 08474_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Nepeta_cataria '2746': 08475_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Origanum_vulgare '2747': 08476_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Perilla_frutescens '2748': 08477_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Phlomoides_tuberosa '2749': 08478_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Physostegia_virginiana '2750': 08479_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Prunella_vulgaris '2751': 08480_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Pycnanthemum_tenuifolium '2752': 08481_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Pycnanthemum_virginianum '2753': 08482_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_apiana '2754': 08483_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_aurea '2755': 08484_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_azurea '2756': 08485_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_coccinea '2757': 08486_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_columbariae '2758': 08487_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_dorrii '2759': 08488_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_farinacea '2760': 08489_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_glutinosa '2761': 08490_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_leucophylla '2762': 08491_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_lyrata '2763': 08492_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_mellifera '2764': 08493_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_microphylla '2765': 08494_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_pratensis '2766': 08495_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_roemeriana '2767': 08496_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_rosmarinus '2768': 08497_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_spathacea '2769': 08498_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_texana '2770': 08499_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Salvia_verticillata '2771': 08500_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_drummondii '2772': 08501_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_galericulata '2773': 08502_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_integrifolia '2774': 08503_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_lateriflora '2775': 08504_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_mexicana '2776': 08505_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_parvula '2777': 08506_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Scutellaria_tuberosa '2778': 08507_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_bullata '2779': 08508_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_byzantina '2780': 08509_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_chamissonis '2781': 08510_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_coccinea '2782': 08511_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_floridana '2783': 08512_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_palustris '2784': 08513_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_recta '2785': 08514_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Stachys_sylvatica '2786': 08515_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Teucrium_canadense '2787': 08516_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Teucrium_chamaedrys '2788': 08517_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Teucrium_cubense '2789': 08518_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Teucrium_scorodonia '2790': 08519_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Thymus_praecox '2791': 08520_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Thymus_pulegioides '2792': 08521_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Thymus_serpyllum '2793': 08522_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Trichostema_dichotomum '2794': 08523_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Trichostema_lanatum '2795': 08524_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Trichostema_lanceolatum '2796': 08525_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Trichostema_parishii '2797': 08526_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Vitex_agnus-castus '2798': 08527_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Vitex_lucens '2799': 08528_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Vitex_rotundifolia '2800': 08529_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lamiaceae_Warnockia_scutellarioides '2801': 08530_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Pinguicula_alpina '2802': 08531_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Pinguicula_vulgaris '2803': 08532_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Utricularia_cornuta '2804': 08533_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Utricularia_gibba '2805': 08534_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Utricularia_intermedia '2806': 08535_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Utricularia_macrorhiza '2807': 08536_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Utricularia_purpurea '2808': 08537_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Lentibulariaceae_Utricularia_subulata '2809': 08538_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Linderniaceae_Lindernia_dubia '2810': 08539_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Linderniaceae_Torenia_crustacea '2811': 08540_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Martyniaceae_Proboscidea_althaeifolia '2812': 08541_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Martyniaceae_Proboscidea_louisianica '2813': 08542_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Mazaceae_Mazus_pumilus '2814': 08543_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Chionanthus_virginicus '2815': 08544_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Forestiera_pubescens '2816': 08545_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_albicans '2817': 08546_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_americana '2818': 08547_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_excelsior '2819': 08548_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_latifolia '2820': 08549_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_nigra '2821': 08550_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_pennsylvanica '2822': 08551_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Fraxinus_velutina '2823': 08552_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Ligustrum_lucidum '2824': 08553_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Ligustrum_quihoui '2825': 08554_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Ligustrum_sinense '2826': 08555_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Ligustrum_vulgare '2827': 08556_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Olea_europaea '2828': 08557_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Oleaceae_Syringa_vulgaris '2829': 08558_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Agalinis_heterophylla '2830': 08559_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Agalinis_purpurea '2831': 08560_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Agalinis_tenuifolia '2832': 08561_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Aphyllon_fasciculatum '2833': 08562_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Aphyllon_purpureum '2834': 08563_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Aphyllon_uniflorum '2835': 08564_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Bartsia_alpina '2836': 08565_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Bellardia_trixago '2837': 08566_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Bellardia_viscosa '2838': 08567_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_affinis '2839': 08568_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_applegatei '2840': 08569_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_arvensis '2841': 08570_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_attenuata '2842': 08571_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_chromosa '2843': 08572_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_coccinea '2844': 08573_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_densiflora '2845': 08574_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_exserta '2846': 08575_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_foliolosa '2847': 08576_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_hispida '2848': 08577_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_indivisa '2849': 08578_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_integra '2850': 08579_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_latifolia '2851': 08580_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_linariifolia '2852': 08581_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_miniata '2853': 08582_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_parviflora '2854': 08583_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_purpurea '2855': 08584_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_rhexiifolia '2856': 08585_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_rubicundula '2857': 08586_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_sessiliflora '2858': 08587_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_tenuiflora '2859': 08588_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Castilleja_wightii '2860': 08589_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Conopholis_alpina '2861': 08590_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Conopholis_americana '2862': 08591_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Cordylanthus_rigidus '2863': 08592_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Epifagus_virginiana '2864': 08593_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Euphrasia_nemorosa '2865': 08594_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Hyobanche_sanguinea '2866': 08595_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Lathraea_squamaria '2867': 08596_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Melampyrum_arvense '2868': 08597_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Melampyrum_lineare '2869': 08598_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Melampyrum_nemorosum '2870': 08599_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Melampyrum_pratense '2871': 08600_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Odontites_vulgaris '2872': 08601_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Orobanche_hederae '2873': 08602_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Orobanche_minor '2874': 08603_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_attollens '2875': 08604_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_bracteosa '2876': 08605_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_canadensis '2877': 08606_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_densiflora '2878': 08607_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_groenlandica '2879': 08608_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_labradorica '2880': 08609_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_lanceolata '2881': 08610_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_racemosa '2882': 08611_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_semibarbata '2883': 08612_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_sylvatica '2884': 08613_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Pedicularis_verticillata '2885': 08614_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Rhinanthus_alectorolophus '2886': 08615_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Rhinanthus_minor '2887': 08616_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Rhinanthus_serotinus '2888': 08617_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Triphysaria_eriantha '2889': 08618_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Orobanchaceae_Triphysaria_pusilla '2890': 08619_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Paulowniaceae_Paulownia_tomentosa '2891': 08620_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Diplacus_aurantiacus '2892': 08621_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Diplacus_bigelovii '2893': 08622_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Diplacus_brevipes '2894': 08623_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Diplacus_longiflorus '2895': 08624_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Diplacus_puniceus '2896': 08625_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Erythranthe_alsinoides '2897': 08626_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Erythranthe_cardinalis '2898': 08627_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Erythranthe_guttata '2899': 08628_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Erythranthe_lewisii '2900': 08629_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Erythranthe_moschata '2901': 08630_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Erythranthe_primuloides '2902': 08631_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Mimulus_alatus '2903': 08632_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Mimulus_ringens '2904': 08633_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Phrymaceae_Phryma_leptostachya '2905': 08634_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Antirrhinum_majus '2906': 08635_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Bacopa_monnieri '2907': 08636_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Chaenorhinum_minus '2908': 08637_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Chelone_glabra '2909': 08638_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Collinsia_heterophylla '2910': 08639_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Collinsia_parviflora '2911': 08640_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Collinsia_verna '2912': 08641_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Cymbalaria_muralis '2913': 08642_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Digitalis_grandiflora '2914': 08643_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Digitalis_purpurea '2915': 08644_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Hippuris_vulgaris '2916': 08645_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Keckiella_antirrhinoides '2917': 08646_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Keckiella_cordifolia '2918': 08647_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Keckiella_ternata '2919': 08648_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Leucospora_multifida '2920': 08649_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Linaria_alpina '2921': 08650_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Linaria_dalmatica '2922': 08651_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Linaria_purpurea '2923': 08652_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Linaria_vulgaris '2924': 08653_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Maurandya_antirrhiniflora '2925': 08654_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Mecardonia_procumbens '2926': 08655_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Mohavea_confertiflora '2927': 08656_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Nuttallanthus_canadensis '2928': 08657_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Nuttallanthus_texanus '2929': 08658_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_barbatus '2930': 08659_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_centranthifolius '2931': 08660_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_cobaea '2932': 08661_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_davidsonii '2933': 08662_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_digitalis '2934': 08663_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_eatonii '2935': 08664_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_grandiflorus '2936': 08665_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_heterophyllus '2937': 08666_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_hirsutus '2938': 08667_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_newberryi '2939': 08668_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_palmeri '2940': 08669_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_procerus '2941': 08670_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_rostriflorus '2942': 08671_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_spectabilis '2943': 08672_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Penstemon_whippleanus '2944': 08673_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_aristata '2945': 08674_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_coronopus '2946': 08675_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_erecta '2947': 08676_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_lanceolata '2948': 08677_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_major '2949': 08678_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_maritima '2950': 08679_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_media '2951': 08680_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_ovata '2952': 08681_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_patagonica '2953': 08682_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_rugelii '2954': 08683_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Plantago_virginica '2955': 08684_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Sairocarpus_coulterianus '2956': 08685_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Sairocarpus_nuttallianus '2957': 08686_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Scoparia_dulcis '2958': 08687_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_americana '2959': 08688_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_anagallis-aquatica '2960': 08689_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_arvensis '2961': 08690_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_beccabunga '2962': 08691_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_chamaedrys '2963': 08692_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_elliptica '2964': 08693_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_filiformis '2965': 08694_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_hederifolia '2966': 08695_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_longifolia '2967': 08696_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_officinalis '2968': 08697_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_peregrina '2969': 08698_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_persica '2970': 08699_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_polita '2971': 08700_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_salicifolia '2972': 08701_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_scutellata '2973': 08702_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_serpyllifolia '2974': 08703_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_spicata '2975': 08704_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_stricta '2976': 08705_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_teucrium '2977': 08706_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronica_wormskjoldii '2978': 08707_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Plantaginaceae_Veronicastrum_virginicum '2979': 08708_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Buddleja_cordata '2980': 08709_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Buddleja_davidii '2981': 08710_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Leucophyllum_frutescens '2982': 08711_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Myoporum_laetum '2983': 08712_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Oftia_africana '2984': 08713_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Scrophularia_californica '2985': 08714_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Scrophularia_nodosa '2986': 08715_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Verbascum_blattaria '2987': 08716_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Verbascum_lychnitis '2988': 08717_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Verbascum_nigrum '2989': 08718_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Verbascum_thapsus '2990': 08719_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Scrophulariaceae_Verbascum_virgatum '2991': 08720_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Stilbaceae_Halleria_lucida '2992': 08721_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Tetrachondraceae_Polypremum_procumbens '2993': 08722_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Aloysia_gratissima '2994': 08723_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Duranta_erecta '2995': 08724_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Glandularia_bipinnatifida '2996': 08725_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Glandularia_canadensis '2997': 08726_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Glandularia_pumila '2998': 08727_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Lantana_camara '2999': 08728_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Lantana_involucrata '3000': 08729_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Lantana_montevidensis '3001': 08730_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Phyla_lanceolata '3002': 08731_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Phyla_nodiflora '3003': 08732_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Stachytarpheta_jamaicensis '3004': 08733_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_bonariensis '3005': 08734_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_bracteata '3006': 08735_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_brasiliensis '3007': 08736_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_halei '3008': 08737_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_hastata '3009': 08738_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_lasiostachys '3010': 08739_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_macdougalii '3011': 08740_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_officinalis '3012': 08741_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_rigida '3013': 08742_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_stricta '3014': 08743_Plantae_Tracheophyta_Magnoliopsida_Lamiales_Verbenaceae_Verbena_urticifolia '3015': 08744_Plantae_Tracheophyta_Magnoliopsida_Laurales_Atherospermataceae_Laurelia_novae-zelandiae '3016': 08745_Plantae_Tracheophyta_Magnoliopsida_Laurales_Calycanthaceae_Calycanthus_floridus '3017': 08746_Plantae_Tracheophyta_Magnoliopsida_Laurales_Calycanthaceae_Calycanthus_occidentalis '3018': 08747_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Beilschmiedia_tawa '3019': 08748_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Cassytha_filiformis '3020': 08749_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Cinnamomum_camphora '3021': 08750_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Laurus_nobilis '3022': 08751_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Lindera_benzoin '3023': 08752_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Persea_americana '3024': 08753_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Sassafras_albidum '3025': 08754_Plantae_Tracheophyta_Magnoliopsida_Laurales_Lauraceae_Umbellularia_californica '3026': 08755_Plantae_Tracheophyta_Magnoliopsida_Laurales_Monimiaceae_Hedycarya_arborea '3027': 08756_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Annonaceae_Annona_glabra '3028': 08757_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Annonaceae_Asimina_triloba '3029': 08758_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Liriodendron_tulipifera '3030': 08759_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Magnolia_acuminata '3031': 08760_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Magnolia_fraseri '3032': 08761_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Magnolia_grandiflora '3033': 08762_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Magnolia_macrophylla '3034': 08763_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Magnolia_tripetala '3035': 08764_Plantae_Tracheophyta_Magnoliopsida_Magnoliales_Magnoliaceae_Magnolia_virginiana '3036': 08765_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Chrysobalanaceae_Chrysobalanus_icaco '3037': 08766_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Acalypha_ostryifolia '3038': 08767_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Acalypha_rhomboidea '3039': 08768_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Cnidoscolus_stimulosus '3040': 08769_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Cnidoscolus_texanus '3041': 08770_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Codiaeum_variegatum '3042': 08771_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_californicus '3043': 08772_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_fruticulosus '3044': 08773_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_glandulosus '3045': 08774_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_lindheimeri '3046': 08775_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_monanthogynus '3047': 08776_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_punctatus '3048': 08777_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Croton_setiger '3049': 08778_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Ditaxis_lanceolata '3050': 08779_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_albomarginata '3051': 08780_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_amygdaloides '3052': 08781_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_antisyphilitica '3053': 08782_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_bicolor '3054': 08783_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_caput-medusae '3055': 08784_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_characias '3056': 08785_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_corollata '3057': 08786_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_cyathophora '3058': 08787_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_cyparissias '3059': 08788_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_dentata '3060': 08789_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_fendleri '3061': 08790_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_helioscopia '3062': 08791_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_heterophylla '3063': 08792_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_hirta '3064': 08793_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_hypericifolia '3065': 08794_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_hyssopifolia '3066': 08795_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_lathyris '3067': 08796_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_maculata '3068': 08797_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_marginata '3069': 08798_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_mauritanica '3070': 08799_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_melanadenia '3071': 08800_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_misera '3072': 08801_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_myrsinites '3073': 08802_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_nutans '3074': 08803_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_oblongata '3075': 08804_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_ophthalmica '3076': 08805_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_paralias '3077': 08806_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_peplus '3078': 08807_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_polycarpa '3079': 08808_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_prostrata '3080': 08809_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_serpens '3081': 08810_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_serpillifolia '3082': 08811_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_spathulata '3083': 08812_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_terracina '3084': 08813_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Euphorbia_virgata '3085': 08814_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Jatropha_dioica '3086': 08815_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Jatropha_gossypiifolia '3087': 08816_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Macaranga_tanarius '3088': 08817_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Mallotus_paniculatus '3089': 08818_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Manihot_esculenta '3090': 08819_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Mercurialis_annua '3091': 08820_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Mercurialis_perennis '3092': 08821_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Ricinus_communis '3093': 08822_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Stillingia_sylvatica '3094': 08823_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Stillingia_texana '3095': 08824_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Euphorbiaceae_Triadica_sebifera '3096': 08825_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_androsaemum '3097': 08826_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_ascyron '3098': 08827_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_calycinum '3099': 08828_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_fraseri '3100': 08829_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_gentianoides '3101': 08830_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_hypericoides '3102': 08831_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_kalmianum '3103': 08832_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_maculatum '3104': 08833_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_mutilum '3105': 08834_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_perforatum '3106': 08835_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_punctatum '3107': 08836_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Hypericaceae_Hypericum_tetrapetalum '3108': 08837_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Linaceae_Linum_bienne '3109': 08838_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Linaceae_Linum_lewisii '3110': 08839_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Linaceae_Linum_pratense '3111': 08840_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Linaceae_Linum_rigidum '3112': 08841_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Malpighiaceae_Byrsonima_crassifolia '3113': 08842_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_caerulea '3114': 08843_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_edulis '3115': 08844_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_foetida '3116': 08845_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_incarnata '3117': 08846_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_lutea '3118': 08847_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_suberosa '3119': 08848_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_tetrandra '3120': 08849_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_tripartita '3121': 08850_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Passiflora_vitifolia '3122': 08851_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Passifloraceae_Turnera_ulmifolia '3123': 08852_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Phyllanthaceae_Bischofia_javanica '3124': 08853_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Phyllanthaceae_Phyllanthus_tenellus '3125': 08854_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Phyllanthaceae_Phyllanthus_urinaria '3126': 08855_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Rhizophoraceae_Rhizophora_mangle '3127': 08856_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_alba '3128': 08857_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_balsamifera '3129': 08858_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_deltoides '3130': 08859_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_fremontii '3131': 08860_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_grandidentata '3132': 08861_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_nigra '3133': 08862_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_tremula '3134': 08863_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_tremuloides '3135': 08864_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Populus_trichocarpa '3136': 08865_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_alba '3137': 08866_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_babylonica '3138': 08867_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_bebbiana '3139': 08868_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_caprea '3140': 08869_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_cinerea '3141': 08870_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_discolor '3142': 08871_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_eriocephala '3143': 08872_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_exigua '3144': 08873_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_gooddingii '3145': 08874_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_interior '3146': 08875_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_laevigata '3147': 08876_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_lasiolepis '3148': 08877_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_myrsinifolia '3149': 08878_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_nigra '3150': 08879_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_reticulata '3151': 08880_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_triandra '3152': 08881_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Salicaceae_Salix_viminalis '3153': 08882_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Melicytus_alpinus '3154': 08883_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Melicytus_ramiflorus '3155': 08884_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_adunca '3156': 08885_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_arvensis '3157': 08886_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_bicolor '3158': 08887_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_biflora '3159': 08888_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_blanda '3160': 08889_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_canadensis '3161': 08890_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_canina '3162': 08891_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_glabella '3163': 08892_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_hastata '3164': 08893_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_hirta '3165': 08894_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_labradorica '3166': 08895_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_lanceolata '3167': 08896_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_lobata '3168': 08897_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_macloskeyi '3169': 08898_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_mirabilis '3170': 08899_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_nuttallii '3171': 08900_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_ocellata '3172': 08901_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_odorata '3173': 08902_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_palmata '3174': 08903_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_pedata '3175': 08904_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_pedatifida '3176': 08905_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_pedunculata '3177': 08906_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_primulifolia '3178': 08907_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_pubescens '3179': 08908_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_purpurea '3180': 08909_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_reichenbachiana '3181': 08910_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_riviniana '3182': 08911_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_rostrata '3183': 08912_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_rotundifolia '3184': 08913_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_sagittata '3185': 08914_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_sempervirens '3186': 08915_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_sororia '3187': 08916_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_striata '3188': 08917_Plantae_Tracheophyta_Magnoliopsida_Malpighiales_Violaceae_Viola_tricolor '3189': 08918_Plantae_Tracheophyta_Magnoliopsida_Malvales_Bixaceae_Bixa_orellana '3190': 08919_Plantae_Tracheophyta_Magnoliopsida_Malvales_Bixaceae_Cochlospermum_vitifolium '3191': 08920_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Cistus_albidus '3192': 08921_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Cistus_creticus '3193': 08922_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Cistus_ladanifer '3194': 08923_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Cistus_monspeliensis '3195': 08924_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Cistus_salviifolius '3196': 08925_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Crocanthemum_canadense '3197': 08926_Plantae_Tracheophyta_Magnoliopsida_Malvales_Cistaceae_Crocanthemum_scoparium '3198': 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08949_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Hibiscus_tiliaceus '3221': 08950_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Hibiscus_trionum '3222': 08951_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Hoheria_populnea '3223': 08952_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Horsfordia_newberryi '3224': 08953_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Kosteletzkya_pentacarpos '3225': 08954_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malacothamnus_fasciculatus '3226': 08955_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_arborea '3227': 08956_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_assurgentiflora '3228': 08957_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_moschata '3229': 08958_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_neglecta '3230': 08959_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_parviflora '3231': 08960_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_sylvestris '3232': 08961_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malva_thuringiaca '3233': 08962_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malvastrum_coromandelianum '3234': 08963_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malvaviscus_arboreus '3235': 08964_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Malvella_leprosa '3236': 08965_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Melochia_pyramidata '3237': 08966_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Melochia_tomentosa '3238': 08967_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Modiola_caroliniana '3239': 08968_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Pachira_aquatica '3240': 08969_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Pavonia_lasiopetala '3241': 08970_Plantae_Tracheophyta_Magnoliopsida_Malvales_Malvaceae_Plagianthus_divaricatus '3242': 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09004_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Lythraceae_Trapa_natans '3276': 09005_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Clidemia_hirta '3277': 09006_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Melastoma_malabathricum '3278': 09007_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Rhexia_alifanus '3279': 09008_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Rhexia_mariana '3280': 09009_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Melastomataceae_Rhexia_virginica '3281': 09010_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Eucalyptus_camaldulensis '3282': 09011_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Eucalyptus_globulus '3283': 09012_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Leptospermum_laevigatum '3284': 09013_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Leptospermum_scoparium '3285': 09014_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Myrtaceae_Lophomyrtus_bullata '3286': 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09048_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Fuchsia_excorticata '3320': 09049_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Fuchsia_magellanica '3321': 09050_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Lopezia_racemosa '3322': 09051_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Ludwigia_alternifolia '3323': 09052_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Ludwigia_octovalvis '3324': 09053_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Ludwigia_palustris '3325': 09054_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Ludwigia_peploides '3326': 09055_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Ludwigia_peruviana '3327': 09056_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_berlandieri '3328': 09057_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_biennis '3329': 09058_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_cespitosa '3330': 09059_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_curtiflora '3331': 09060_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_deltoides '3332': 09061_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_drummondii '3333': 09062_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_elata '3334': 09063_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_gaura '3335': 09064_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_glaucifolia '3336': 09065_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_glazioviana '3337': 09066_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_hartwegii '3338': 09067_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_laciniata '3339': 09068_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_macrocarpa '3340': 09069_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_rosea '3341': 09070_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_rubricaulis '3342': 09071_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_speciosa '3343': 09072_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_suffrutescens '3344': 09073_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_suffulta '3345': 09074_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Oenothera_triloba '3346': 09075_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Onagraceae_Taraxia_ovata '3347': 09076_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Penaeaceae_Penaea_mucronata '3348': 09077_Plantae_Tracheophyta_Magnoliopsida_Myrtales_Penaeaceae_Saltera_sarcocolla '3349': 09078_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Cabombaceae_Brasenia_schreberi '3350': 09079_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nuphar_advena '3351': 09080_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nuphar_lutea '3352': 09081_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nuphar_polysepala '3353': 09082_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nuphar_variegata '3354': 09083_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nymphaea_alba '3355': 09084_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nymphaea_candida '3356': 09085_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nymphaea_nouchali '3357': 09086_Plantae_Tracheophyta_Magnoliopsida_Nymphaeales_Nymphaeaceae_Nymphaea_odorata '3358': 09087_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Cunoniaceae_Bauera_rubioides '3359': 09088_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Cunoniaceae_Weinmannia_racemosa '3360': 09089_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Elaeocarpaceae_Aristotelia_serrata '3361': 09090_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Elaeocarpaceae_Elaeocarpus_hookerianus '3362': 09091_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_acetosella '3363': 09092_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_articulata '3364': 09093_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_corniculata '3365': 09094_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_debilis '3366': 09095_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_dillenii '3367': 09096_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_drummondii '3368': 09097_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_incarnata '3369': 09098_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_latifolia '3370': 09099_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_luteola '3371': 09100_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_montana '3372': 09101_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_obtusa '3373': 09102_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_oregana '3374': 09103_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_pes-caprae '3375': 09104_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_polyphylla '3376': 09105_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_purpurea '3377': 09106_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_stricta '3378': 09107_Plantae_Tracheophyta_Magnoliopsida_Oxalidales_Oxalidaceae_Oxalis_violacea '3379': 09108_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Aristolochia_californica '3380': 09109_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Aristolochia_clematitis '3381': 09110_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Asarum_canadense '3382': 09111_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Asarum_caudatum '3383': 09112_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Asarum_europaeum '3384': 09113_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Endodeca_serpentaria '3385': 09114_Plantae_Tracheophyta_Magnoliopsida_Piperales_Aristolochiaceae_Hexastylis_arifolia '3386': 09115_Plantae_Tracheophyta_Magnoliopsida_Piperales_Piperaceae_Piper_excelsum '3387': 09116_Plantae_Tracheophyta_Magnoliopsida_Piperales_Saururaceae_Anemopsis_californica '3388': 09117_Plantae_Tracheophyta_Magnoliopsida_Piperales_Saururaceae_Houttuynia_cordata '3389': 09118_Plantae_Tracheophyta_Magnoliopsida_Piperales_Saururaceae_Saururus_cernuus '3390': 09119_Plantae_Tracheophyta_Magnoliopsida_Proteales_Nelumbonaceae_Nelumbo_lutea '3391': 09120_Plantae_Tracheophyta_Magnoliopsida_Proteales_Nelumbonaceae_Nelumbo_nucifera '3392': 09121_Plantae_Tracheophyta_Magnoliopsida_Proteales_Platanaceae_Platanus_occidentalis '3393': 09122_Plantae_Tracheophyta_Magnoliopsida_Proteales_Platanaceae_Platanus_racemosa '3394': 09123_Plantae_Tracheophyta_Magnoliopsida_Proteales_Platanaceae_Platanus_wrightii '3395': 09124_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Banksia_integrifolia '3396': 09125_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Banksia_marginata '3397': 09126_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Hakea_sericea '3398': 09127_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Knightia_excelsa '3399': 09128_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Leucadendron_laureolum '3400': 09129_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Leucadendron_rubrum '3401': 09130_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Leucadendron_salignum '3402': 09131_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Leucospermum_conocarpodendron '3403': 09132_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Leucospermum_cuneiforme '3404': 09133_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Mimetes_cucullatus '3405': 09134_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Protea_caffra '3406': 09135_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Protea_cynaroides '3407': 09136_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Protea_laurifolia '3408': 09137_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Protea_neriifolia '3409': 09138_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Protea_nitida '3410': 09139_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Protea_repens '3411': 09140_Plantae_Tracheophyta_Magnoliopsida_Proteales_Proteaceae_Serruria_fasciflora '3412': 09141_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Achlys_triphylla '3413': 09142_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_aquifolium '3414': 09143_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_bealei '3415': 09144_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_darwinii '3416': 09145_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_nervosa '3417': 09146_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_repens '3418': 09147_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_thunbergii '3419': 09148_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_trifoliolata '3420': 09149_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Berberis_vulgaris '3421': 09150_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Caulophyllum_giganteum '3422': 09151_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Caulophyllum_thalictroides '3423': 09152_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Jeffersonia_diphylla '3424': 09153_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Nandina_domestica '3425': 09154_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Podophyllum_peltatum '3426': 09155_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Berberidaceae_Vancouveria_hexandra '3427': 09156_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Menispermaceae_Cocculus_carolinus '3428': 09157_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Menispermaceae_Menispermum_canadense '3429': 09158_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Argemone_albiflora '3430': 09159_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Argemone_mexicana '3431': 09160_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Argemone_munita '3432': 09161_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Argemone_ochroleuca '3433': 09162_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Argemone_polyanthemos '3434': 09163_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Capnoides_sempervirens '3435': 09164_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Chelidonium_majus '3436': 09165_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Corydalis_aurea '3437': 09166_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Corydalis_cava '3438': 09167_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Corydalis_flavula '3439': 09168_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Corydalis_solida '3440': 09169_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Dendromecon_rigida '3441': 09170_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Dicentra_canadensis '3442': 09171_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Dicentra_cucullaria '3443': 09172_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Dicentra_formosa '3444': 09173_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Ehrendorferia_chrysantha '3445': 09174_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Eschscholzia_caespitosa '3446': 09175_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Eschscholzia_californica '3447': 09176_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Eschscholzia_minutiflora '3448': 09177_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Eschscholzia_parishii '3449': 09178_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Fumaria_capreolata '3450': 09179_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Fumaria_muralis '3451': 09180_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Fumaria_officinalis '3452': 09181_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Glaucium_flavum '3453': 09182_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Lamprocapnos_spectabilis '3454': 09183_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Papaver_cambricum '3455': 09184_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Papaver_dubium '3456': 09185_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Papaver_heterophyllum '3457': 09186_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Papaver_rhoeas '3458': 09187_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Papaver_somniferum '3459': 09188_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Platystemon_californicus '3460': 09189_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Pseudofumaria_lutea '3461': 09190_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Romneya_coulteri '3462': 09191_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Sanguinaria_canadensis '3463': 09192_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Papaveraceae_Stylophorum_diphyllum '3464': 09193_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aconitum_columbianum '3465': 09194_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aconitum_delphiniifolium '3466': 09195_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aconitum_lycoctonum '3467': 09196_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aconitum_napellus '3468': 09197_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aconitum_septentrionale '3469': 09198_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Actaea_pachypoda '3470': 09199_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Actaea_racemosa '3471': 09200_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Actaea_rubra '3472': 09201_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Actaea_spicata '3473': 09202_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Adonis_vernalis '3474': 09203_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemonastrum_canadense '3475': 09204_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemone_berlandieri '3476': 09205_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemone_coronaria '3477': 09206_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemone_cylindrica '3478': 09207_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemone_hortensis '3479': 09208_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemone_multifida '3480': 09209_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemone_virginiana '3481': 09210_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemonoides_altaica '3482': 09211_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemonoides_nemorosa '3483': 09212_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemonoides_quinquefolia '3484': 09213_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemonoides_ranunculoides '3485': 09214_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Anemonoides_sylvestris '3486': 09215_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_atrata '3487': 09216_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_canadensis '3488': 09217_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_chrysantha '3489': 09218_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_coerulea '3490': 09219_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_flavescens '3491': 09220_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_formosa '3492': 09221_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Aquilegia_vulgaris '3493': 09222_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Caltha_leptosepala '3494': 09223_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Caltha_palustris '3495': 09224_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ceratocephala_testiculata '3496': 09225_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_alpina '3497': 09226_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_crispa '3498': 09227_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_drummondii '3499': 09228_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_lasiantha '3500': 09229_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_ligusticifolia '3501': 09230_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_occidentalis '3502': 09231_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_paniculata '3503': 09232_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_pitcheri '3504': 09233_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_terniflora '3505': 09234_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_texensis '3506': 09235_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_virginiana '3507': 09236_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Clematis_vitalba '3508': 09237_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Consolida_regalis '3509': 09238_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Coptis_trifolia '3510': 09239_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_cardinale '3511': 09240_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_carolinianum '3512': 09241_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_glaucum '3513': 09242_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_nudicaule '3514': 09243_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_nuttallianum '3515': 09244_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_parishii '3516': 09245_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Delphinium_tricorne '3517': 09246_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Enemion_biternatum '3518': 09247_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Eranthis_hyemalis '3519': 09248_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ficaria_verna '3520': 09249_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Helleborus_foetidus '3521': 09250_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Hepatica_acutiloba '3522': 09251_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Hepatica_americana '3523': 09252_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Hepatica_nobilis '3524': 09253_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Hydrastis_canadensis '3525': 09254_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Nigella_damascena '3526': 09255_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Pulsatilla_alpina '3527': 09256_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Pulsatilla_nuttalliana '3528': 09257_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Pulsatilla_occidentalis '3529': 09258_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Pulsatilla_patens '3530': 09259_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_abortivus '3531': 09260_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_acris '3532': 09261_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_aquatilis '3533': 09262_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_auricomus '3534': 09263_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_bulbosus '3535': 09264_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_californicus '3536': 09265_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_cassubicus '3537': 09266_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_fascicularis '3538': 09267_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_flammula '3539': 09268_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_glaberrimus '3540': 09269_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_hispidus '3541': 09270_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_muricatus '3542': 09271_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_occidentalis '3543': 09272_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_polyanthemos '3544': 09273_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_recurvatus '3545': 09274_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_repens '3546': 09275_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Ranunculus_sceleratus '3547': 09276_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_aquilegiifolium '3548': 09277_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_dasycarpum '3549': 09278_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_dioicum '3550': 09279_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_fendleri '3551': 09280_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_lucidum '3552': 09281_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_minus '3553': 09282_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_pubescens '3554': 09283_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Thalictrum_thalictroides '3555': 09284_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Trollius_asiaticus '3556': 09285_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Trollius_europaeus '3557': 09286_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Trollius_laxus '3558': 09287_Plantae_Tracheophyta_Magnoliopsida_Ranunculales_Ranunculaceae_Xanthorhiza_simplicissima '3559': 09288_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Cannabis_sativa '3560': 09289_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Celtis_laevigata '3561': 09290_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Celtis_occidentalis '3562': 09291_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Celtis_pallida '3563': 09292_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Celtis_reticulata '3564': 09293_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Humulus_japonicus '3565': 09294_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Humulus_lupulus '3566': 09295_Plantae_Tracheophyta_Magnoliopsida_Rosales_Cannabaceae_Trema_orientalis '3567': 09296_Plantae_Tracheophyta_Magnoliopsida_Rosales_Elaeagnaceae_Elaeagnus_angustifolia '3568': 09297_Plantae_Tracheophyta_Magnoliopsida_Rosales_Elaeagnaceae_Elaeagnus_commutata '3569': 09298_Plantae_Tracheophyta_Magnoliopsida_Rosales_Elaeagnaceae_Elaeagnus_pungens '3570': 09299_Plantae_Tracheophyta_Magnoliopsida_Rosales_Elaeagnaceae_Elaeagnus_umbellata '3571': 09300_Plantae_Tracheophyta_Magnoliopsida_Rosales_Elaeagnaceae_Hippophae_rhamnoides '3572': 09301_Plantae_Tracheophyta_Magnoliopsida_Rosales_Elaeagnaceae_Shepherdia_canadensis '3573': 09302_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Artocarpus_altilis '3574': 09303_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Artocarpus_heterophyllus '3575': 09304_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Broussonetia_papyrifera '3576': 09305_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Fatoua_villosa '3577': 09306_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Ficus_aurea '3578': 09307_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Ficus_carica '3579': 09308_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Ficus_microcarpa '3580': 09309_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Ficus_petiolaris '3581': 09310_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Ficus_pumila '3582': 09311_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Ficus_religiosa '3583': 09312_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Maclura_pomifera '3584': 09313_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Morus_alba '3585': 09314_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Morus_indica '3586': 09315_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Morus_rubra '3587': 09316_Plantae_Tracheophyta_Magnoliopsida_Rosales_Moraceae_Streblus_heterophyllus '3588': 09317_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Berchemia_scandens '3589': 09318_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_americanus '3590': 09319_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_cordulatus '3591': 09320_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_crassifolius '3592': 09321_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_cuneatus '3593': 09322_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_fendleri '3594': 09323_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_herbaceus '3595': 09324_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_integerrimus '3596': 09325_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_leucodermis '3597': 09326_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_megacarpus '3598': 09327_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_oliganthus '3599': 09328_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_perplexans '3600': 09329_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_prostratus '3601': 09330_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_spinosus '3602': 09331_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_thyrsiflorus '3603': 09332_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_velutinus '3604': 09333_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ceanothus_verrucosus '3605': 09334_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Colubrina_texensis '3606': 09335_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Condalia_hookeri '3607': 09336_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Discaria_toumatou '3608': 09337_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Frangula_alnus '3609': 09338_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Frangula_californica '3610': 09339_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Frangula_caroliniana '3611': 09340_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Frangula_purshiana '3612': 09341_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Paliurus_spina-christi '3613': 09342_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Rhamnus_alaternus '3614': 09343_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Rhamnus_alnifolia '3615': 09344_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Rhamnus_cathartica '3616': 09345_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Rhamnus_crocea '3617': 09346_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Rhamnus_humboldtiana '3618': 09347_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Rhamnus_ilicifolia '3619': 09348_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Trichocephalus_stipularis '3620': 09349_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rhamnaceae_Ziziphus_obtusifolia '3621': 09350_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Acaena_anserinifolia '3622': 09351_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Acaena_novae-zelandiae '3623': 09352_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Adenostoma_fasciculatum '3624': 09353_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Adenostoma_sparsifolium '3625': 09354_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Agrimonia_eupatoria '3626': 09355_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Agrimonia_gryposepala '3627': 09356_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Agrimonia_parviflora '3628': 09357_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Agrimonia_pilosa '3629': 09358_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Amelanchier_alnifolia '3630': 09359_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Amelanchier_arborea '3631': 09360_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Amelanchier_spicata '3632': 09361_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Amelanchier_utahensis '3633': 09362_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Argentina_anserina '3634': 09363_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Aronia_melanocarpa '3635': 09364_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Aruncus_dioicus '3636': 09365_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Cercocarpus_betuloides '3637': 09366_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Cercocarpus_ledifolius '3638': 09367_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Cercocarpus_montanus '3639': 09368_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Chamaebatia_foliolosa '3640': 09369_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Chamaebatiaria_millefolium '3641': 09370_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Coleogyne_ramosissima '3642': 09371_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Comarum_palustre '3643': 09372_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Crataegus_marshallii '3644': 09373_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Crataegus_monogyna '3645': 09374_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Dasiphora_fruticosa '3646': 09375_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Dryas_drummondii '3647': 09376_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Dryas_octopetala '3648': 09377_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Drymocallis_arguta '3649': 09378_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Drymocallis_glandulosa '3650': 09379_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Eriobotrya_japonica '3651': 09380_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Fallugia_paradoxa '3652': 09381_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Filipendula_rubra '3653': 09382_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Filipendula_ulmaria '3654': 09383_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Filipendula_vulgaris '3655': 09384_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Fragaria_chiloensis '3656': 09385_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Fragaria_vesca '3657': 09386_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Fragaria_virginiana '3658': 09387_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Fragaria_viridis '3659': 09388_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_aleppicum '3660': 09389_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_canadense '3661': 09390_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_macrophyllum '3662': 09391_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_rivale '3663': 09392_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_rossii '3664': 09393_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_triflorum '3665': 09394_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Geum_urbanum '3666': 09395_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Heteromeles_arbutifolia '3667': 09396_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Holodiscus_discolor '3668': 09397_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Kerria_japonica '3669': 09398_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Luetkea_pectinata '3670': 09399_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Malus_domestica '3671': 09400_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Malus_fusca '3672': 09401_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Malus_pumila '3673': 09402_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Oemleria_cerasiformis '3674': 09403_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Petrophytum_caespitosum '3675': 09404_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Photinia_serratifolia '3676': 09405_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Physocarpus_capitatus '3677': 09406_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Physocarpus_opulifolius '3678': 09407_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_argentea '3679': 09408_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_canadensis '3680': 09409_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_erecta '3681': 09410_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_gracilis '3682': 09411_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_indica '3683': 09412_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_norvegica '3684': 09413_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_recta '3685': 09414_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_reptans '3686': 09415_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Potentilla_simplex '3687': 09416_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_americana '3688': 09417_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_angustifolia '3689': 09418_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_avium '3690': 09419_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_caroliniana '3691': 09420_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_cerasifera '3692': 09421_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_dulcis '3693': 09422_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_emarginata '3694': 09423_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_fasciculata '3695': 09424_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_fremontii '3696': 09425_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_ilicifolia '3697': 09426_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_laurocerasus '3698': 09427_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_mexicana '3699': 09428_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_padus '3700': 09429_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_pensylvanica '3701': 09430_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_persica '3702': 09431_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_pumila '3703': 09432_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_serotina '3704': 09433_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_spinosa '3705': 09434_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Prunus_virginiana '3706': 09435_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Purshia_stansburyana '3707': 09436_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Purshia_tridentata '3708': 09437_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Pyracantha_coccinea '3709': 09438_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Pyrus_calleryana '3710': 09439_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Pyrus_communis '3711': 09440_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rhaphiolepis_indica '3712': 09441_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rhodotypos_scandens '3713': 09442_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_acicularis '3714': 09443_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_arkansana '3715': 09444_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_bracteata '3716': 09445_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_californica '3717': 09446_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_canina '3718': 09447_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_carolina '3719': 09448_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_gymnocarpa '3720': 09449_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_majalis '3721': 09450_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_multiflora '3722': 09451_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_nutkana '3723': 09452_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_palustris '3724': 09453_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_rubiginosa '3725': 09454_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_rugosa '3726': 09455_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_spinosissima '3727': 09456_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rosa_woodsii '3728': 09457_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_allegheniensis '3729': 09458_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_arcticus '3730': 09459_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_armeniacus '3731': 09460_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_bifrons '3732': 09461_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_caesius '3733': 09462_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_chamaemorus '3734': 09463_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_cissoides '3735': 09464_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_dalibarda '3736': 09465_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_flagellaris '3737': 09466_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_hispidus '3738': 09467_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_idaeus '3739': 09468_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_laciniatus '3740': 09469_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_leucodermis '3741': 09470_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_occidentalis '3742': 09471_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_odoratus '3743': 09472_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_parviflorus '3744': 09473_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_pedatus '3745': 09474_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_phoenicolasius '3746': 09475_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_pubescens '3747': 09476_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_saxatilis '3748': 09477_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_schmidelioides '3749': 09478_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_spectabilis '3750': 09479_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_trivialis '3751': 09480_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_ulmifolius '3752': 09481_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Rubus_ursinus '3753': 09482_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sanguisorba_canadensis '3754': 09483_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sanguisorba_minor '3755': 09484_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sanguisorba_officinalis '3756': 09485_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sibbaldia_procumbens '3757': 09486_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sibbaldiopsis_tridentata '3758': 09487_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sorbaria_sorbifolia '3759': 09488_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sorbus_americana '3760': 09489_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sorbus_aucuparia '3761': 09490_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Sorbus_sitchensis '3762': 09491_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Spiraea_alba '3763': 09492_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Spiraea_betulifolia '3764': 09493_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Spiraea_douglasii '3765': 09494_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Spiraea_japonica '3766': 09495_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Spiraea_splendens '3767': 09496_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Spiraea_tomentosa '3768': 09497_Plantae_Tracheophyta_Magnoliopsida_Rosales_Rosaceae_Waldsteinia_fragarioides '3769': 09498_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_alata '3770': 09499_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_americana '3771': 09500_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_crassifolia '3772': 09501_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_glabra '3773': 09502_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_laevis '3774': 09503_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_minor '3775': 09504_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_parvifolia '3776': 09505_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_pumila '3777': 09506_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_rubra '3778': 09507_Plantae_Tracheophyta_Magnoliopsida_Rosales_Ulmaceae_Ulmus_thomasii '3779': 09508_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Boehmeria_cylindrica '3780': 09509_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Elatostema_rugosum '3781': 09510_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Laportea_canadensis '3782': 09511_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Parietaria_judaica '3783': 09512_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Parietaria_pensylvanica '3784': 09513_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Pilea_microphylla '3785': 09514_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Pilea_pumila '3786': 09515_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Urtica_chamaedryoides '3787': 09516_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Urtica_dioica '3788': 09517_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Urtica_ferox '3789': 09518_Plantae_Tracheophyta_Magnoliopsida_Rosales_Urticaceae_Urtica_urens '3790': 09519_Plantae_Tracheophyta_Magnoliopsida_Santalales_Loranthaceae_Ileostylus_micranthus '3791': 09520_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Arceuthobium_campylopodum '3792': 09521_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Colpoon_compressum '3793': 09522_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Comandra_umbellata '3794': 09523_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Exocarpos_cupressiformis '3795': 09524_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Geocaulon_lividum '3796': 09525_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Phoradendron_californicum '3797': 09526_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Phoradendron_leucarpum '3798': 09527_Plantae_Tracheophyta_Magnoliopsida_Santalales_Santalaceae_Viscum_album '3799': 09528_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Anacardium_occidentale '3800': 09529_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Cotinus_coggygria '3801': 09530_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Malosma_laurina '3802': 09531_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Mangifera_indica '3803': 09532_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Pistacia_chinensis '3804': 09533_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Pistacia_lentiscus '3805': 09534_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_aromatica '3806': 09535_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_copallinum '3807': 09536_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_glabra '3808': 09537_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_integrifolia '3809': 09538_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_lanceolata '3810': 09539_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_microphylla '3811': 09540_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_ovata '3812': 09541_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_typhina '3813': 09542_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Rhus_virens '3814': 09543_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Schinus_molle '3815': 09544_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Schinus_terebinthifolia '3816': 09545_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Searsia_lucida '3817': 09546_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Toxicodendron_diversilobum '3818': 09547_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Toxicodendron_radicans '3819': 09548_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Toxicodendron_rydbergii '3820': 09549_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Anacardiaceae_Toxicodendron_vernix '3821': 09550_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Burseraceae_Bursera_microphylla '3822': 09551_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Burseraceae_Bursera_simaruba '3823': 09552_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Meliaceae_Azadirachta_indica '3824': 09553_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Meliaceae_Dysoxylum_spectabile '3825': 09554_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Meliaceae_Melia_azedarach '3826': 09555_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Citrus_trifoliata '3827': 09556_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Cneoridium_dumosum '3828': 09557_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Correa_reflexa '3829': 09558_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Murraya_paniculata '3830': 09559_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Ptelea_trifoliata '3831': 09560_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Thamnosma_montana '3832': 09561_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Zanthoxylum_americanum '3833': 09562_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Zanthoxylum_clava-herculis '3834': 09563_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Zanthoxylum_fagara '3835': 09564_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Rutaceae_Zanthoxylum_hirsutum '3836': 09565_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_campestre '3837': 09566_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_circinatum '3838': 09567_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_floridanum '3839': 09568_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_ginnala '3840': 09569_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_glabrum '3841': 09570_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_grandidentatum '3842': 09571_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_macrophyllum '3843': 09572_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_negundo '3844': 09573_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_nigrum '3845': 09574_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_palmatum '3846': 09575_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_pensylvanicum '3847': 09576_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_platanoides '3848': 09577_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_pseudoplatanus '3849': 09578_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_rubrum '3850': 09579_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_saccharinum '3851': 09580_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_saccharum '3852': 09581_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_spicatum '3853': 09582_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Acer_tataricum '3854': 09583_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Aesculus_californica '3855': 09584_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Aesculus_glabra '3856': 09585_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Aesculus_hippocastanum '3857': 09586_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Aesculus_pavia '3858': 09587_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Alectryon_excelsus '3859': 09588_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Cardiospermum_halicacabum '3860': 09589_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Dodonaea_viscosa '3861': 09590_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Koelreuteria_paniculata '3862': 09591_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Sapindus_drummondii '3863': 09592_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Sapindaceae_Ungnadia_speciosa '3864': 09593_Plantae_Tracheophyta_Magnoliopsida_Sapindales_Simaroubaceae_Ailanthus_altissima '3865': 09594_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Altingiaceae_Liquidambar_styraciflua '3866': 09595_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Cotyledon_orbiculata '3867': 09596_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Crassula_connata '3868': 09597_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Crassula_ovata '3869': 09598_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_arizonica '3870': 09599_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_cymosa '3871': 09600_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_edulis '3872': 09601_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_farinosa '3873': 09602_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_lanceolata '3874': 09603_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_pulverulenta '3875': 09604_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Dudleya_saxosa '3876': 09605_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Echeveria_gibbiflora '3877': 09606_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Hylotelephium_telephium '3878': 09607_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Kalanchoe_delagoensis '3879': 09608_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Kalanchoe_pinnata '3880': 09609_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Orostachys_spinosa '3881': 09610_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Rhodiola_integrifolia '3882': 09611_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Rhodiola_rosea '3883': 09612_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_acre '3884': 09613_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_album '3885': 09614_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_dasyphyllum '3886': 09615_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_lanceolatum '3887': 09616_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_nuttallii '3888': 09617_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_obtusatum '3889': 09618_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_sexangulare '3890': 09619_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_spathulifolium '3891': 09620_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sedum_ternatum '3892': 09621_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sempervivum_arachnoideum '3893': 09622_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Sempervivum_tectorum '3894': 09623_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Tylecodon_paniculatus '3895': 09624_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Crassulaceae_Umbilicus_rupestris '3896': 09625_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_americanum '3897': 09626_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_aureum '3898': 09627_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_bracteosum '3899': 09628_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_cereum '3900': 09629_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_cynosbati '3901': 09630_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_indecorum '3902': 09631_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_lacustre '3903': 09632_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_malvaceum '3904': 09633_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_nevadense '3905': 09634_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_nigrum '3906': 09635_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_roezlii '3907': 09636_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_rubrum '3908': 09637_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_sanguineum '3909': 09638_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_speciosum '3910': 09639_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_triste '3911': 09640_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Grossulariaceae_Ribes_uva-crispa '3912': 09641_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Haloragaceae_Haloragis_erecta '3913': 09642_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Haloragaceae_Myriophyllum_aquaticum '3914': 09643_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Haloragaceae_Myriophyllum_spicatum '3915': 09644_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Hamamelidaceae_Hamamelis_virginiana '3916': 09645_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Iteaceae_Itea_virginica '3917': 09646_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Paeoniaceae_Paeonia_brownii '3918': 09647_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Paeoniaceae_Paeonia_californica '3919': 09648_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Penthoraceae_Penthorum_sedoides '3920': 09649_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Chrysosplenium_alternifolium '3921': 09650_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Chrysosplenium_americanum '3922': 09651_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Darmera_peltata '3923': 09652_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Heuchera_cylindrica '3924': 09653_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Heuchera_micrantha '3925': 09654_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Heuchera_richardsonii '3926': 09655_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Leptarrhena_pyrolifolia '3927': 09656_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Lithophragma_affine '3928': 09657_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Lithophragma_heterophyllum '3929': 09658_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Lithophragma_parviflorum '3930': 09659_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Micranthes_californica '3931': 09660_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Micranthes_ferruginea '3932': 09661_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Micranthes_pensylvanica '3933': 09662_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Micranthes_virginiensis '3934': 09663_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Mitella_diphylla '3935': 09664_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Mitella_nuda '3936': 09665_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Saxifraga_aizoides '3937': 09666_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Saxifraga_bronchialis '3938': 09667_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Saxifraga_granulata '3939': 09668_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Saxifraga_oppositifolia '3940': 09669_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Saxifraga_paniculata '3941': 09670_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Saxifraga_tricuspidata '3942': 09671_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Tellima_grandiflora '3943': 09672_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Tiarella_cordifolia '3944': 09673_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Tiarella_trifoliata '3945': 09674_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Tolmiea_menziesii '3946': 09675_Plantae_Tracheophyta_Magnoliopsida_Saxifragales_Saxifragaceae_Whipplea_modesta '3947': 09676_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Calystegia_macrostegia '3948': 09677_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Calystegia_sepium '3949': 09678_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Calystegia_silvatica '3950': 09679_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Calystegia_soldanella '3951': 09680_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Convolvulus_althaeoides '3952': 09681_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Convolvulus_arvensis '3953': 09682_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Convolvulus_equitans '3954': 09683_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Cuscuta_californica '3955': 09684_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Cuscuta_gronovii '3956': 09685_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Cuscuta_pacifica '3957': 09686_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Dichondra_carolinensis '3958': 09687_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Dichondra_repens '3959': 09688_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Distimake_dissectus '3960': 09689_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Evolvulus_alsinoides '3961': 09690_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Evolvulus_sericeus '3962': 09691_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_alba '3963': 09692_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_batatas '3964': 09693_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_cairica '3965': 09694_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_carnea '3966': 09695_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_coccinea '3967': 09696_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_cordatotriloba '3968': 09697_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_hederacea '3969': 09698_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_hederifolia '3970': 09699_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_imperati '3971': 09700_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_indica '3972': 09701_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_lacunosa '3973': 09702_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_obscura '3974': 09703_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_pandurata '3975': 09704_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_pes-caprae '3976': 09705_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_purpurea '3977': 09706_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_quamoclit '3978': 09707_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_sagittata '3979': 09708_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Ipomoea_triloba '3980': 09709_Plantae_Tracheophyta_Magnoliopsida_Solanales_Convolvulaceae_Jacquemontia_tamnifolia '3981': 09710_Plantae_Tracheophyta_Magnoliopsida_Solanales_Hydroleaceae_Hydrolea_ovata '3982': 09711_Plantae_Tracheophyta_Magnoliopsida_Solanales_Montiniaceae_Montinia_caryophyllacea '3983': 09712_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Alkekengi_officinarum '3984': 09713_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Atropa_belladonna '3985': 09714_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Capsicum_annuum '3986': 09715_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Datura_discolor '3987': 09716_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Datura_inoxia '3988': 09717_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Datura_stramonium '3989': 09718_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Datura_wrightii '3990': 09719_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Hyoscyamus_niger '3991': 09720_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Lycium_andersonii '3992': 09721_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Lycium_barbarum '3993': 09722_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Lycium_carolinianum '3994': 09723_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Lycium_ferocissimum '3995': 09724_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Nicandra_physalodes '3996': 09725_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Nicotiana_glauca '3997': 09726_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Nicotiana_obtusifolia '3998': 09727_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Physalis_angulata '3999': 09728_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Physalis_crassifolia '4000': 09729_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Physalis_heterophylla '4001': 09730_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Physalis_peruviana '4002': 09731_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Quincula_lobata '4003': 09732_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_americanum '4004': 09733_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_carolinense '4005': 09734_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_chenopodioides '4006': 09735_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_dimidiatum '4007': 09736_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_diphyllum '4008': 09737_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_douglasii '4009': 09738_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_dulcamara '4010': 09739_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_elaeagnifolium '4011': 09740_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_emulans '4012': 09741_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_erianthum '4013': 09742_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_laciniatum '4014': 09743_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_lycopersicum '4015': 09744_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_mauritianum '4016': 09745_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_nigrum '4017': 09746_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_pseudocapsicum '4018': 09747_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_rostratum '4019': 09748_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_tuberosum '4020': 09749_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_umbelliferum '4021': 09750_Plantae_Tracheophyta_Magnoliopsida_Solanales_Solanaceae_Solanum_xanti '4022': 09751_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Ampelopsis_arborea '4023': 09752_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Ampelopsis_cordata '4024': 09753_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Ampelopsis_glandulosa '4025': 09754_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Cayratia_japonica '4026': 09755_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Cissus_trifoliata '4027': 09756_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Parthenocissus_inserta '4028': 09757_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Parthenocissus_quinquefolia '4029': 09758_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Parthenocissus_tricuspidata '4030': 09759_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Vitis_aestivalis '4031': 09760_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Vitis_arizonica '4032': 09761_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Vitis_californica '4033': 09762_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Vitis_mustangensis '4034': 09763_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Vitis_riparia '4035': 09764_Plantae_Tracheophyta_Magnoliopsida_Vitales_Vitaceae_Vitis_rotundifolia '4036': 09765_Plantae_Tracheophyta_Magnoliopsida_Zygophyllales_Krameriaceae_Krameria_bicolor '4037': 09766_Plantae_Tracheophyta_Magnoliopsida_Zygophyllales_Krameriaceae_Krameria_erecta '4038': 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09788_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Juniperus_scopulorum '4060': 09789_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Juniperus_virginiana '4061': 09790_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Sequoia_sempervirens '4062': 09791_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Sequoiadendron_giganteum '4063': 09792_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Taxodium_ascendens '4064': 09793_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Taxodium_distichum '4065': 09794_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Taxodium_mucronatum '4066': 09795_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Thuja_occidentalis '4067': 09796_Plantae_Tracheophyta_Pinopsida_Pinales_Cupressaceae_Thuja_plicata '4068': 09797_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Abies_alba '4069': 09798_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Abies_balsamea '4070': 09799_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Abies_concolor '4071': 09800_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Abies_grandis '4072': 09801_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Abies_lasiocarpa '4073': 09802_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Abies_sibirica '4074': 09803_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Larix_decidua '4075': 09804_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Larix_laricina '4076': 09805_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Larix_occidentalis '4077': 09806_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Larix_sibirica '4078': 09807_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_abies '4079': 09808_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_engelmannii '4080': 09809_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_glauca '4081': 09810_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_mariana '4082': 09811_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_obovata '4083': 09812_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_pungens '4084': 09813_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_rubens '4085': 09814_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Picea_sitchensis '4086': 09815_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_albicaulis '4087': 09816_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_banksiana '4088': 09817_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_cembroides '4089': 09818_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_contorta '4090': 09819_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_coulteri '4091': 09820_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_echinata '4092': 09821_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_edulis '4093': 09822_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_elliottii '4094': 09823_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_flexilis '4095': 09824_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_jeffreyi '4096': 09825_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_lambertiana '4097': 09826_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_longaeva '4098': 09827_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_monophylla '4099': 09828_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_monticola '4100': 09829_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_nigra '4101': 09830_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_palustris '4102': 09831_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_pinaster '4103': 09832_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_pinea '4104': 09833_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_ponderosa '4105': 09834_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_radiata '4106': 09835_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_resinosa '4107': 09836_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_rigida '4108': 09837_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_sabiniana '4109': 09838_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_sibirica '4110': 09839_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_strobus '4111': 09840_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_sylvestris '4112': 09841_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_taeda '4113': 09842_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_torreyana '4114': 09843_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pinus_virginiana '4115': 09844_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Pseudotsuga_menziesii '4116': 09845_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Tsuga_canadensis '4117': 09846_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Tsuga_heterophylla '4118': 09847_Plantae_Tracheophyta_Pinopsida_Pinales_Pinaceae_Tsuga_mertensiana '4119': 09848_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Dacrycarpus_dacrydioides '4120': 09849_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Dacrydium_cupressinum '4121': 09850_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Phyllocladus_alpinus '4122': 09851_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Phyllocladus_trichomanoides '4123': 09852_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Podocarpus_laetus '4124': 09853_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Podocarpus_totara '4125': 09854_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Prumnopitys_ferruginea '4126': 09855_Plantae_Tracheophyta_Pinopsida_Pinales_Podocarpaceae_Prumnopitys_taxifolia '4127': 09856_Plantae_Tracheophyta_Pinopsida_Pinales_Taxaceae_Taxus_baccata '4128': 09857_Plantae_Tracheophyta_Pinopsida_Pinales_Taxaceae_Taxus_brevifolia '4129': 09858_Plantae_Tracheophyta_Pinopsida_Pinales_Taxaceae_Taxus_canadensis '4130': 09859_Plantae_Tracheophyta_Pinopsida_Pinales_Taxaceae_Torreya_californica '4131': 09860_Plantae_Tracheophyta_Polypodiopsida_Cyatheales_Cyatheaceae_Cyathea_dealbata '4132': 09861_Plantae_Tracheophyta_Polypodiopsida_Cyatheales_Cyatheaceae_Cyathea_medullaris '4133': 09862_Plantae_Tracheophyta_Polypodiopsida_Cyatheales_Cyatheaceae_Cyathea_smithii '4134': 09863_Plantae_Tracheophyta_Polypodiopsida_Cyatheales_Dicksoniaceae_Dicksonia_antarctica '4135': 09864_Plantae_Tracheophyta_Polypodiopsida_Cyatheales_Dicksoniaceae_Dicksonia_fibrosa '4136': 09865_Plantae_Tracheophyta_Polypodiopsida_Cyatheales_Dicksoniaceae_Dicksonia_squarrosa '4137': 09866_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_arvense '4138': 09867_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_fluviatile '4139': 09868_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_hyemale '4140': 09869_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_laevigatum '4141': 09870_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_pratense '4142': 09871_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_ramosissimum '4143': 09872_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_scirpoides '4144': 09873_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_sylvaticum '4145': 09874_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_telmateia '4146': 09875_Plantae_Tracheophyta_Polypodiopsida_Equisetales_Equisetaceae_Equisetum_variegatum '4147': 09876_Plantae_Tracheophyta_Polypodiopsida_Gleicheniales_Gleicheniaceae_Dicranopteris_linearis '4148': 09877_Plantae_Tracheophyta_Polypodiopsida_Gleicheniales_Gleicheniaceae_Gleichenia_dicarpa '4149': 09878_Plantae_Tracheophyta_Polypodiopsida_Gleicheniales_Gleicheniaceae_Sticherus_cunninghamii '4150': 09879_Plantae_Tracheophyta_Polypodiopsida_Hymenophyllales_Hymenophyllaceae_Hymenophyllum_demissum '4151': 09880_Plantae_Tracheophyta_Polypodiopsida_Hymenophyllales_Hymenophyllaceae_Hymenophyllum_flabellatum '4152': 09881_Plantae_Tracheophyta_Polypodiopsida_Hymenophyllales_Hymenophyllaceae_Hymenophyllum_nephrophyllum '4153': 09882_Plantae_Tracheophyta_Polypodiopsida_Hymenophyllales_Hymenophyllaceae_Trichomanes_venosum '4154': 09883_Plantae_Tracheophyta_Polypodiopsida_Ophioglossales_Ophioglossaceae_Botrychium_lunaria '4155': 09884_Plantae_Tracheophyta_Polypodiopsida_Ophioglossales_Ophioglossaceae_Botrypus_virginianus '4156': 09885_Plantae_Tracheophyta_Polypodiopsida_Ophioglossales_Ophioglossaceae_Sceptridium_dissectum '4157': 09886_Plantae_Tracheophyta_Polypodiopsida_Ophioglossales_Ophioglossaceae_Sceptridium_multifidum '4158': 09887_Plantae_Tracheophyta_Polypodiopsida_Osmundales_Osmundaceae_Leptopteris_hymenophylloides '4159': 09888_Plantae_Tracheophyta_Polypodiopsida_Osmundales_Osmundaceae_Osmunda_claytoniana '4160': 09889_Plantae_Tracheophyta_Polypodiopsida_Osmundales_Osmundaceae_Osmunda_regalis '4161': 09890_Plantae_Tracheophyta_Polypodiopsida_Osmundales_Osmundaceae_Osmundastrum_cinnamomeum '4162': 09891_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_appendiculatum '4163': 09892_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_bulbiferum '4164': 09893_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_ceterach '4165': 09894_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_flabellifolium '4166': 09895_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_flaccidum '4167': 09896_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_gracillimum '4168': 09897_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_hookerianum '4169': 09898_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_montanum '4170': 09899_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_nidus '4171': 09900_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_oblongifolium '4172': 09901_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_platyneuron '4173': 09902_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_polyodon '4174': 09903_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_rhizophyllum '4175': 09904_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_ruta-muraria '4176': 09905_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_scolopendrium '4177': 09906_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_trichomanes '4178': 09907_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Aspleniaceae_Asplenium_viride '4179': 09908_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Athyriaceae_Athyrium_angustum '4180': 09909_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Athyriaceae_Athyrium_filix-femina '4181': 09910_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Athyriaceae_Deparia_acrostichoides '4182': 09911_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Blechnum_chambersii '4183': 09912_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Blechnum_filiforme '4184': 09913_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Blechnum_novae-zelandiae '4185': 09914_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Blechnum_parrisiae '4186': 09915_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Blechnum_penna-marina '4187': 09916_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Blechnum_procerum '4188': 09917_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Cranfillia_fluviatilis '4189': 09918_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Lomaria_discolor '4190': 09919_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Struthiopteris_spicant '4191': 09920_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Telmatoblechnum_serrulatum '4192': 09921_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Woodwardia_areolata '4193': 09922_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Woodwardia_fimbriata '4194': 09923_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Blechnaceae_Woodwardia_virginica '4195': 09924_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Cystopteridaceae_Cystopteris_bulbifera '4196': 09925_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Cystopteridaceae_Cystopteris_fragilis '4197': 09926_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Cystopteridaceae_Cystopteris_protrusa '4198': 09927_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Cystopteridaceae_Gymnocarpium_dryopteris '4199': 09928_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Dennstaedtia_punctilobula '4200': 09929_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Histiopteris_incisa '4201': 09930_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Hypolepis_ambigua '4202': 09931_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Paesia_scaberula '4203': 09932_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Pteridium_aquilinum '4204': 09933_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Pteridium_esculentum '4205': 09934_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dennstaedtiaceae_Pteridium_pinetorum '4206': 09935_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Diplaziopsidaceae_Homalosorus_pycnocarpos '4207': 09936_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Cyrtomium_falcatum '4208': 09937_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_arguta '4209': 09938_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_carthusiana '4210': 09939_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_cristata '4211': 09940_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_expansa '4212': 09941_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_filix-mas '4213': 09942_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_fragrans '4214': 09943_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_intermedia '4215': 09944_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Dryopteris_marginalis '4216': 09945_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Polystichum_acrostichoides '4217': 09946_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Polystichum_lonchitis '4218': 09947_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Polystichum_munitum '4219': 09948_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Polystichum_neozelandicum '4220': 09949_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Polystichum_vestitum '4221': 09950_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Dryopteridaceae_Rumohra_adiantiformis '4222': 09951_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Nephrolepidaceae_Nephrolepis_cordifolia '4223': 09952_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Onocleaceae_Matteuccia_struthiopteris '4224': 09953_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Onocleaceae_Onoclea_sensibilis '4225': 09954_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Microsorum_pustulatum '4226': 09955_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Microsorum_scandens '4227': 09956_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Notogrammitis_heterophylla '4228': 09957_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Phlebodium_aureum '4229': 09958_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Pleopeltis_michauxiana '4230': 09959_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Polypodium_californicum '4231': 09960_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Polypodium_glycyrrhiza '4232': 09961_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Polypodium_scouleri '4233': 09962_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Polypodium_virginianum '4234': 09963_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Polypodium_vulgare '4235': 09964_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Polypodiaceae_Pyrrosia_eleagnifolia '4236': 09965_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Acrostichum_danaeifolium '4237': 09966_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Adiantum_aleuticum '4238': 09967_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Adiantum_capillus-veneris '4239': 09968_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Adiantum_cunninghamii '4240': 09969_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Adiantum_hispidulum '4241': 09970_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Adiantum_jordanii '4242': 09971_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Adiantum_pedatum '4243': 09972_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Aspidotis_densa '4244': 09973_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Astrolepis_sinuata '4245': 09974_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Cryptogramma_acrostichoides '4246': 09975_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Myriopteris_alabamensis '4247': 09976_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Myriopteris_aurea '4248': 09977_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Myriopteris_parryi '4249': 09978_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_andromedifolia '4250': 09979_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_atropurpurea '4251': 09980_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_glabella '4252': 09981_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_mucronata '4253': 09982_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pellaea_rotundifolia '4254': 09983_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pentagramma_triangularis '4255': 09984_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pteris_cretica '4256': 09985_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pteris_macilenta '4257': 09986_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pteris_tremula '4258': 09987_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Pteridaceae_Pteris_vittata '4259': 09988_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Parathelypteris_noveboracensis '4260': 09989_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Phegopteris_connectilis '4261': 09990_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Phegopteris_hexagonoptera '4262': 09991_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Pneumatopteris_pennigera '4263': 09992_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Thelypteridaceae_Thelypteris_palustris '4264': 09993_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Woodsiaceae_Woodsia_ilvensis '4265': 09994_Plantae_Tracheophyta_Polypodiopsida_Polypodiales_Woodsiaceae_Woodsia_obtusa '4266': 09995_Plantae_Tracheophyta_Polypodiopsida_Psilotales_Psilotaceae_Psilotum_nudum '4267': 09996_Plantae_Tracheophyta_Polypodiopsida_Psilotales_Psilotaceae_Tmesipteris_elongata '4268': 09997_Plantae_Tracheophyta_Polypodiopsida_Salviniales_Salviniaceae_Azolla_filiculoides '4269': 09998_Plantae_Tracheophyta_Polypodiopsida_Salviniales_Salviniaceae_Salvinia_minima '4270': 09999_Plantae_Tracheophyta_Polypodiopsida_Schizaeales_Lygodiaceae_Lygodium_japonicum splits: - name: train num_bytes: 25180455748.65 num_examples: 213550 download_size: 7778993107 dataset_size: 25180455748.65 --- # Dataset Card for "inat_2021_train_mini_plantae" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

one-sec-cv12/chunk_170

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 23363195760.375 num_examples: 243245 download_size: 21013363110 dataset_size: 23363195760.375 --- # Dataset Card for "chunk_170" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

james-burton/OrientalMuseum_min6-num

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eg5169 '1211': eg517 '1212': eg518 '1213': eg5214 '1214': eg5227 '1215': eg5231 '1216': eg5253 '1217': eg5256 '1218': eg5258 '1219': eg5259 '1220': eg5260 '1221': eg5270 '1222': eg5273 '1223': eg5278 '1224': eg5288 '1225': eg5329 '1226': eg535 '1227': eg5382 '1228': eg577 '1229': eg578 '1230': eg5809 '1231': eg608 '1232': eg609 '1233': eg615 '1234': eg6360 '1235': eg6417 '1236': eg6419 '1237': eg6435 '1238': eg6449 '1239': eg6739 '1240': eg6767 '1241': eg6802 '1242': eg6814 '1243': eg6822 '1244': eg6883 '1245': eg6897 '1246': eg6955 '1247': eg6974 '1248': eg718 '1249': eg724 '1250': eg726 '1251': eg727 '1252': eg732 '1253': eg733 '1254': eg779 '1255': eg898 '1256': eg901 '1257': eg909 '1258': eg913 '1259': eg914 - name: file dtype: string - name: image dtype: image - name: root dtype: string - name: description dtype: string - name: object_name dtype: string - name: other_name dtype: string - name: material dtype: string - name: production.period dtype: string - name: production.place dtype: string splits: - name: train num_bytes: 923492132.4197494 num_examples: 9456 - name: validation num_bytes: 305527605.0268552 num_examples: 2225 - name: test num_bytes: 250516356.24239534 num_examples: 2226 download_size: 1632705825 dataset_size: 1479536093.689 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

app_reviews

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - sentiment-scoring pretty_name: AppReviews dataset_info: features: - name: package_name dtype: string - name: review dtype: string - name: date dtype: string - name: star dtype: int8 splits: - name: train num_bytes: 32768731 num_examples: 288065 download_size: 13207727 dataset_size: 32768731 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Home Page](https://github.com/sealuzh/user_quality) - **Repository:** [Repo Link](https://github.com/sealuzh/user_quality) - **Paper:** [Link](https://giograno.me/assets/pdf/workshop/wama17.pdf) - **Leaderboard: - **Point of Contact:** [Darshan Gandhi](darshangandhi1151@gmail.com) ### Dataset Summary It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews (extracted with specific text mining approaches) ### Supported Tasks and Leaderboards The dataset we provide comprises 395 different apps from F-Droid repository, including code quality indicators of 629 versions of these apps. It also encloses app reviews related to each of these versions, which have been automatically categorized classifying types of user feedback from a software maintenance and evolution perspective. ### Languages The dataset is a monolingual dataset which has the messages English. ## Dataset Structure ### Data Instances The dataset consists of a message in English. {'package_name': 'com.mantz_it.rfanalyzer', 'review': "Great app! The new version now works on my Bravia Android TV which is great as it's right by my rooftop aerial cable. The scan feature would be useful...any ETA on when this will be available? Also the option to import a list of bookmarks e.g. from a simple properties file would be useful.", 'date': 'October 12 2016', 'star': 4} ### Data Fields * package_name : Name of the Software Application Package * review : Message of the user * date : date when the user posted the review * star : rating provied by the user for the application ### Data Splits There is training data, with a total of : 288065 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset With the help of this dataset one can try to understand more about software applications and what are the views and opinions of the users about them. This helps to understand more about which type of software applications are prefeered by the users and how do these applications facilitate the user to help them solve their problems and issues. ### Discussion of Biases The reviews are only for applications which are in the open-source software applications, the other sectors have not been considered here ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Giovanni Grano - (University of Zurich), Sebastiano Panichella - (University of Zurich), Andrea di Sorbo - (University of Sannio) ### Licensing Information [More Information Needed] ### Citation Information @InProceedings{Zurich Open Repository and Archive:dataset, title = {Software Applications User Reviews}, authors={Grano, Giovanni; Di Sorbo, Andrea; Mercaldo, Francesco; Visaggio, Corrado A; Canfora, Gerardo; Panichella, Sebastiano}, year={2017} } ### Contributions Thanks to [@darshan-gandhi](https://github.com/darshan-gandhi) for adding this dataset.

jlbaker361/anime_faces_dim_128_50k

--- dataset_info: features: - name: image dtype: image - name: split dtype: string - name: src dtype: string - name: style dtype: string splits: - name: train num_bytes: 1348590698.0 num_examples: 50000 download_size: 1346915159 dataset_size: 1348590698.0 --- # Dataset Card for "anime_faces_dim_128_50k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

JeffersonMusic/MichaelJackson

--- license: apache-2.0 ---

open-llm-leaderboard/details_ibivibiv__orthorus-125b-moe-v2

--- pretty_name: Evaluation run of ibivibiv/orthorus-125b-moe-v2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ibivibiv/orthorus-125b-moe-v2](https://huggingface.co/ibivibiv/orthorus-125b-moe-v2)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ibivibiv__orthorus-125b-moe-v2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-06T15:03:25.094734](https://huggingface.co/datasets/open-llm-leaderboard/details_ibivibiv__orthorus-125b-moe-v2/blob/main/results_2024-02-06T15-03-25.094734.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.22895924430194997,\n\ \ \"acc_stderr\": 0.029775044140996448,\n \"acc_norm\": 0.22892897024882422,\n\ \ \"acc_norm_stderr\": 0.03055773036734348,\n \"mc1\": 0.23745410036719705,\n\ \ \"mc1_stderr\": 0.014896277441041845,\n \"mc2\": 0.48491700399143933,\n\ \ \"mc2_stderr\": 0.016301526988248338\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.22184300341296928,\n \"acc_stderr\": 0.012141659068147884,\n\ \ \"acc_norm\": 0.2627986348122867,\n \"acc_norm_stderr\": 0.012862523175351333\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2591117307309301,\n\ \ \"acc_stderr\": 0.004372516060164754,\n \"acc_norm\": 0.2517426807408883,\n\ \ \"acc_norm_stderr\": 0.004331271717773866\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2740740740740741,\n\ \ \"acc_stderr\": 0.038532548365520024,\n \"acc_norm\": 0.2740740740740741,\n\ \ \"acc_norm_stderr\": 0.038532548365520024\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\ \ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.3,\n\ \ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \ \ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.2528301886792453,\n \"acc_stderr\": 0.026749899771241238,\n\ \ \"acc_norm\": 0.2528301886792453,\n \"acc_norm_stderr\": 0.026749899771241238\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536955,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536955\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\"\ : 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n\ \ \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n\ \ \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n\ \ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n\ \ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.21164021164021163,\n \"acc_stderr\": 0.021037331505262883,\n \"\ acc_norm\": 0.21164021164021163,\n \"acc_norm_stderr\": 0.021037331505262883\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15873015873015872,\n\ \ \"acc_stderr\": 0.03268454013011744,\n \"acc_norm\": 0.15873015873015872,\n\ \ \"acc_norm_stderr\": 0.03268454013011744\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"\ acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n \"\ acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\ acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.20207253886010362,\n \"acc_stderr\": 0.02897908979429673,\n\ \ \"acc_norm\": 0.20207253886010362,\n \"acc_norm_stderr\": 0.02897908979429673\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.24615384615384617,\n \"acc_stderr\": 0.02184086699042309,\n\ \ \"acc_norm\": 0.24615384615384617,\n \"acc_norm_stderr\": 0.02184086699042309\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \ \ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\ \ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"\ acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"\ acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"\ acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\ \ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.2645739910313901,\n\ \ \"acc_stderr\": 0.02960510321703834,\n \"acc_norm\": 0.2645739910313901,\n\ \ \"acc_norm_stderr\": 0.02960510321703834\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.183206106870229,\n \"acc_stderr\": 0.03392770926494733,\n\ \ \"acc_norm\": 0.183206106870229,\n \"acc_norm_stderr\": 0.03392770926494733\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\ acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2962962962962963,\n\ \ \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.2962962962962963,\n\ \ \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n\ \ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\ \ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.21359223300970873,\n \"acc_stderr\": 0.04058042015646034,\n\ \ \"acc_norm\": 0.21359223300970873,\n \"acc_norm_stderr\": 0.04058042015646034\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2777777777777778,\n\ \ \"acc_stderr\": 0.02934311479809447,\n \"acc_norm\": 0.2777777777777778,\n\ \ \"acc_norm_stderr\": 0.02934311479809447\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2388250319284802,\n\ \ \"acc_stderr\": 0.015246803197398675,\n \"acc_norm\": 0.2388250319284802,\n\ \ \"acc_norm_stderr\": 0.015246803197398675\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\ \ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1832797427652733,\n\ \ \"acc_stderr\": 0.021974198848265805,\n \"acc_norm\": 0.1832797427652733,\n\ \ \"acc_norm_stderr\": 0.021974198848265805\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\ \ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \ \ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\ \ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.25163398692810457,\n \"acc_stderr\": 0.01755581809132226,\n \ \ \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.01755581809132226\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.20909090909090908,\n\ \ \"acc_stderr\": 0.03895091015724136,\n \"acc_norm\": 0.20909090909090908,\n\ \ \"acc_norm_stderr\": 0.03895091015724136\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.18775510204081633,\n \"acc_stderr\": 0.02500025603954621,\n\ \ \"acc_norm\": 0.18775510204081633,\n \"acc_norm_stderr\": 0.02500025603954621\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24378109452736318,\n\ \ \"acc_stderr\": 0.03036049015401465,\n \"acc_norm\": 0.24378109452736318,\n\ \ \"acc_norm_stderr\": 0.03036049015401465\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.2891566265060241,\n\ \ \"acc_stderr\": 0.03529486801511115,\n \"acc_norm\": 0.2891566265060241,\n\ \ \"acc_norm_stderr\": 0.03529486801511115\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.03188578017686399,\n\ \ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.03188578017686399\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23745410036719705,\n\ \ \"mc1_stderr\": 0.014896277441041845,\n \"mc2\": 0.48491700399143933,\n\ \ \"mc2_stderr\": 0.016301526988248338\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.49329123914759276,\n \"acc_stderr\": 0.014051220692330349\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/ibivibiv/orthorus-125b-moe-v2 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|arc:challenge|25_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-06T15-03-25.094734.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|gsm8k|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hellaswag|10_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-06T15-03-25.094734.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-management|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-06T15-03-25.094734.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|truthfulqa:mc|0_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-06T15-03-25.094734.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_06T15_03_25.094734 path: - '**/details_harness|winogrande|5_2024-02-06T15-03-25.094734.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-06T15-03-25.094734.parquet' - config_name: results data_files: - split: 2024_02_06T15_03_25.094734 path: - results_2024-02-06T15-03-25.094734.parquet - split: latest path: - results_2024-02-06T15-03-25.094734.parquet --- # Dataset Card for Evaluation run of ibivibiv/orthorus-125b-moe-v2  Dataset automatically created during the evaluation run of model [ibivibiv/orthorus-125b-moe-v2](https://huggingface.co/ibivibiv/orthorus-125b-moe-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ibivibiv__orthorus-125b-moe-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-06T15:03:25.094734](https://huggingface.co/datasets/open-llm-leaderboard/details_ibivibiv__orthorus-125b-moe-v2/blob/main/results_2024-02-06T15-03-25.094734.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "acc": 0.22895924430194997, "acc_stderr": 0.029775044140996448, "acc_norm": 0.22892897024882422, "acc_norm_stderr": 0.03055773036734348, "mc1": 0.23745410036719705, "mc1_stderr": 0.014896277441041845, "mc2": 0.48491700399143933, "mc2_stderr": 0.016301526988248338 }, "harness|arc:challenge|25": { "acc": 0.22184300341296928, "acc_stderr": 0.012141659068147884, "acc_norm": 0.2627986348122867, "acc_norm_stderr": 0.012862523175351333 }, "harness|hellaswag|10": { "acc": 0.2591117307309301, "acc_stderr": 0.004372516060164754, "acc_norm": 0.2517426807408883, "acc_norm_stderr": 0.004331271717773866 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.22, "acc_stderr": 0.04163331998932268, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932268 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.2740740740740741, "acc_stderr": 0.038532548365520024, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.038532548365520024 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.17763157894736842, "acc_stderr": 0.031103182383123398, "acc_norm": 0.17763157894736842, "acc_norm_stderr": 0.031103182383123398 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.2528301886792453, "acc_stderr": 0.026749899771241238, "acc_norm": 0.2528301886792453, "acc_norm_stderr": 0.026749899771241238 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.18, "acc_stderr": 0.038612291966536955, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536955 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.20809248554913296, "acc_stderr": 0.030952890217749874, "acc_norm": 0.20809248554913296, "acc_norm_stderr": 0.030952890217749874 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.26382978723404255, "acc_stderr": 0.028809989854102973, "acc_norm": 0.26382978723404255, "acc_norm_stderr": 0.028809989854102973 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813365, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813365 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.21164021164021163, "acc_stderr": 0.021037331505262883, "acc_norm": 0.21164021164021163, "acc_norm_stderr": 0.021037331505262883 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.15873015873015872, "acc_stderr": 0.03268454013011744, "acc_norm": 0.15873015873015872, "acc_norm_stderr": 0.03268454013011744 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.1774193548387097, "acc_stderr": 0.02173254068932927, "acc_norm": 0.1774193548387097, "acc_norm_stderr": 0.02173254068932927 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.15270935960591134, "acc_stderr": 0.02530890453938063, "acc_norm": 0.15270935960591134, "acc_norm_stderr": 0.02530890453938063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.17676767676767677, "acc_stderr": 0.027178752639044915, "acc_norm": 0.17676767676767677, "acc_norm_stderr": 0.027178752639044915 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.20207253886010362, "acc_stderr": 0.02897908979429673, "acc_norm": 0.20207253886010362, "acc_norm_stderr": 0.02897908979429673 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.24615384615384617, "acc_stderr": 0.02184086699042309, "acc_norm": 0.24615384615384617, "acc_norm_stderr": 0.02184086699042309 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2111111111111111, "acc_stderr": 0.024882116857655075, "acc_norm": 0.2111111111111111, "acc_norm_stderr": 0.024882116857655075 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.21008403361344538, "acc_stderr": 0.026461398717471874, "acc_norm": 0.21008403361344538, "acc_norm_stderr": 0.026461398717471874 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.1986754966887417, "acc_stderr": 0.03257847384436776, "acc_norm": 0.1986754966887417, "acc_norm_stderr": 0.03257847384436776 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.1926605504587156, "acc_stderr": 0.016909276884936094, "acc_norm": 0.1926605504587156, "acc_norm_stderr": 0.016909276884936094 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.1527777777777778, "acc_stderr": 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"acc_norm": 0.20909090909090908, "acc_norm_stderr": 0.03895091015724136 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.18775510204081633, "acc_stderr": 0.02500025603954621, "acc_norm": 0.18775510204081633, "acc_norm_stderr": 0.02500025603954621 }, "harness|hendrycksTest-sociology|5": { "acc": 0.24378109452736318, "acc_stderr": 0.03036049015401465, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-virology|5": { "acc": 0.2891566265060241, "acc_stderr": 0.03529486801511115, "acc_norm": 0.2891566265060241, "acc_norm_stderr": 0.03529486801511115 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03188578017686399, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03188578017686399 }, "harness|truthfulqa:mc|0": { "mc1": 0.23745410036719705, "mc1_stderr": 0.014896277441041845, "mc2": 0.48491700399143933, "mc2_stderr": 0.016301526988248338 }, "harness|winogrande|5": { "acc": 0.49329123914759276, "acc_stderr": 0.014051220692330349 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

HebArabNlpProject/ArabicSentimentDataSet

--- license: cc-by-4.0 ---

projetosoclts/Vicente

--- license: openrail ---

freshpearYoon/vr_train_free_63

--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sampling_rate dtype: int64 - name: filename dtype: string - name: NumOfUtterance dtype: int64 - name: text dtype: string - name: samplingrate dtype: int64 - name: begin_time dtype: float64 - name: end_time dtype: float64 - name: speaker_id dtype: string - name: directory dtype: string splits: - name: train num_bytes: 7111025704 num_examples: 10000 download_size: 1184721349 dataset_size: 7111025704 configs: - config_name: default data_files: - split: train path: data/train-* ---

gayanin/pubmed-abstracts-noised-with-prob-dist-common

--- dataset_info: config_name: prob-01 features: - name: refs dtype: string - name: trans dtype: string splits: - name: train num_bytes: 18087365 num_examples: 74724 - name: test num_bytes: 2317206 num_examples: 9341 - name: validation num_bytes: 2379842 num_examples: 9341 download_size: 12754200 dataset_size: 22784413 configs: - config_name: prob-01 data_files: - split: train path: prob-01/train-* - split: test path: prob-01/test-* - split: validation path: prob-01/validation-* ---

PDScience/CollegeAuditData

--- license: apache-2.0 task_categories: - question-answering - feature-extraction - text-generation language: - en tags: - finance pretty_name: College Audit Data size_categories: - 100K<n<1M --- ### Dataset Description: College and University Financial Audits and Text Dataset #### **Overview** The College and University Financial Audits and Text Dataset is a dataset that combines audited financial statement texts with key extracted financial data from various colleges and universities. This dataset stands out for its emphasis on long context input data, making it especially useful for training large language models to extract data from long input texts. #### **Dataset Composition** This dataset contains over 125,000 entries with the following variables: 1. **AuditText**: The complete text of the audited financial statement. These can be very long input texts. 2. **fiscalYear**: The fiscal year of the reported data. 3. **auditYear**: The year in which the audit was performed. 4. **value**: Value of the variable in fiscal year==fiscalYear as reported in the audit text. 5. **variable**: Variable extracted from the audit text. #### **Key Features and Use Cases** - **Long Context Input Data**: The inclusion of complete audit texts offers a unique opportunity for training and testing LLMs on long-form financial documents. - **Model Training and fine-tuning**: Ideal for fine-tuning AI models for data extraction from long textual inputs, such as in-depth financial reports. - **Financial and Educational Research**: Facilitates comprehensive analysis in the domains of finance and education, allowing for nuanced insights into the financial workings of higher education institutions. #### **Suggested prompt format for training** **Prompt**: "Extract the {variable} for fiscal year {fiscalYear} from the following text: \n\n {AuditText}" **Response**: "{value}"

tner/tweebank_ner

--- language: - en license: - other multilinguality: - monolingual size_categories: - 1k<10K task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: TweeBank NER --- # Dataset Card for "tner/tweebank_ner" ## Dataset Description - **Repository:** [T-NER](https://github.com/asahi417/tner) - **Paper:** [https://arxiv.org/abs/2201.07281](https://arxiv.org/abs/2201.07281) - **Dataset:** TweeBank NER - **Domain:** Twitter - **Number of Entity:** 4 ### Dataset Summary TweeBank NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project. - Entity Types: `LOC`, `MISC`, `PER`, `ORG` ## Dataset Structure ### Data Instances An example of `train` looks as follows. ``` { 'tokens': ['RT', '@USER2362', ':', 'Farmall', 'Heart', 'Of', 'The', 'Holidays', 'Tabletop', 'Christmas', 'Tree', 'With', 'Lights', 'And', 'Motion', 'URL1087', '#Holiday', '#Gifts'], 'tags': [8, 8, 8, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8] } ``` ### Label ID The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweebank_ner/raw/main/dataset/label.json). ```python { "B-LOC": 0, "B-MISC": 1, "B-ORG": 2, "B-PER": 3, "I-LOC": 4, "I-MISC": 5, "I-ORG": 6, "I-PER": 7, "O": 8 } ``` ### Data Splits | name |train|validation|test| |---------|----:|---------:|---:| |tweebank_ner | 1639| 710 |1201| ### Citation Information ``` @article{DBLP:journals/corr/abs-2201-07281, author = {Hang Jiang and Yining Hua and Doug Beeferman and Deb Roy}, title = {Annotating the Tweebank Corpus on Named Entity Recognition and Building {NLP} Models for Social Media Analysis}, journal = {CoRR}, volume = {abs/2201.07281}, year = {2022}, url = {https://arxiv.org/abs/2201.07281}, eprinttype = {arXiv}, eprint = {2201.07281}, timestamp = {Fri, 21 Jan 2022 13:57:15 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2201-07281.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

CorpuSlave/common-knowledge

--- license: cc-by-nc-sa-4.0 configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: doc_id dtype: string splits: - name: train num_bytes: 10650689076 num_examples: 6216545 download_size: 5826173649 dataset_size: 10650689076 ---

prachathai67k

--- annotations_creators: - found language_creators: - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - topic-classification paperswithcode_id: prachathai-67k pretty_name: prachathai67k dataset_info: features: - name: url dtype: string - name: date dtype: string - name: title dtype: string - name: body_text dtype: string - name: politics dtype: class_label: names: '0': neg '1': pos - name: human_rights dtype: class_label: names: '0': neg '1': pos - name: quality_of_life dtype: class_label: names: '0': neg '1': pos - name: international dtype: class_label: names: '0': neg '1': pos - name: social dtype: class_label: names: '0': neg '1': pos - name: environment dtype: class_label: names: '0': neg '1': pos - name: economics dtype: class_label: names: '0': neg '1': pos - name: culture dtype: class_label: names: '0': neg '1': pos - name: labor dtype: class_label: names: '0': neg '1': pos - name: national_security dtype: class_label: names: '0': neg '1': pos - name: ict dtype: class_label: names: '0': neg '1': pos - name: education dtype: class_label: names: '0': neg '1': pos config_name: prachathai67k splits: - name: train num_bytes: 865848436 num_examples: 54379 - name: validation num_bytes: 108641386 num_examples: 6721 - name: test num_bytes: 110034036 num_examples: 6789 download_size: 254240975 dataset_size: 1084523858 --- # Dataset Card for `prachathai67k` ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/PyThaiNLP/prachathai-67k - **Repository:** https://github.com/PyThaiNLP/prachathai-67k - **Paper:** - **Leaderboard:** - **Point of Contact:** https://github.com/PyThaiNLP/ ### Dataset Summary `prachathai-67k`: News Article Corpus and Multi-label Text Classificdation from Prachathai.com The `prachathai-67k` dataset was scraped from the news site [Prachathai](prachathai.com). We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. It contains 67,889 articles wtih 12 curated tags from August 24, 2004 to November 15, 2018. The dataset was originally scraped by [@lukkiddd](https://github.com/lukkiddd) and cleaned by [@cstorm125](https://github.com/cstorm125). Download the dataset [here](https://www.dropbox.com/s/fsxepdka4l2pr45/prachathai-67k.zip?dl=1). You can also see preliminary exploration in [exploration.ipynb](https://github.com/PyThaiNLP/prachathai-67k/blob/master/exploration.ipynb). This dataset is a part of [pyThaiNLP](https://github.com/PyThaiNLP/) Thai text [classification-benchmarks](https://github.com/PyThaiNLP/classification-benchmarks). For the benchmark, we selected the following tags with substantial volume that resemble **classifying types of articles**: * `การเมือง` - politics * `สิทธิมนุษยชน` - human_rights * `คุณภาพชีวิต` - quality_of_life * `ต่างประเทศ` - international * `สังคม` - social * `สิ่งแวดล้อม` - environment * `เศรษฐกิจ` - economics * `วัฒนธรรม` - culture * `แรงงาน` - labor * `ความมั่นคง` - national_security * `ไอซีที` - ict * `การศึกษา` - education ### Supported Tasks and Leaderboards multi-label text classification, language modeling ### Languages Thai ## Dataset Structure ### Data Instances {'body_text': '17 พ.ย. 2558 Blognone [1] รายงานว่า กลุ่มแฮคเกอร์ Anonymous ประกาศสงครามไซเบอร์กับกลุ่มหัวรุนแรงหลังจากกลุ่ม IS ออกมาประกาศว่าเป็นผู้อยู่เบื้องหลังการโจมตีกรุงปารีสในคืนวันศุกร์ที่ผ่านมา\n\n\nภาพในคลิปใน YouTube โฆษกของกลุ่มแฮคเกอร์สวมหน้ากากที่เป็นสัญลักษณ์ของกลุ่มได้ออกมาอ่านแถลงเป็นภาษาฝรั่งเศส มีใจความว่า จากการโจมตีของกลุ่ม IS ในกรุงปารีส กลุ่ม Anonymous ทั่วโลกจะตามล่ากลุ่ม IS เหมือนที่เคยทำตอนที่มีการโจมตีสำนักพิมพ์ Charlie Hebdo และครั้งนี้จะเป็นปฏิบัติการโจมตีครั้งใหญ่ที่สุดของกลุ่ม Anonymous เลย นอกจากนี้กลุ่ม Anonymous ยังแสดงความเสียใจต่อครอบครัวผู้สูญเสียในเหตุการณ์ครั้งนี้\nกลุ่ม Anonymous เคยประกาศสงครามกับกลุ่ม IS หลังจากการโจมตีสำนักพิมพ์ Charlie Hebdo ที่ฝรั่งเศสเมื่อต้นปีที่ผ่านมา ซึ่งครั้งนั้นกลุ่ม Anonymous อ้างว่าได้ระงับบัญชีผู้ใช้งานที่เกี่ยวข้องกับ IS ไปหลายพันบัญชี (อ่านรายละเอียดเพิ่มเติม จากBlognone ที่\xa0\xa0กลุ่มแฮคเกอร์ Anonymous ประกาศสงครามไซเบอร์ขอกวาดล้างพวก ISIS [2])', 'culture': 0, 'date': '2015-11-17 18:14', 'economics': 0, 'education': 0, 'environment': 0, 'human_rights': 0, 'ict': 1, 'international': 1, 'labor': 0, 'national_security': 0, 'politics': 0, 'quality_of_life': 0, 'social': 0, 'title': 'แฮคเกอร์ Anonymous ลั่นทำสงครามไซเบอร์ครั้งใหญ่สุดกับกลุ่ม IS', 'url': 'https://prachatai.com/print/62490'} {'body_text': 'แถลงการณ์\n\n\xa0\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์\n\n\xa0\n\nมหาวิทยาลัยธรรมศาสตร์ก่อตั้งขึ้นภายใต้แนวคิดการให้การศึกษากับประชาชนเพื่อสนับสนุนการปกครองระบอบประชาธิปไตย อีกทั้งยังเป็นสถาบันหนึ่งที่อยู่เคียงข้างประชาชนมาโดยตลอด\n\n\xa0\n\nสถานการณ์สังคมไทยปัจจุบันได้เกิดความขัดแย้งทางการเมือง ทางแนวคิด จนลุกลามเป็นวิกฤตการณ์อันหาทางออกได้ยากยิ่ง องค์กรนักศึกษามหาวิทยาลัยธรรมศาสตร์ขอร้องเรียนและเสนอแนะต่อทุกฝ่าย โดยยึดหลักแนวทางตามรัฐธรรมนูญแห่งราชอาณาจักรไทย พ.ศ. ๒๕๕๐ อันเป็นกฎหมายสูงสุดในการจัดการปกครองรัฐ ที่มีผลบังคับใช้อยู่ในปัจจุบันซึ่งผ่านการประชามติจากปวงชนชาวไทยเมื่อวันที่ ๑๙ สิงหาคม พ.ศ. ๒๕๕๐ แล้วดังต่อนี้\n\n\xa0\n\n๑.การชุมชมโดยสงบและปราศจากอาวุธย่อมได้รับการคุ้มครองตามรัฐธรรมนูญ แต่หากการชุมนุมและเคลื่อนไหวของกลุ่มใดๆ มีการละเมิดสิทธิและเสรีภาพของผู้อื่นหรือก่อให้เกิดความเสียหายต่อชีวิตและทรัพย์สินของบุคคลและส่วนรวมนั้น ไม่สามารถกระทำได้ การใช้ความรุนแรง การกระทำอุกอาจต่างๆ ทั้งต่อบุคคลและทรัพย์สิน การยั่วยุ ปลุกระดมเพื่อหวังผลในการปะทะต่อสู้ จึงควรได้รับการกล่าวโทษ\n\n\xa0\n\nดังนั้นทั้งกลุ่มพันธมิตรประชาชนเพื่อประชาธิปไตย (พธม.) และกลุ่มแนวร่วมประชาธิปไตยไม่เอาเผด็จการแห่งชาติ (นปช.) จึงควรยอมรับกระบวนการตามกฎหมาย และหากถูกกล่าวหาไม่ว่ากรณีใดๆ ก็ควรพิสูจน์ความบริสุทธิ์โดยใช้กระบวนการยุติธรรม และหากจะยังชุมนุมต่อไปก็ยังคงทำได้ภายใต้บทบัญญัติแห่งกฎหมาย\n\n\xa0\n\nองค์กรนักศึกษามหาวิทยาลัยธรรมศาสตร์ จึงร้องขอให้หน่วยงานต่างๆ ที่เกี่ยวข้องดำเนินการตามกระบวนการทางกฎหมายกับการกระทำที่ผิดบทบัญญัติแห่งกฎหมายที่ทุกฝ่ายได้กระทำไป\n\n\xa0\n\n๒.นายสมัคร สุนทรเวช นายกรัฐมนตรี ไม่มีความเหมาะสมในการบริหารราชการแผ่นดินขาดหลักธรรมาภิบาล แต่ทั้งนี้นายสมัคร สุนทรเวช ยังคงยืนยันและกล่าวอ้างความชอบธรรมตามระบอบประชาธิปไตยภายใต้รัฐธรรมนูญ โดยไม่คำนึงถึงกระแสเรียกร้องใดๆ อันส่งผลให้ความขัดแย้งทางสังคมยิ่งบานปลายจนกลายเป็นวิกฤตการณ์เช่นปัจจุบัน ซึ่งก่อให้เกิดความเสียหายต่อประเทศแนวโน้มจะคลี่คลาย\n\n\xa0\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์ จึงเห็นว่า ควรใช้สิทธิตามรัฐธรรมนูญแห่งราชอาณาจักรไทย พุทธศักราช ๒๕๕๐ มาตรา ๑๖๔ โดยการเข้าชื่อเพื่อร้องต่อประธานวุฒิสภาเพื่อให้มีมติตามมาตรา ๒๗๔ ให้ถอดถอนนายสมัคร สุนทรเวช ออกจากตำแหน่งนายกรัฐมนตรีตามมาตรา ๒๗๐ ณ ลานโพ มหาวิทยาลัยธรรมศาสตร์ ท่าพระจันทร์ อาคารเรียนรวมสังคมศาสตร์ อาคารปิยชาติ และตึกกิจกรรมนักศึกษา มหาวิทยาลัยธรรมศาสตร์ ศูนย์รังสิต\n\n\xa0\n\n\xa0\n\nด้วยความสมานฉันท์\n\nองค์การนักศึกษามหาวิทยาลัยธรรมศาสตร์', 'culture': 0, 'date': '2008-09-06 03:36', 'economics': 0, 'education': 0, 'environment': 0, 'human_rights': 0, 'ict': 0, 'international': 0, 'labor': 0, 'national_security': 0, 'politics': 1, 'quality_of_life': 0, 'social': 0, 'title': 'แถลงการณ์ อมธ.แนะใช้สิทธิ ตาม รธน.เข้าชื่อร้องต่อประธานวุฒิสภาถอดถอน "สมัคร" จากตำแหน่งนายกฯ', 'url': 'https://prachatai.com/print/18038'} ### Data Fields - `url`: url of the article - `date`: date the article was published - `title`: title of the article - `body_text`: body text of the article - `politics`: 1 if sample has this tag else 0 - `human_rights`: 1 if sample has this tag else 0 - `quality_of_life`: 1 if sample has this tag else 0 - `international`: 1 if sample has this tag else 0 - `social`: 1 if sample has this tag else 0 - `environment`: 1 if sample has this tag else 0 - `economics`: 1 if sample has this tag else 0 - `culture`: 1 if sample has this tag else 0 - `labor`: 1 if sample has this tag else 0 - `national_security`: 1 if sample has this tag else 0 - `ict`: 1 if sample has this tag else 0 - `education`: 1 if sample has this tag else 0 ### Data Splits | | train | valid | test | |-------------------|-------|--------|------| | # articles | 54379 | 6721 | 6789 | | politics | 31401 | 3852 | 3842 | | human_rights | 12061 | 1458 | 1511 | | quality_of_life | 9037 | 1144 | 1127 | | international | 6432 | 828 | 834 | | social | 6321 | 782 | 789 | | environment | 6157 | 764 | 772 | | economics | 3994 | 487 | 519 | | culture | 3279 | 388 | 398 | | labor | 2905 | 375 | 350 | | national_security | 2865 | 339 | 338 | | ict | 2326 | 285 | 292 | | education | 2093 | 248 | 255 | ## Dataset Creation ### Curation Rationale The data was scraped from the news site [Prachathai](prachathai.com) from August 24, 2004 to November 15, 2018. The initial intention was to use the dataset as a benchmark for Thai text classification. Due to the size of the dataset, it can also be used for language modeling. ### Source Data #### Initial Data Collection and Normalization 67,889 articles wtih 51,797 tags were scraped from the news site [Prachathai](prachathai.com) from August 24, 2004 to November 15, 2018. We filtered out those articles with less than 500 characters of body text, mostly images and cartoons. #### Who are the source language producers? Prachathai.com ### Annotations #### Annotation process Tags are annotated for the news website Prachathai.com #### Who are the annotators? We assume that the reporters who wrote the articles or other Prachathai staff gave each article its tags. ### Personal and Sensitive Information We do not expect any personal and sensitive information to be present since all data are public news articles. ## Considerations for Using the Data ### Social Impact of Dataset - classification benchmark for multi-label Thai text classification ### Discussion of Biases Prachathai.com is a left-leaning, human-right-focused news site, and thus unusual news labels such as human rights and quality of life. The news articles are expected to be left-leaning in contents. ### Other Known Limitations Dataset provided for research purposes only. Please check dataset license for additional information. ## Additional Information ### Dataset Curators PyThaiNLP ### Licensing Information CC-BY-NC ### Citation Information @misc{prachathai67k, author = {cstorm125, lukkiddd }, title = {prachathai67k}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, howpublished={\\url{https://github.com/PyThaiNLP/prachathai-67k}}, } ### Contributions Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset.

Rycharllyson/CallYou

--- license: openrail ---

mfa13/test1

--- license: mit ---

liuyanchen1015/MULTI_VALUE_qqp_who_at

--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 121227 num_examples: 597 - name: test num_bytes: 1338416 num_examples: 6621 - name: train num_bytes: 1231970 num_examples: 5952 download_size: 1647252 dataset_size: 2691613 --- # Dataset Card for "MULTI_VALUE_qqp_who_at" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jtatman/cosmopedia-100k-sharegpt

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 542800124 num_examples: 100000 download_size: 308483304 dataset_size: 542800124 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "cosmopedia-100k-sharegpt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Moneyshots/Asdf

--- license: unknown ---

CyberHarem/brooklyn_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of brooklyn/ブルックリン/布鲁克林 (Azur Lane) This is the dataset of brooklyn/ブルックリン/布鲁克林 (Azur Lane), containing 43 images and their tags. The core tags of this character are `breasts, long_hair, dark-skinned_female, dark_skin, blonde_hair, large_breasts, red_eyes, ahoge, bow, very_long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 43 | 42.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brooklyn_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 43 | 27.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brooklyn_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 100 | 58.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brooklyn_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 43 | 38.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brooklyn_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 100 | 73.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/brooklyn_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/brooklyn_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 29 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, elbow_gloves, black_gloves, thighhighs, blush, bare_shoulders, cleavage, garter_straps, simple_background, hair_bow, smile, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | elbow_gloves | black_gloves | thighhighs | blush | bare_shoulders | cleavage | garter_straps | simple_background | hair_bow | smile | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:---------------|:---------------|:-------------|:--------|:-----------------|:-----------|:----------------|:--------------------|:-----------|:--------|:-------------------| | 0 | 29 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

Falah/fish_eye_overlooking_industrial_site_prompts

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 1803324 num_examples: 10000 download_size: 90166 dataset_size: 1803324 --- # Dataset Card for "fish_eye_overlooking_industrial_site_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

juancopi81/orca-math-word-problems-180036_190038

--- language: - en dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 15860347 num_examples: 10002 download_size: 7112305 dataset_size: 15860347 configs: - config_name: default data_files: - split: train path: data/train-* ---

tulip4attoo/qa_pairs_3rd

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 5757757 num_examples: 15596 download_size: 3660410 dataset_size: 5757757 --- # Dataset Card for "qa_pairs_3rd" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

andersonbcdefg/supernatural-instructions-2m

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 1859403487.079275 num_examples: 1990915 download_size: 521457643 dataset_size: 1859403487.079275 --- # Dataset Card for "supernatural-instructions-2m" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Gabriel1322/tes

--- license: openrail ---

lmvasque/kwiziq

--- license: cc-by-4.0 --- ## About this dataset This dataset was collected from [kwiziq.com](https://www.kwiziq.com/), a website dedicated to aid Spanish learning through automated methods. It also provides articles in different CEFR-based levels. We have scraped the freely available articles from its original [website](https://www.kwiziq.com/) to make it available to the community. If you use this data, please credit the original [website]((https://www.kwiziq.com/) and our work as well. ## Citation If you use our splits in your research, please cite our work: "[A Benchmark for Neural Readability Assessment of Texts in Spanish](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link)". ``` @inproceedings{vasquez-rodriguez-etal-2022-benchmarking, title = "A Benchmark for Neural Readability Assessment of Texts in Spanish", author = "V{\'a}squez-Rodr{\'\i}guez, Laura and Cuenca-Jim{\'\e}nez, Pedro-Manuel and Morales-Esquivel, Sergio Esteban and Alva-Manchego, Fernando", booktitle = "Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), EMNLP 2022", month = dec, year = "2022", } ``` You can also find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark).

open-llm-leaderboard/details_TFLai__SpeechlessV1-Nova-13B

--- pretty_name: Evaluation run of TFLai/SpeechlessV1-Nova-13B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TFLai/SpeechlessV1-Nova-13B](https://huggingface.co/TFLai/SpeechlessV1-Nova-13B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TFLai__SpeechlessV1-Nova-13B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-22T07:37:37.459766](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__SpeechlessV1-Nova-13B/blob/main/results_2023-10-22T07-37-37.459766.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.006396812080536913,\n\ \ \"em_stderr\": 0.0008164468837432435,\n \"f1\": 0.0904718959731546,\n\ \ \"f1_stderr\": 0.0018774631078676703,\n \"acc\": 0.41594466880448644,\n\ \ \"acc_stderr\": 0.00908410979036271\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.006396812080536913,\n \"em_stderr\": 0.0008164468837432435,\n\ \ \"f1\": 0.0904718959731546,\n \"f1_stderr\": 0.0018774631078676703\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0576194086429113,\n \ \ \"acc_stderr\": 0.006418593319822863\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7742699289660616,\n \"acc_stderr\": 0.011749626260902554\n\ \ }\n}\n```" repo_url: https://huggingface.co/TFLai/SpeechlessV1-Nova-13B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|arc:challenge|25_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-05T14:12:12.910236.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_22T07_37_37.459766 path: - '**/details_harness|drop|3_2023-10-22T07-37-37.459766.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-22T07-37-37.459766.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_22T07_37_37.459766 path: - '**/details_harness|gsm8k|5_2023-10-22T07-37-37.459766.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-22T07-37-37.459766.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hellaswag|10_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-05T14:12:12.910236.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-management|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T14:12:12.910236.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_05T14_12_12.910236 path: - '**/details_harness|truthfulqa:mc|0_2023-09-05T14:12:12.910236.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-05T14:12:12.910236.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_22T07_37_37.459766 path: - '**/details_harness|winogrande|5_2023-10-22T07-37-37.459766.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-22T07-37-37.459766.parquet' - config_name: results data_files: - split: 2023_09_05T14_12_12.910236 path: - results_2023-09-05T14:12:12.910236.parquet - split: 2023_10_22T07_37_37.459766 path: - results_2023-10-22T07-37-37.459766.parquet - split: latest path: - results_2023-10-22T07-37-37.459766.parquet --- # Dataset Card for Evaluation run of TFLai/SpeechlessV1-Nova-13B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TFLai/SpeechlessV1-Nova-13B - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [TFLai/SpeechlessV1-Nova-13B](https://huggingface.co/TFLai/SpeechlessV1-Nova-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_TFLai__SpeechlessV1-Nova-13B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-22T07:37:37.459766](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__SpeechlessV1-Nova-13B/blob/main/results_2023-10-22T07-37-37.459766.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.006396812080536913, "em_stderr": 0.0008164468837432435, "f1": 0.0904718959731546, "f1_stderr": 0.0018774631078676703, "acc": 0.41594466880448644, "acc_stderr": 0.00908410979036271 }, "harness|drop|3": { "em": 0.006396812080536913, "em_stderr": 0.0008164468837432435, "f1": 0.0904718959731546, "f1_stderr": 0.0018774631078676703 }, "harness|gsm8k|5": { "acc": 0.0576194086429113, "acc_stderr": 0.006418593319822863 }, "harness|winogrande|5": { "acc": 0.7742699289660616, "acc_stderr": 0.011749626260902554 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]