davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
cr7Por/my_controlnet	--- dataset_info: features: - name: image dtype: image - name: image_crop dtype: image - name: image_caption dtype: string splits: - name: train num_bytes: 135354742.0 num_examples: 435 download_size: 135278720 dataset_size: 135354742.0 --- # Dataset Card for "my_controlnet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ZeeshanChaudharee/example	--- license: apache-2.0 ---
ilhamxx/xdata_invoices	--- license: unknown ---
winddude/reddit_finance_43_250k	--- license: gpl-3.0 language: - en tags: - finance - investing - crypto - reddit --- # reddit finance 43 250k `reddit_finance_43_250k` is a collection of 250k post/comment pairs from 43 financial, investing and crypto subreddits. Post must have all been text, with a length of 250chars, and a positive score. Each subreddit is narrowed down to the 70th qunatile before being mergered with their top 3 comments and than the other subs. Further score based methods are used to select the top 250k post/comment pairs. The code to recreate the dataset is here: <https://github.com/getorca/ProfitsBot_V0_OLLM/tree/main/ds_builder> The trained lora model is here: <https://huggingface.co/winddude/pb_lora_7b_v0.1>
version-control/data-2	--- dataset_info: features: - name: version dtype: string - name: code dtype: string - name: apis sequence: string - name: full_version dtype: string - name: repo_name dtype: string - name: hexsha dtype: string splits: - name: torch num_bytes: 8549368 num_examples: 697 - name: tensorflow num_bytes: 4122692 num_examples: 276 - name: scipy num_bytes: 4668643 num_examples: 193 - name: pandas num_bytes: 6791523 num_examples: 483 - name: sklearn num_bytes: 2050255 num_examples: 170 - name: numpy num_bytes: 29978447 num_examples: 1757 - name: matplotlib num_bytes: 3453619 num_examples: 251 download_size: 21315897 dataset_size: 59614547 configs: - config_name: default data_files: - split: torch path: data/torch-* - split: tensorflow path: data/tensorflow-* - split: scipy path: data/scipy-* - split: pandas path: data/pandas-* - split: sklearn path: data/sklearn-* - split: numpy path: data/numpy-* - split: matplotlib path: data/matplotlib-* ---
carnival13/massive_eval_DA_tokenized	--- dataset_info: features: - name: pass_label dtype: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 23064510 num_examples: 24160 download_size: 5097845 dataset_size: 23064510 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "massive_eval_DA_tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Giacinta/weibo	--- license: apache-2.0 task_categories: - text-classification language: - zh tags: - medical pretty_name: weibo size_categories: - n<1K configs: - config_name: default data_files: - split: train path: PYH微博抽样数据.csv ---
KShivendu/wikipedia-1k-cohere-openai-embeddings	--- language: en license: mit dataset_info: features: - name: id dtype: int32 - name: title dtype: string - name: text dtype: string - name: url dtype: string - name: wiki_id dtype: int32 - name: views dtype: float32 - name: paragraph_id dtype: int32 - name: langs dtype: int32 - name: cohere sequence: float32 - name: openai sequence: float64 splits: - name: train num_bytes: 15850870 num_examples: 1000 download_size: 13208079 dataset_size: 15850870 tags: - openai - cohere - wikipedia --- Smaller version of https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings that includes Cohere as well as OpenAI embeddings (`text-embedding-ada-002`) 100k version of this dataset will be released soon.
psm151/wav2vec2-large-xlsr-turkish-demo-colab	--- license: openrail ---
CyberHarem/yat_sen_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yat_sen/逸仙/逸仙 (Azur Lane) This is the dataset of yat_sen/逸仙/逸仙 (Azur Lane), containing 86 images and their tags. The core tags of this character are `long_hair, bangs, hair_ornament, black_hair, breasts, red_eyes, hair_flower, large_breasts, blunt_bangs, 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 \| 86 \| 150.70 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yat_sen_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 86 \| 77.18 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yat_sen_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 200 \| 151.65 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yat_sen_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 86 \| 128.46 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yat_sen_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 200 \| 224.93 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yat_sen_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/yat_sen_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 \| 7 \| ![](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, china_dress, cleavage_cutout, detached_sleeves, flower, looking_at_viewer, solo, white_dress, bare_shoulders, black_thighhighs, smile, blush, bridal_gauntlets, covered_navel, gloves, sitting, white_background \| \| 1 \| 6 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, bare_shoulders, china_dress, detached_sleeves, flower, looking_at_viewer, smile, solo, white_dress, closed_mouth, sleeveless_dress, black_gloves, black_thighhighs, cleavage_cutout, official_alternate_costume, oil-paper_umbrella, pelvic_curtain, thighs, brown_gloves, full_body, holding_umbrella, sitting \| \| 2 \| 8 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, flower, looking_at_viewer, solo, white_dress, cleavage, sitting, white_thighhighs, blush, closed_mouth, long_sleeves, chinese_clothes, official_alternate_costume, smile, collarbone, holding_umbrella, feet, full_body, no_shoes, see-through_dress \| \| 3 \| 11 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, smile, solo, flower, looking_at_viewer, red_dress, closed_mouth, long_sleeves, brown_hair, wide_sleeves, jewelry, sitting, china_dress, hair_bun, holding_fan \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| china_dress \| cleavage_cutout \| detached_sleeves \| flower \| looking_at_viewer \| solo \| white_dress \| bare_shoulders \| black_thighhighs \| smile \| blush \| bridal_gauntlets \| covered_navel \| gloves \| sitting \| white_background \| closed_mouth \| sleeveless_dress \| black_gloves \| official_alternate_costume \| oil-paper_umbrella \| pelvic_curtain \| thighs \| brown_gloves \| full_body \| holding_umbrella \| cleavage \| white_thighhighs \| long_sleeves \| chinese_clothes \| collarbone \| feet \| no_shoes \| see-through_dress \| red_dress \| brown_hair \| wide_sleeves \| jewelry \| hair_bun \| holding_fan \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------\|:------------------\|:-------------------\|:---------\|:--------------------\|:-------\|:--------------\|:-----------------\|:-------------------\|:--------\|:--------\|:-------------------\|:----------------\|:---------\|:----------\|:-------------------\|:---------------\|:-------------------\|:---------------\|:-----------------------------\|:---------------------\|:-----------------\|:---------\|:---------------\|:------------\|:-------------------\|:-----------\|:-------------------\|:---------------\|:------------------\|:-------------\|:-------\|:-----------\|:--------------------\|:------------\|:-------------\|:---------------\|:----------\|:-----------\|:--------------\| \| 0 \| 7 \| ![](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 \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 6 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 8 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| \| \| X \| X \| X \| X \| \| \| X \| X \| \| \| \| X \| \| X \| \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| 3 \| 11 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| \| \| X \| X \| X \| \| \| \| X \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \|
open-llm-leaderboard/details_OpenModels4all__gemma-1.1-7b-it	--- pretty_name: Evaluation run of OpenModels4all/gemma-1.1-7b-it dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [OpenModels4all/gemma-1.1-7b-it](https://huggingface.co/OpenModels4all/gemma-1.1-7b-it)\ \ 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_OpenModels4all__gemma-1.1-7b-it\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-09T11:06:14.950936](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenModels4all__gemma-1.1-7b-it/blob/main/results_2024-04-09T11-06-14.950936.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\": 0.6014747509936101,\n\ \ \"acc_stderr\": 0.03326656009179332,\n \"acc_norm\": 0.6064901781756107,\n\ \ \"acc_norm_stderr\": 0.03393142550082237,\n \"mc1\": 0.34149326805385555,\n\ \ \"mc1_stderr\": 0.016600688619950826,\n \"mc2\": 0.5039555794689907,\n\ \ \"mc2_stderr\": 0.01643070151671278\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5656996587030717,\n \"acc_stderr\": 0.01448470304885736,\n\ \ \"acc_norm\": 0.5998293515358362,\n \"acc_norm_stderr\": 0.014317197787809174\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5862378012348137,\n\ \ \"acc_stderr\": 0.004915003499517829,\n \"acc_norm\": 0.7620991834295957,\n\ \ \"acc_norm_stderr\": 0.004249278842903416\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.5111111111111111,\n\ \ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.5111111111111111,\n\ \ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \ \ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6226415094339622,\n \"acc_stderr\": 0.029832808114796,\n\ \ \"acc_norm\": 0.6226415094339622,\n \"acc_norm_stderr\": 0.029832808114796\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6597222222222222,\n\ \ \"acc_stderr\": 0.039621355734862175,\n \"acc_norm\": 0.6597222222222222,\n\ \ \"acc_norm_stderr\": 0.039621355734862175\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\ : 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_mathematics\|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_medicine\|5\": {\n \"acc\": 0.5953757225433526,\n\ \ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n\ \ \"acc_norm_stderr\": 0.03742461193887248\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\":\ \ 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n\ \ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.032469569197899575,\n\ \ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.032469569197899575\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.40350877192982454,\n\ \ \"acc_stderr\": 0.04615186962583702,\n \"acc_norm\": 0.40350877192982454,\n\ \ \"acc_norm_stderr\": 0.04615186962583702\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.46296296296296297,\n \"acc_stderr\": 0.02568056464005688,\n \"\ acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.02568056464005688\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7451612903225806,\n\ \ \"acc_stderr\": 0.024790118459332208,\n \"acc_norm\": 0.7451612903225806,\n\ \ \"acc_norm_stderr\": 0.024790118459332208\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175007,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175007\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|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-high_school_european_history\|5\"\ : {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.03588624800091707,\n\ \ \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.03588624800091707\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7575757575757576,\n \"acc_stderr\": 0.030532892233932046,\n \"\ acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.030532892233932046\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.02649905770139744,\n\ \ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.02649905770139744\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5974358974358974,\n \"acc_stderr\": 0.02486499515976775,\n \ \ \"acc_norm\": 0.5974358974358974,\n \"acc_norm_stderr\": 0.02486499515976775\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.362962962962963,\n \"acc_stderr\": 0.02931820364520686,\n \ \ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.02931820364520686\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.030489911417673227,\n\ \ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.030489911417673227\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8091743119266055,\n \"acc_stderr\": 0.01684767640009109,\n \"\ acc_norm\": 0.8091743119266055,\n \"acc_norm_stderr\": 0.01684767640009109\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4537037037037037,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7401960784313726,\n \"acc_stderr\": 0.03077855467869327,\n \"\ acc_norm\": 0.7401960784313726,\n \"acc_norm_stderr\": 0.03077855467869327\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7426160337552743,\n \"acc_stderr\": 0.0284588209914603,\n \ \ \"acc_norm\": 0.7426160337552743,\n \"acc_norm_stderr\": 0.0284588209914603\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7085201793721974,\n\ \ \"acc_stderr\": 0.030500283176545854,\n \"acc_norm\": 0.7085201793721974,\n\ \ \"acc_norm_stderr\": 0.030500283176545854\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6946564885496184,\n \"acc_stderr\": 0.04039314978724561,\n\ \ \"acc_norm\": 0.6946564885496184,\n \"acc_norm_stderr\": 0.04039314978724561\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990946,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990946\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.034878251684978906,\n\ \ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.034878251684978906\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8418803418803419,\n\ \ \"acc_stderr\": 0.0239023255495604,\n \"acc_norm\": 0.8418803418803419,\n\ \ \"acc_norm_stderr\": 0.0239023255495604\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7650063856960408,\n\ \ \"acc_stderr\": 0.015162024152278445,\n \"acc_norm\": 0.7650063856960408,\n\ \ \"acc_norm_stderr\": 0.015162024152278445\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.025816756791584204,\n\ \ \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.025816756791584204\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.23687150837988827,\n\ \ \"acc_stderr\": 0.01421957078810399,\n \"acc_norm\": 0.23687150837988827,\n\ \ \"acc_norm_stderr\": 0.01421957078810399\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6699346405228758,\n \"acc_stderr\": 0.026925654653615697,\n\ \ \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.026925654653615697\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6527331189710611,\n\ \ \"acc_stderr\": 0.027040745502307336,\n \"acc_norm\": 0.6527331189710611,\n\ \ \"acc_norm_stderr\": 0.027040745502307336\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6358024691358025,\n \"acc_stderr\": 0.02677492989972234,\n\ \ \"acc_norm\": 0.6358024691358025,\n \"acc_norm_stderr\": 0.02677492989972234\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.45390070921985815,\n \"acc_stderr\": 0.02970045324729147,\n \ \ \"acc_norm\": 0.45390070921985815,\n \"acc_norm_stderr\": 0.02970045324729147\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4485006518904824,\n\ \ \"acc_stderr\": 0.012702317490559806,\n \"acc_norm\": 0.4485006518904824,\n\ \ \"acc_norm_stderr\": 0.012702317490559806\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.03035969707904612,\n\ \ \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03035969707904612\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5898692810457516,\n \"acc_stderr\": 0.019898412717635903,\n \ \ \"acc_norm\": 0.5898692810457516,\n \"acc_norm_stderr\": 0.019898412717635903\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.0287951855742913,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.0287951855742913\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8109452736318408,\n\ \ \"acc_stderr\": 0.027686913588013028,\n \"acc_norm\": 0.8109452736318408,\n\ \ \"acc_norm_stderr\": 0.027686913588013028\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5060240963855421,\n\ \ \"acc_stderr\": 0.03892212195333047,\n \"acc_norm\": 0.5060240963855421,\n\ \ \"acc_norm_stderr\": 0.03892212195333047\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.0312678171466318,\n\ \ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.0312678171466318\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.34149326805385555,\n\ \ \"mc1_stderr\": 0.016600688619950826,\n \"mc2\": 0.5039555794689907,\n\ \ \"mc2_stderr\": 0.01643070151671278\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.6992896606156275,\n \"acc_stderr\": 0.01288801049470473\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.4177407126611069,\n \ \ \"acc_stderr\": 0.013584820638504828\n }\n}\n```" repo_url: https://huggingface.co/OpenModels4all/gemma-1.1-7b-it 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_09T11_06_14.950936 path: - '/details_harness\|arc:challenge\|25_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-09T11-06-14.950936.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|gsm8k\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hellaswag\|10_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-09T11-06-14.950936.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-09T11-06-14.950936.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '/details_harness\|winogrande\|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-09T11-06-14.950936.parquet' - config_name: results data_files: - split: 2024_04_09T11_06_14.950936 path: - results_2024-04-09T11-06-14.950936.parquet - split: latest path: - results_2024-04-09T11-06-14.950936.parquet --- # Dataset Card for Evaluation run of OpenModels4all/gemma-1.1-7b-it <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [OpenModels4all/gemma-1.1-7b-it](https://huggingface.co/OpenModels4all/gemma-1.1-7b-it) 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_OpenModels4all__gemma-1.1-7b-it", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-09T11:06:14.950936](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenModels4all__gemma-1.1-7b-it/blob/main/results_2024-04-09T11-06-14.950936.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": 0.6014747509936101, "acc_stderr": 0.03326656009179332, "acc_norm": 0.6064901781756107, "acc_norm_stderr": 0.03393142550082237, "mc1": 0.34149326805385555, "mc1_stderr": 0.016600688619950826, "mc2": 0.5039555794689907, "mc2_stderr": 0.01643070151671278 }, "harness\|arc:challenge\|25": { "acc": 0.5656996587030717, "acc_stderr": 0.01448470304885736, "acc_norm": 0.5998293515358362, "acc_norm_stderr": 0.014317197787809174 }, "harness\|hellaswag\|10": { "acc": 0.5862378012348137, "acc_stderr": 0.004915003499517829, "acc_norm": 0.7620991834295957, "acc_norm_stderr": 0.004249278842903416 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5111111111111111, "acc_stderr": 0.04318275491977976, "acc_norm": 0.5111111111111111, "acc_norm_stderr": 0.04318275491977976 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6226415094339622, "acc_stderr": 0.029832808114796, "acc_norm": 0.6226415094339622, "acc_norm_stderr": 0.029832808114796 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6597222222222222, "acc_stderr": 0.039621355734862175, "acc_norm": 0.6597222222222222, "acc_norm_stderr": 0.039621355734862175 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5953757225433526, "acc_stderr": 0.03742461193887248, "acc_norm": 0.5953757225433526, "acc_norm_stderr": 0.03742461193887248 }, "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": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5574468085106383, "acc_stderr": 0.032469569197899575, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.032469569197899575 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.40350877192982454, "acc_stderr": 0.04615186962583702, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.04615186962583702 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.46296296296296297, "acc_stderr": 0.02568056464005688, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.02568056464005688 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.044444444444444495, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.044444444444444495 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7451612903225806, "acc_stderr": 0.024790118459332208, "acc_norm": 0.7451612903225806, "acc_norm_stderr": 0.024790118459332208 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175007, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175007 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.696969696969697, "acc_stderr": 0.03588624800091707, "acc_norm": 0.696969696969697, "acc_norm_stderr": 0.03588624800091707 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7575757575757576, "acc_stderr": 0.030532892233932046, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.030532892233932046 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8393782383419689, "acc_stderr": 0.02649905770139744, "acc_norm": 0.8393782383419689, "acc_norm_stderr": 0.02649905770139744 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5974358974358974, "acc_stderr": 0.02486499515976775, "acc_norm": 0.5974358974358974, "acc_norm_stderr": 0.02486499515976775 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.362962962962963, "acc_stderr": 0.02931820364520686, "acc_norm": 0.362962962962963, "acc_norm_stderr": 0.02931820364520686 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6722689075630253, "acc_stderr": 0.030489911417673227, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.030489911417673227 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.32450331125827814, "acc_stderr": 0.03822746937658752, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.03822746937658752 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8091743119266055, "acc_stderr": 0.01684767640009109, "acc_norm": 0.8091743119266055, "acc_norm_stderr": 0.01684767640009109 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4537037037037037, "acc_stderr": 0.033953227263757976, "acc_norm": 0.4537037037037037, "acc_norm_stderr": 0.033953227263757976 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7401960784313726, "acc_stderr": 0.03077855467869327, "acc_norm": 0.7401960784313726, "acc_norm_stderr": 0.03077855467869327 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7426160337552743, "acc_stderr": 0.0284588209914603, "acc_norm": 0.7426160337552743, "acc_norm_stderr": 0.0284588209914603 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7085201793721974, "acc_stderr": 0.030500283176545854, "acc_norm": 0.7085201793721974, "acc_norm_stderr": 0.030500283176545854 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6946564885496184, "acc_stderr": 0.04039314978724561, "acc_norm": 0.6946564885496184, "acc_norm_stderr": 0.04039314978724561 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990946, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990946 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252626, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252626 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7300613496932515, "acc_stderr": 0.034878251684978906, "acc_norm": 0.7300613496932515, "acc_norm_stderr": 0.034878251684978906 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8418803418803419, "acc_stderr": 0.0239023255495604, "acc_norm": 0.8418803418803419, "acc_norm_stderr": 0.0239023255495604 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7650063856960408, "acc_stderr": 0.015162024152278445, "acc_norm": 0.7650063856960408, "acc_norm_stderr": 0.015162024152278445 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6416184971098265, "acc_stderr": 0.025816756791584204, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.025816756791584204 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23687150837988827, "acc_stderr": 0.01421957078810399, "acc_norm": 0.23687150837988827, "acc_norm_stderr": 0.01421957078810399 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6699346405228758, "acc_stderr": 0.026925654653615697, "acc_norm": 0.6699346405228758, "acc_norm_stderr": 0.026925654653615697 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6527331189710611, "acc_stderr": 0.027040745502307336, "acc_norm": 0.6527331189710611, "acc_norm_stderr": 0.027040745502307336 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6358024691358025, "acc_stderr": 0.02677492989972234, "acc_norm": 0.6358024691358025, "acc_norm_stderr": 0.02677492989972234 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.45390070921985815, "acc_stderr": 0.02970045324729147, "acc_norm": 0.45390070921985815, "acc_norm_stderr": 0.02970045324729147 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4485006518904824, "acc_stderr": 0.012702317490559806, "acc_norm": 0.4485006518904824, "acc_norm_stderr": 0.012702317490559806 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5147058823529411, "acc_stderr": 0.03035969707904612, "acc_norm": 0.5147058823529411, "acc_norm_stderr": 0.03035969707904612 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5898692810457516, "acc_stderr": 0.019898412717635903, "acc_norm": 0.5898692810457516, "acc_norm_stderr": 0.019898412717635903 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7183673469387755, "acc_stderr": 0.0287951855742913, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.0287951855742913 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8109452736318408, "acc_stderr": 0.027686913588013028, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.027686913588013028 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5060240963855421, "acc_stderr": 0.03892212195333047, "acc_norm": 0.5060240963855421, "acc_norm_stderr": 0.03892212195333047 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7894736842105263, "acc_stderr": 0.0312678171466318, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.0312678171466318 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.34149326805385555, "mc1_stderr": 0.016600688619950826, "mc2": 0.5039555794689907, "mc2_stderr": 0.01643070151671278 }, "harness\|winogrande\|5": { "acc": 0.6992896606156275, "acc_stderr": 0.01288801049470473 }, "harness\|gsm8k\|5": { "acc": 0.4177407126611069, "acc_stderr": 0.013584820638504828 } } ``` ## 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]
sieu-n/alpaca_eval_multilingual	--- license: cc-by-nc-4.0 --- ### Usage ``` load_dataset("krenerd/alpaca_eval_multilingual", "alpaca_eval") # or alpaca_eval_en load_dataset("krenerd/alpaca_eval_multilingual", "alpaca_eval_ko") load_dataset("krenerd/alpaca_eval_multilingual", "alpaca_eval_ja") ``` ### Method The dataset was translated by GPT-4 API using the following prompt. ``` ja = ChatPromptTemplate.from_messages( [ SystemMessagePromptTemplate.from_template( "You are a helpful assistant fluent in English and Japanese." ), HumanMessagePromptTemplate.from_template( "Translate the following text to Japanese. Show the answer only. このテキストを直訳するのではなく、その意味を保持しつつ、より自然なリクエストに言い換えて翻訳してください text=```{instruction}```" ), ] ) ko = ChatPromptTemplate.from_messages( [ SystemMessagePromptTemplate.from_template( "You are a helpful assistant fluent in English and Korean." ), HumanMessagePromptTemplate.from_template( "Translate the following text to Korean. Show the answer only. 말 그대로 번역하지 말고, 의미가 유지되는 한에서 자연스러운 요청으로 번역해줘. text=```{instruction}```" ), ] ) ``` Script: https://gist.github.com/sieu-n/88542733914f80f780359f5c82c99a62
llm-blender/mix-instruct	--- license: mit task_categories: - text-generation language: - en pretty_name: mix-instruct size_categories: - 100K<n<1M --- # MixInstruct ## Introduction This is the official realease of dataset MixInstruct for project LLM-Blender. This dataset contains 11 responses from the current popular instruction following-LLMs that includes: 1. [Stanford Alpaca](https://huggingface.co/chavinlo/alpaca-native) 2. [FastChat Vicuna](https://huggingface.co/eachadea/vicuna-13b-1.1) 3. [Dolly V2](https://huggingface.co/databricks/dolly-v2-12b) 4. [StableLM](https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b) 5. [Open Assistant](https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5) 6. [Koala](https://huggingface.co/TheBloke/koala-7B-HF) 7. [Baize](https://huggingface.co/mosesjun0h/llama-7b-hf-baize-lora-bf16) 8. [Flan-T5](https://huggingface.co/google/flan-t5-xxl) 9. [ChatGLM](https://huggingface.co/THUDM/chatglm-6b) 10. [MOSS](https://huggingface.co/fnlp/moss-moon-003-sft) 11. [Moasic MPT](https://huggingface.co/mosaicml/mpt-7b-instruct) We evaluate each response with auto metrics including BLEU, ROUGE, BERTScore, BARTScore. And provide pairwise comparison results by prompting ChatGPT for the $4771$ examples in the test split. (The rest $229$ examples contain contents filtered by the API). ## Data Format ```json [ { "id": "unified_chip2/69962", "instruction": "", "input": "I've always wondered what the difference is between a skeptic and a denier.", "output": "A skeptic is someone who questions the validity of something, while a denier is someone who outright rejects something without evidence or reason.", "candidates": [ { "decoding_method": "top_p_sampling", "model": "oasst-sft-4-pythia-12b-epoch-3.5", "text": "A skeptic is someone who doubts or expresses ...", "scores": { "logprobs": -0.02404022216796875, "bleu": 5.656152750894142, "bertscore": 0.7549101114273071, "rouge1": 0.2857142857142857, "rouge2": 0.1272727272727273, "rougeL": 0.23214285714285715, "rougeLsum": 0.23214285714285715 } }, ... ], }, ... ] ``` Examples evaluted by ChatGPT will contain another filed cmp_results. The options contains: 1. A is better 2. B is better 3. Same good 4. Same bad ```json "cmp_results": { "model_A,model_B": "A is better", ... }, ``` Each cmp_results field is encoded into a str in a json format. Please first use `json.loads(item['cmp_results'])` to get the cmp_results for each item. "null" denotes no cmp_results from ChatGPT avaliable. ## Eval Results ### Auto Metrics - train \| Models (down) / Metircs (right) \| logprobs \| rougeL \| rouge2 \| rougeLsum \| rouge1 \| bleu \| bertscore \| bleurt \| bartscore \| \|:----------------------------------\|:------------\|:----------------\|:----------------\|:----------------\|:----------------\|:----------------\|:----------------\|:----------------\|:-------------\| \| alpaca-native \| -6.1247 \| 0.248 \| 0.1414 \| 0.2986 \| 0.3347 \| 8.057 \| 0.7196 \| -0.5092 \| -3.5335 \| \| chatglm-6b \| -10.1263 \| 0.2231 \| 0.1212 \| 0.2743 \| 0.3074 \| 6.2597 \| 0.7043 \| -0.6071 \| -3.4975 \| \| dolly-v2-12b \| -24.8508 \| 0.1245 \| 0.0502 \| 0.1625 \| 0.1836 \| 2.1062 \| 0.6244 \| -0.8562 \| -3.8145 \| \| flan-t5-xxl \| -1.0717 \| 0.1202 \| 0.0456 \| 0.1334 \| 0.1489 \| 1.8418 \| 0.6514 \| -1.2176 \| -4.537 \| \| koala-7B-HF \| -10.8323 \| 0.1533 \| 0.0683 \| 0.1909 \| 0.2165 \| 3.2848 \| 0.6436 \| -0.8284 \| -3.8326 \| \| llama-7b-hf-baize-lora-bf16 \| -24.8867 \| 0.1539 \| 0.0797 \| 0.2042 \| 0.2276 \| 3.4928 \| 0.6564 \| -0.6575 \| -3.496 \| \| moss-moon-003-sft \| -796.1366 \| 0.1599 \| 0.0898 \| 0.2135 \| 0.236 \| 3.944 \| 0.6689 \| -0.5617 \| -3.3404 \| \| mpt-7b \| -174.1702 \| 0.1118 \| 0.0447 \| 0.1517 \| 0.1683 \| 1.7698 \| 0.618 \| -0.9525 \| -3.9119 \| \| mpt-7b-instruct \| -156.8005 \| 0.1225 \| 0.0538 \| 0.1669 \| 0.1861 \| 2.1041 \| 0.6327 \| -0.8176 \| -3.6996 \| \| oasst-sft-4-pythia-12b-epoch-3.5 \| -4.7714 \| 0.2902 \| 0.1763 \| 0.3447 \| 0.386 \| 10.6599 \| 0.748 \| -0.3762 \| -3.4221 \| \| stablelm-tuned-alpha-7b \| -1268.9396 \| 0.1336 \| 0.0544 \| 0.1714 \| 0.1948 \| 2.6348 \| 0.6355 \| -0.9585 \| -4.0795 \| \| vicuna-13b-1.1 \| -11.1528 \| 0.211 \| 0.1219 \| 0.2671 \| 0.3003 \| 6.3697 \| 0.6928 \| -0.6194 \| -3.4233 \| \| Best Model Metric Perf \| -1.0717 \| 0.2902 \| 0.1763 \| 0.3447 \| 0.386 \| 10.6599 \| 0.748 \| -0.3762 \| -3.3404 \| \| Oracle \| 0.0 \| 0.3611 \| 0.2471 \| 0.4242 \| 0.4706 \| 15.8557 \| 0.7783 \| 0.0723 \| 0.0 \| \| Oracle-Best_Model Gap \| 1.0717 \| 0.0709 \| 0.0708 \| 0.0794 \| 0.0846 \| 5.1958 \| 0.0303 \| 0.4484 \| 3.3404 \| - val \| Models (down) / Metircs (right) \| logprobs \| rouge1 \| rouge2 \| rougeLsum \| rougeL \| bleu \| bertscore \| bleurt \| bartscore \| \|:----------------------------------\|:------------\|:----------------\|:----------------\|:----------------\|:----------------\|:----------------\|:----------------\|:----------------\|:---------------\| \| alpaca-native \| -3.3832 \| 0.3342 \| 0.1452 \| 0.299 \| 0.2503 \| 8.1749 \| 0.7198 \| -0.5076 \| -3.5517 \| \| chatglm-6b \| -4.7033 \| 0.3066 \| 0.1216 \| 0.2743 \| 0.2241 \| 6.3323 \| 0.7053 \| -0.6091 \| -3.51 \| \| dolly-v2-12b \| -9.1237 \| 0.1843 \| 0.0511 \| 0.1633 \| 0.1254 \| 2.1368 \| 0.6257 \| -0.852 \| -3.8121 \| \| flan-t5-xxl \| -1.0077 \| 0.1497 \| 0.0464 \| 0.1342 \| 0.1212 \| 1.8653 \| 0.652 \| -1.2089 \| -4.5407 \| \| koala-7B-HF \| -6.015 \| 0.2154 \| 0.068 \| 0.1903 \| 0.1538 \| 3.2596 \| 0.6425 \| -0.8298 \| -3.8456 \| \| llama-7b-hf-baize-lora-bf16 \| -12.2594 \| 0.2261 \| 0.0803 \| 0.2034 \| 0.1543 \| 3.5462 \| 0.6562 \| -0.6604 \| -3.4831 \| \| moss-moon-003-sft \| -357.3054 \| 0.2053 \| 0.0678 \| 0.1851 \| 0.1361 \| 2.9639 \| 0.648 \| -0.7261 \| -3.6317 \| \| mpt-7b \| -171.9416 \| 0.1663 \| 0.0447 \| 0.1499 \| 0.1111 \| 1.7555 \| 0.617 \| -0.964 \| -3.9189 \| \| mpt-7b-instruct \| -157.1143 \| 0.1841 \| 0.054 \| 0.1652 \| 0.1224 \| 2.1252 \| 0.6307 \| -0.8275 \| -3.7183 \| \| oasst-ft-4-pythia-12b-epoch-3.5 \| -1.6194 \| 0.3835 \| 0.1761 \| 0.3434 \| 0.2896 \| 10.5858 \| 0.7479 \| -0.378 \| -3.4366 \| \| stablelm-tuned-alpha-7b \| -869.6767 \| 0.192 \| 0.0529 \| 0.1688 \| 0.1317 \| 2.5687 \| 0.6314 \| -0.9618 \| -4.1008 \| \| vicuna-13b-1.1 \| -5.6143 \| 0.3029 \| 0.1242 \| 0.2701 \| 0.2142 \| 6.5299 \| 0.695 \| -0.6212 \| -3.4332 \| \| Best Model Metric Perf \| -1.0077 \| 0.3835 \| 0.1761 \| 0.3434 \| 0.2896 \| 10.5858 \| 0.7479 \| -0.378 \| -3.4332 \| \| Oracle \| 0.0 \| 0.4712 \| 0.2488 \| 0.4258 \| 0.3642 \| 15.9896 \| 0.7794 \| 0.0726 \| 0.0 \| \| Oracle-Best_Model Gap \| 1.0077 \| 0.0877 \| 0.0728 \| 0.0824 \| 0.0746 \| 5.4038 \| 0.0315 \| 0.4506 \| 3.4332 \| - test \| Models (down) / Metircs (right) \| logprobs \| rougeL \| rougeLsum \| rouge1 \| rouge2 \| bleu \| bertscore \| bleurt \| bartscore \| \|:----------------------------------\|:------------\|:----------------\|:----------------\|:----------------\|:----------------\|:----------------\|:----------------\|:----------------\|:---------------\| \| alpaca-native \| -3.458 \| 0.2421 \| 0.2915 \| 0.3276 \| 0.1362 \| 7.6478 \| 0.7146 \| -0.5307 \| -3.5696 \| \| chatglm-6b \| -4.7418 \| 0.2225 \| 0.2734 \| 0.3063 \| 0.1192 \| 6.0493 \| 0.7038 \| -0.6167 \| -3.5193 \| \| dolly-v2-12b \| -9.1266 \| 0.1236 \| 0.1606 \| 0.1811 \| 0.0495 \| 2.062 \| 0.6226 \| -0.8654 \| -3.8331 \| \| flan-t5-xxl \| -0.9924 \| 0.1172 \| 0.1296 \| 0.1444 \| 0.0432 \| 1.6066 \| 0.6492 \| -1.2288 \| -4.5717 \| \| koala-7B-HF \| -6.1159 \| 0.1507 \| 0.1871 \| 0.2131 \| 0.0662 \| 3.0983 \| 0.6396 \| -0.8354 \| -3.8496 \| \| llama-7b-hf-baize-lora-bf16 \| -11.9519 \| 0.1521 \| 0.2022 \| 0.2253 \| 0.0781 \| 3.4005 \| 0.6557 \| -0.663 \| -3.526 \| \| moss-moon-003-sft \| -356.8774 \| 0.1365 \| 0.1863 \| 0.2062 \| 0.0686 \| 2.9561 \| 0.6485 \| -0.7261 \| -3.6461 \| \| mpt-7b \| -176.2144 \| 0.1106 \| 0.1498 \| 0.1663 \| 0.0439 \| 1.7392 \| 0.6165 \| -0.9636 \| -3.9419 \| \| mpt-7b-instruct \| -156.0153 \| 0.121 \| 0.1647 \| 0.1837 \| 0.0524 \| 2.0692 \| 0.6321 \| -0.8232 \| -3.7208 \| \| oasst-sft-4-pythia-12b-epoch-3.5 \| -1.6749 \| 0.2873 \| 0.341 \| 0.3813 \| 0.1738 \| 10.5046 \| 0.7468 \| -0.3908 \| -3.4486 \| \| stablelm-tuned-alpha-7b \| -831.595 \| 0.1306 \| 0.1672 \| 0.1904 \| 0.0524 \| 2.5044 \| 0.6247 \| -0.9832 \| -4.1208 \| \| vicuna-13b-1.1 \| -5.6914 \| 0.2122 \| 0.2677 \| 0.3012 \| 0.1223 \| 6.3584 \| 0.696 \| -0.6146 \| -3.4368 \| \| Best Model Metric Perf \| -0.9924 \| 0.2873 \| 0.341 \| 0.3813 \| 0.1738 \| 10.5046 \| 0.7468 \| -0.3908 \| -3.4368 \| \| Oracle \| 0.0 \| 0.3585 \| 0.4201 \| 0.466 \| 0.2438 \| 15.4971 \| 0.7767 \| 0.0679 \| 0.0 \| \| Oracle-Best_Model Gap \| 0.9924 \| 0.0712 \| 0.0791 \| 0.0847 \| 0.07 \| 4.9925 \| 0.0299 \| 0.4587 \| 3.4368 \| ### ChatGPT CMPTS (4771 examples) \| Methods \| BERTScore \| BARTScore \| BLEURT \| GPT-Rank \| Beat Vic(%) \| Beat OA(%) \| Top-1(%) \| Top-2(%) \| Top-3(%) \| \|:-----------------:\|:---------:\|:---------:\|:---------:\|:--------:\|:----------:\|:----------:\|:----------:\|:----------:\|:----------:\| \| Open Assistant \| 74.68 \| -3.45 \| -0.39 \| 3.90 \| 62.78 \| N/A \| 17.35 \| 35.67 \| 51.98 \| \| Vicuna \| 69.60 \| -3.44 \| -0.61 \| 4.13 \| N/A \| 64.77 \| 25.47 \| 41.23 \| 52.88 \| \| Alpaca \| 71.46 \| -3.57 \| -0.53 \| 4.62 \| 56.70 \| 61.35 \| 15.41 \| 29.81 \| 44.46 \| \| Baize \| 65.57 \| -3.53 \| -0.66 \| 4.86 \| 52.76 \| 56.40 \| 14.23 \| 26.91 \| 38.80 \| \| moss \| 64.85 \| -3.65 \| -0.73 \| 5.09 \| 51.62 \| 51.79 \| 15.93 \| 27.52 \| 38.27 \| \| ChatGLM \| 70.38 \| -3.52 \| -0.62 \| 5.63 \| 44.04 \| 45.67 \| 9.41 \| 19.37 \| 28.78 \| \| Koala \| 63.96 \| -3.85 \| -0.84 \| 6.76 \| 39.93 \| 39.01 \| 8.15 \| 15.72 \| 22.55 \| \| Dolly v2 \| 62.26 \| -3.83 \| -0.87 \| 6.90 \| 33.33 \| 31.44 \| 5.16 \| 10.06 \| 16.45 \| \| Mosaic MPT \| 63.21 \| -3.72 \| -0.82 \| 7.19 \| 30.87 \| 30.16 \| 5.39 \| 10.61 \| 16.24 \| \| StableLM \| 62.47 \| -4.12 \| -0.98 \| 8.71 \| 21.55 \| 19.87 \| 2.33 \| 4.74 \| 7.96 \| \| Flan-T5 \| 64.92 \| -4.57 \| -1.23 \| 8.81 \| 23.89 \| 19.93 \| 1.30 \| 2.87 \| 5.32 \| \| Oracle(BERTScore) \| 77.67 \| -3.17 \| -0.27 \| 3.88 \| 54.41 \| 38.84 \| 20.16 \| 38.11 \| 53.49 \| \| Oracle(BLEURT) \| 75.02 \| -3.15 \| -0.15 \| 3.77 \| 55.61 \| 45.80 \| 21.48 \| 39.84 \| 55.36 \| \| Oracle(BARTScore) \| 73.23 \| -2.87 \| -0.38 \| 3.69 \| 50.32 \| 57.01 \| 26.10 \| 43.70 \| 57.33 \| \| Oracle(ChatGPT) \| 70.32 \| -3.33 \| -0.51 \| 1.00 \| 100.00 \| 100.00 \| 100.00 \| 100.00 \| 100.00 \|
LambdaTests/VQAv2_sample_validation_benchmarks_partition_global_6_loca_6	--- dataset_info: features: - name: id dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 43 num_examples: 1 download_size: 0 dataset_size: 43 --- # Dataset Card for "VQAv2_sample_validation_benchmarks_partition_global_6_loca_6" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
textminr/topic-labeling	--- language: - en - de size_categories: - n<1K configs: - config_name: gutenberg default: true data_files: - split: train path: - "gutenberg/train.jsonl" - config_name: generic data_files: - split: train path: - "base/train.jsonl" - "mn-ds/train.jsonl" task_categories: - text-classification pretty_name: Topic Labeling --- # Topic Labeling This is a carefully crafted dataset for topic labeling. It maps a series of words (generated by topic models like LDA, Top2Vec etc.) to a topic label so that LLMs like T5, Mistral etc. can be fine-tuned on this dataset to generate topic labels for a given text. Parts of this dataset have been labeled manually or using GPT-4. Source datasets: - [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) - [textminr/mn-ds](https://huggingface.co/datasets/textminr/mn-ds) Additional sources: - [Project Gutenberg](https://www.gutenberg.org/)
amogh-sinha/text	--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 24588.0 num_examples: 3 - name: test num_bytes: 8196 num_examples: 1 download_size: 22034 dataset_size: 32784.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/tuye_arknights	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tuye/トゥイエ/图耶 (Arknights) This is the dataset of tuye/トゥイエ/图耶 (Arknights), containing 69 images and their tags. The core tags of this character are `animal_ears, dark_skin, dark-skinned_female, purple_eyes, white_hair, long_hair, hair_between_eyes, horse_ears, grey_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 \| 69 \| 125.13 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tuye_arknights/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 69 \| 106.81 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tuye_arknights/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 184 \| 210.75 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tuye_arknights/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/tuye_arknights', 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 \| 35 \| ![](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, long_sleeves, looking_at_viewer, yellow_jacket, open_jacket, yellow_coat, black_gloves, holding, thigh_strap, black_dress, closed_mouth, fingerless_gloves, smile, twintails, umbrella \| \| 1 \| 12 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, long_sleeves, looking_at_viewer, solo, official_alternate_costume, short_hair, closed_mouth, white_ascot, white_gloves, white_thighhighs, black_dress, flower, garter_straps, smile, black_footwear, white_background, holding, shoes, simple_background, sitting \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| long_sleeves \| looking_at_viewer \| yellow_jacket \| open_jacket \| yellow_coat \| black_gloves \| holding \| thigh_strap \| black_dress \| closed_mouth \| fingerless_gloves \| smile \| twintails \| umbrella \| official_alternate_costume \| short_hair \| white_ascot \| white_gloves \| white_thighhighs \| flower \| garter_straps \| black_footwear \| white_background \| shoes \| simple_background \| sitting \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:---------------\|:--------------------\|:----------------\|:--------------\|:--------------\|:---------------\|:----------\|:--------------\|:--------------\|:---------------\|:--------------------\|:--------\|:------------\|:-----------\|:-----------------------------\|:-------------\|:--------------\|:---------------\|:-------------------\|:---------\|:----------------\|:-----------------\|:-------------------\|:--------\|:--------------------\|:----------\| \| 0 \| 35 \| ![](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 \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 12 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| \| \| \| \| X \| \| X \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
Vaibhav9401/toxic25m	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: llama_finetune_text dtype: string splits: - name: train num_bytes: 20143312184 num_examples: 25159680 download_size: 3446911922 dataset_size: 20143312184 --- # Dataset Card for "toxic25m" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ChrisWilson/twitter_dataset_1710354721	--- 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: 33402 num_examples: 89 download_size: 26069 dataset_size: 33402 configs: - config_name: default data_files: - split: train path: data/train-* ---
HarryAJMK418/NewEmINTER	--- license: openrail ---
romariocamilo/noah.mp3	--- license: openrail ---
Djacon/ru_goemotions	--- language: - ru license: - mit multilinguality: - monolingual task_categories: - text-classification task_ids: - multi-class-classification - multi-label-classification pretty_name: RuGoEmotions tags: - emotion --- # Dataset Card for GoEmotions ## 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 Summary The RuGoEmotions dataset contains 34k Reddit comments labeled for 9 emotion categories (joy, interest, surprice, sadness, anger, disgust, fear, guilt and neutral). The dataset already with predefined train/val/test splits ### Supported Tasks and Leaderboards This dataset is intended for multi-class, multi-label emotion classification. ### Languages The data is in Russian. ## Dataset Structure ### Data Instances Each instance is a reddit comment with one or more emotion annotations (or neutral). ### Data Fields The configuration includes: - `text`: the reddit comment - `labels`: the emotion annotations ### Data Splits The simplified data includes a set of train/val/test splits with 26.9k, 3.29k, and 3.37k examples respectively. ## Dataset Creation ### Curation Rationale From the paper abstract: > Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. ### Source Data #### Initial Data Collection and Normalization Data was collected from Reddit comments via a variety of automated methods discussed in 3.1 of the paper. #### Who are the source language producers? English-speaking Reddit users. ### Annotations #### Who are the annotators? Annotations were produced by 3 English-speaking crowdworkers in India. ### Personal and Sensitive Information This dataset includes the original usernames of the Reddit users who posted each comment. Although Reddit usernames are typically disasociated from personal real-world identities, this is not always the case. It may therefore be possible to discover the identities of the individuals who created this content in some cases. ## Considerations for Using the Data ### Social Impact of Dataset Emotion detection is a worthwhile problem which can potentially lead to improvements such as better human/computer interaction. However, emotion detection algorithms (particularly in computer vision) have been abused in some cases to make erroneous inferences in human monitoring and assessment applications such as hiring decisions, insurance pricing, and student attentiveness (see [this article](https://www.unite.ai/ai-now-institute-warns-about-misuse-of-emotion-detection-software-and-other-ethical-issues/)). ### Discussion of Biases From the authors' github page: > Potential biases in the data include: Inherent biases in Reddit and user base biases, the offensive/vulgar word lists used for data filtering, inherent or unconscious bias in assessment of offensive identity labels, annotators were all native English speakers from India. All these likely affect labelling, precision, and recall for a trained model. Anyone using this dataset should be aware of these limitations of the dataset. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Researchers at Amazon Alexa, Google Research, and Stanford. See the [author list](https://arxiv.org/abs/2005.00547). ### Licensing Information The GitHub repository which houses this dataset has an [Apache License 2.0](https://github.com/google-research/google-research/blob/master/LICENSE). ### Citation Information @inproceedings{demszky2020goemotions, author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith}, booktitle = {58th Annual Meeting of the Association for Computational Linguistics (ACL)}, title = {{GoEmotions: A Dataset of Fine-Grained Emotions}}, year = {2020} } ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.
xjlulu/ntu_adl_intent	--- license: apache-2.0 task_categories: - text-classification language: - en ---
CyberHarem/tsukumo_yatsuhashi_touhou	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tsukumo_yatsuhashi/九十九八橋 (Touhou) This is the dataset of tsukumo_yatsuhashi/九十九八橋 (Touhou), containing 253 images and their tags. The core tags of this character are `brown_hair, short_hair, hairband, brown_eyes, purple_hairband`, 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 \| 253 \| 190.77 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsukumo_yatsuhashi_touhou/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 253 \| 149.04 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsukumo_yatsuhashi_touhou/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 452 \| 250.06 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsukumo_yatsuhashi_touhou/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 253 \| 183.29 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsukumo_yatsuhashi_touhou/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 452 \| 296.12 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsukumo_yatsuhashi_touhou/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/tsukumo_yatsuhashi_touhou', 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 \| 12 \| ![](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, barefoot, black_skirt, smile, solo, white_shirt, collared_shirt, long_sleeves, simple_background, closed_mouth, eighth_note, frilled_skirt, full_body, white_background, looking_at_viewer, staff_(music), instrument \| \| 1 \| 15 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, long_sleeves, shirt, skirt, smile, solo, looking_at_viewer, eighth_note, open_mouth \| \| 2 \| 10 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, beamed_eighth_notes, black_skirt, collared_shirt, long_sleeves, looking_at_viewer, solo, white_shirt, bangs, beamed_sixteenth_notes, frilled_skirt, open_mouth, quarter_note, :d, hair_between_eyes, simple_background, staff_(music), blush, cowboy_shot, instrument \| \| 3 \| 17 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 2girls, shirt, smile, long_sleeves, open_mouth, sisters, barefoot, eighth_note, purple_hair, biwa_lute, hair_flower, looking_at_viewer, black_skirt \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| barefoot \| black_skirt \| smile \| solo \| white_shirt \| collared_shirt \| long_sleeves \| simple_background \| closed_mouth \| eighth_note \| frilled_skirt \| full_body \| white_background \| looking_at_viewer \| staff_(music) \| instrument \| shirt \| skirt \| open_mouth \| beamed_eighth_notes \| bangs \| beamed_sixteenth_notes \| quarter_note \| :d \| hair_between_eyes \| blush \| cowboy_shot \| 2girls \| sisters \| purple_hair \| biwa_lute \| hair_flower \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------\|:--------------\|:--------\|:-------\|:--------------\|:-----------------\|:---------------\|:--------------------\|:---------------\|:--------------\|:----------------\|:------------\|:-------------------\|:--------------------\|:----------------\|:-------------\|:--------\|:--------\|:-------------\|:----------------------\|:--------\|:-------------------------\|:---------------\|:-----\|:--------------------\|:--------\|:--------------\|:---------\|:----------\|:--------------\|:------------\|:--------------\| \| 0 \| 12 \| ![](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 \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 15 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| \| \| X \| X \| \| \| X \| \| \| X \| \| \| \| X \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 10 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| X \| \| X \| X \| X \| X \| X \| \| \| X \| \| \| X \| X \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| 3 \| 17 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| \| X \| X \| X \| \| \| \| X \| \| \| X \| \| \| \| X \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \|
theojiang/contrastive_conditional_vid_diff_std_1_6_webvid-test	--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: edited_image dtype: image splits: - name: train num_bytes: 73413092.0 num_examples: 2000 download_size: 72714847 dataset_size: 73413092.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
HamdanXI/arb-eng-parallel-1mill-splitted	--- dataset_info: features: - name: arabic dtype: string - name: english dtype: string splits: - name: train num_bytes: 343460673.8616423 num_examples: 800000 - name: validation num_bytes: 42932584.23270529 num_examples: 100000 - name: test num_bytes: 42932584.23270529 num_examples: 100000 download_size: 240888206 dataset_size: 429325842.3270529 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
AlekseyKorshuk/light-wild-chatml	--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: do_train dtype: bool - name: role dtype: string splits: - name: train num_bytes: 17574962 num_examples: 11556 download_size: 6961878 dataset_size: 17574962 --- # Dataset Card for "light-wild-chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Greich/linkedin_posts	--- license: odbl ---
AmelieSchreiber/binding_sites_random_split_by_family	--- license: mit --- This is a refined version of a dataset obtained from UniProt ([see here](https://www.uniprot.org/uniprotkb?facets=reviewed%3Atrue%2Cproteins_with%3A9&fields=accession%2Cprotein_families%2Cft_binding%2Cft_act_site%2Csequence&query=%28family%3A%29+AND+%28ft_binding%3A%29&view=table)). The data was first sorted by family, then random families were selected until approximately 20% of the data was separates out for test data. Next, each sequences longer than 1000 residues was segmented into non-overlapping sections of 1000 amino acids or less. Any sequences with only partial binding site annotations were thrown out (any sequences with `<`, `>`, or `?`).
apollo-research/sae-Skylion007-openwebtext-tokenizer-gpt2	--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 36178777200.0 num_examples: 8824092 download_size: 17731169875 dataset_size: 36178777200.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
tonyassi/celebrity-1000	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Aaron Eckhart '1': Aaron Paul '2': Aaron Rodgers '3': Aaron Taylor-Johnson '4': Abbi Jacobson '5': Abhishek Bachchan '6': Abigail Breslin '7': Abigail Spencer '8': Adam Brody '9': Adam Devine '10': Adam Driver '11': Adam Lambert '12': Adam Levine '13': Adam Sandler '14': Adam Scott '15': Adele '16': Adrian Grenier '17': Adèle Exarchopoulos '18': Aidan Gillen '19': Aidan Turner '20': Aishwarya Rai '21': Aja Naomi King '22': Alden Ehrenreich '23': Aldis Hodge '24': Alec Baldwin '25': Alex Morgan '26': Alex Pettyfer '27': Alex Rodriguez '28': Alexander Skarsgård '29': Alexandra Daddario '30': Alfre Woodard '31': Alia Shawkat '32': Alice Braga '33': Alice Eve '34': Alicia Keys '35': Alicia Vikander '36': Alison Brie '37': Allison Janney '38': Allison Williams '39': Alyson Hannigan '40': Amanda Peet '41': Amanda Seyfried '42': Amandla Stenberg '43': Amber Heard '44': America Ferrera '45': Amy Adams '46': Amy Poehler '47': Amy Schumer '48': Ana de Armas '49': Andie MacDowell '50': Andrew Garfield '51': Andrew Lincoln '52': Andrew Scott '53': Andy Garcia '54': Andy Samberg '55': Andy Serkis '56': Angela Bassett '57': Angelina Jolie '58': Anna Camp '59': Anna Faris '60': Anna Kendrick '61': Anna Paquin '62': AnnaSophia Robb '63': Annabelle Wallis '64': Anne Hathaway '65': Anne Marie '66': Anne-Marie '67': Ansel Elgort '68': Anson Mount '69': Anthony Hopkins '70': Anthony Joshua '71': Anthony Mackie '72': Antonio Banderas '73': Anya Taylor-Joy '74': Ariana Grande '75': Armie Hammer '76': Ashley Judd '77': Ashton Kutcher '78': Aubrey Plaza '79': Auli'i Cravalho '80': Awkwafina '81': Barack Obama '82': Bella Hadid '83': Bella Thorne '84': Ben Barnes '85': Ben Mendelsohn '86': Ben Stiller '87': Ben Whishaw '88': Benedict Cumberbatch '89': Benedict Wong '90': Benicio del Toro '91': Bill Gates '92': Bill Hader '93': Bill Murray '94': Bill Pullman '95': Bill Skarsgård '96': Billie Eilish '97': Billie Lourd '98': Billy Crudup '99': Billy Porter '100': Blake Lively '101': Bob Odenkirk '102': Bonnie Wright '103': Boyd Holbrook '104': Brad Pitt '105': Bradley Cooper '106': Brendan Fraser '107': Brian Cox '108': Brie Larson '109': Brittany Snow '110': Bryan Cranston '111': Bryce Dallas Howard '112': Busy Philipps '113': Caitriona Balfe '114': Cameron Diaz '115': Camila Cabello '116': Camila Mendes '117': Cardi B '118': Carey Mulligan '119': Carla Gugino '120': Carrie Underwood '121': Casey Affleck '122': Cate Blanchett '123': Catherine Keener '124': Catherine Zeta-Jones '125': Celine Dion '126': Chace Crawford '127': Chadwick Boseman '128': Channing Tatum '129': Charlie Cox '130': Charlie Day '131': Charlie Hunnam '132': Charlie Plummer '133': Charlize Theron '134': Chiara Ferragni '135': Chiwetel Ejiofor '136': Chloe Bennet '137': Chloe Grace Moretz '138': Chloe Sevigny '139': Chloë Grace Moretz '140': Chloë Sevigny '141': Chris Cooper '142': Chris Evans '143': Chris Hemsworth '144': Chris Martin '145': Chris Messina '146': Chris Noth '147': Chris O'Dowd '148': Chris Pine '149': Chris Pratt '150': Chris Tucker '151': Chrissy Teigen '152': Christian Bale '153': Christian Slater '154': Christina Aguilera '155': Christina Applegate '156': Christina Hendricks '157': Christina Milian '158': Christina Ricci '159': Christine Baranski '160': Christoph Waltz '161': Christopher Plummer '162': Christopher Walken '163': Cillian Murphy '164': Claire Foy '165': Clive Owen '166': Clive Standen '167': Cobie Smulders '168': Colin Farrell '169': Colin Firth '170': Colin Hanks '171': Connie Britton '172': Conor McGregor '173': Constance Wu '174': Constance Zimmer '175': Courteney Cox '176': Cristiano Ronaldo '177': Daisy Ridley '178': Dak Prescott '179': Dakota Fanning '180': Dakota Johnson '181': Damian Lewis '182': Dan Stevens '183': Danai Gurira '184': Dane DeHaan '185': Daniel Craig '186': Daniel Dae Kim '187': Daniel Day-Lewis '188': Daniel Gillies '189': Daniel Kaluuya '190': Daniel Mays '191': Daniel Radcliffe '192': Danny DeVito '193': Darren Criss '194': Dave Bautista '195': Dave Franco '196': Dave Grohl '197': Daveed Diggs '198': David Attenborough '199': David Beckham '200': David Duchovny '201': David Harbour '202': David Oyelowo '203': David Schwimmer '204': David Tennant '205': David Thewlis '206': Dax Shepard '207': Debra Messing '208': Demi Lovato '209': Dennis Quaid '210': Denzel Washington '211': Dermot Mulroney '212': Dev Patel '213': Diane Keaton '214': Diane Kruger '215': Diane Lane '216': Diego Boneta '217': Diego Luna '218': Djimon Hounsou '219': Dolly Parton '220': Domhnall Gleeson '221': Dominic Cooper '222': Dominic Monaghan '223': Dominic West '224': Don Cheadle '225': Donald Glover '226': Donald Sutherland '227': Donald Trump '228': Dua Lipa '229': Dwayne "The Rock" Johnson '230': Dwayne Johnson '231': Dylan O'Brien '232': Ed Harris '233': Ed Helms '234': Ed Sheeran '235': Eddie Murphy '236': Eddie Redmayne '237': Edgar Ramirez '238': Edward Norton '239': Eiza Gonzalez '240': Eiza González '241': Elijah Wood '242': Elisabeth Moss '243': Elisha Cuthbert '244': Eliza Coupe '245': Elizabeth Banks '246': Elizabeth Debicki '247': Elizabeth Lail '248': Elizabeth McGovern '249': Elizabeth Moss '250': Elizabeth Olsen '251': Elle Fanning '252': Ellen DeGeneres '253': Ellen Page '254': Ellen Pompeo '255': Ellie Goulding '256': Elon Musk '257': Emile Hirsch '258': Emilia Clarke '259': Emilia Fox '260': Emily Beecham '261': Emily Blunt '262': Emily Browning '263': Emily Deschanel '264': Emily Hampshire '265': Emily Mortimer '266': Emily Ratajkowski '267': Emily VanCamp '268': Emily Watson '269': Emma Bunton '270': Emma Chamberlain '271': Emma Corrin '272': Emma Mackey '273': Emma Roberts '274': Emma Stone '275': Emma Thompson '276': Emma Watson '277': Emmanuelle Chriqui '278': Emmy Rossum '279': Eoin Macken '280': Eric Bana '281': Ethan Hawke '282': Eva Green '283': Eva Longoria '284': Eva Mendes '285': Evan Peters '286': Evan Rachel Wood '287': Evangeline Lilly '288': Ewan McGregor '289': Ezra Miller '290': Felicity Huffman '291': Felicity Jones '292': Finn Wolfhard '293': Florence Pugh '294': Florence Welch '295': Forest Whitaker '296': Freddie Highmore '297': Freddie Prinze Jr. '298': Freema Agyeman '299': Freida Pinto '300': Freya Allan '301': Gabrielle Union '302': Gael Garcia Bernal '303': Gael García Bernal '304': Gal Gadot '305': Garrett Hedlund '306': Gary Oldman '307': Gemma Arterton '308': Gemma Chan '309': Gemma Whelan '310': George Clooney '311': George Lucas '312': Gerard Butler '313': Giancarlo Esposito '314': Giannis Antetokounmpo '315': Gigi Hadid '316': Gillian Anderson '317': Gillian Jacobs '318': Gina Carano '319': Gina Gershon '320': Gina Rodriguez '321': Ginnifer Goodwin '322': Gisele Bundchen '323': Glenn Close '324': Grace Kelly '325': Greg Kinnear '326': Greta Gerwig '327': Greta Scacchi '328': Greta Thunberg '329': Gugu Mbatha-Raw '330': Guy Ritchie '331': Gwen Stefani '332': Gwendoline Christie '333': Gwyneth Paltrow '334': Hafthor Bjornsson '335': Hailee Steinfeld '336': Hailey Bieber '337': Haley Joel Osment '338': Halle Berry '339': Hannah Simone '340': Harrison Ford '341': Harry Styles '342': Harvey Weinstein '343': Hayden Panettiere '344': Hayley Atwell '345': Helen Hunt '346': Helen Mirren '347': Helena Bonham Carter '348': Henry Cavill '349': Henry Golding '350': Hilary Swank '351': Himesh Patel '352': Hozier '353': Hugh Bonneville '354': Hugh Dancy '355': Hugh Grant '356': Hugh Jackman '357': Hugh Laurie '358': Ian Somerhalder '359': Idris Elba '360': Imelda Staunton '361': Imogen Poots '362': Ioan Gruffudd '363': Isabella Rossellini '364': Isabelle Huppert '365': Isla Fisher '366': Issa Rae '367': Iwan Rheon '368': J.K. Rowling '369': J.K. Simmons '370': Jack Black '371': Jack Reynor '372': Jack Whitehall '373': Jackie Chan '374': Jada Pinkett Smith '375': Jaden Smith '376': Jaimie Alexander '377': Jake Gyllenhaal '378': Jake Johnson '379': Jake T. Austin '380': James Cameron '381': James Corden '382': James Franco '383': James Marsden '384': James McAvoy '385': James Norton '386': Jamie Bell '387': Jamie Chung '388': Jamie Dornan '389': Jamie Foxx '390': Jamie Lee Curtis '391': Jamie Oliver '392': Jane Fonda '393': Jane Krakowski '394': Jane Levy '395': Jane Lynch '396': Jane Seymour '397': Janelle Monáe '398': January Jones '399': Jared Leto '400': Jason Bateman '401': Jason Clarke '402': Jason Derulo '403': Jason Isaacs '404': Jason Momoa '405': Jason Mraz '406': Jason Schwartzman '407': Jason Segel '408': Jason Statham '409': Jason Sudeikis '410': Javier Bardem '411': Jay Baruchel '412': Jay-Z '413': Jeff Bezos '414': Jeff Bridges '415': Jeff Daniels '416': Jeff Goldblum '417': Jeffrey Dean Morgan '418': Jeffrey Donovan '419': Jeffrey Wright '420': Jemima Kirke '421': Jenna Coleman '422': Jenna Fischer '423': Jenna Ortega '424': Jennifer Aniston '425': Jennifer Connelly '426': Jennifer Coolidge '427': Jennifer Esposito '428': Jennifer Garner '429': Jennifer Hudson '430': Jennifer Lawrence '431': Jennifer Lopez '432': Jennifer Love Hewitt '433': Jenny Slate '434': Jeremy Irons '435': Jeremy Renner '436': Jeremy Strong '437': Jerry Seinfeld '438': Jesse Eisenberg '439': Jesse Metcalfe '440': Jesse Plemons '441': Jesse Tyler Ferguson '442': Jesse Williams '443': Jessica Alba '444': Jessica Biel '445': Jessica Chastain '446': Jessica Lange '447': Jessie Buckley '448': Jim Carrey '449': Jim Parsons '450': Joan Collins '451': Joan Cusack '452': Joanne Froggatt '453': Joaquin Phoenix '454': Jodie Comer '455': Jodie Foster '456': Joe Jonas '457': Joe Keery '458': Joel Edgerton '459': Joel Kinnaman '460': Joel McHale '461': John Boyega '462': John C. Reilly '463': John Cena '464': John Cho '465': John Cleese '466': John Corbett '467': John David Washington '468': John Goodman '469': John Hawkes '470': John Krasinski '471': John Legend '472': John Leguizamo '473': John Lithgow '474': John Malkovich '475': John Mayer '476': John Mulaney '477': John Oliver '478': John Slattery '479': John Travolta '480': John Turturro '481': Johnny Depp '482': Johnny Knoxville '483': Jon Bernthal '484': Jon Favreau '485': Jon Hamm '486': Jonah Hill '487': Jonathan Groff '488': Jonathan Majors '489': Jonathan Pryce '490': Jonathan Rhys Meyers '491': Jordan Peele '492': Jordana Brewster '493': Joseph Fiennes '494': Joseph Gordon-Levitt '495': Josh Allen '496': Josh Brolin '497': Josh Gad '498': Josh Hartnett '499': Josh Hutcherson '500': Josh Radnor '501': Jude Law '502': Judy Dench '503': Judy Greer '504': Julia Garner '505': Julia Louis-Dreyfus '506': Julia Roberts '507': Julia Stiles '508': Julian Casablancas '509': Julian McMahon '510': Julianna Margulies '511': Julianne Hough '512': Julianne Moore '513': Julianne Nicholson '514': Juliette Binoche '515': Juliette Lewis '516': Juno Temple '517': Jurnee Smollett '518': Justin Bartha '519': Justin Bieber '520': Justin Hartley '521': Justin Herbert '522': Justin Long '523': Justin Theroux '524': Justin Timberlake '525': KJ Apa '526': Kaitlyn Dever '527': Kaley Cuoco '528': Kanye West '529': Karl Urban '530': Kat Dennings '531': Kate Beckinsale '532': Kate Bosworth '533': Kate Hudson '534': Kate Mara '535': Kate Middleton '536': Kate Upton '537': Kate Walsh '538': Kate Winslet '539': Katee Sackhoff '540': Katherine Heigl '541': Katherine Langford '542': Katherine Waterston '543': Kathryn Hahn '544': Katie Holmes '545': Katie McGrath '546': Katy Perry '547': Kaya Scodelario '548': Keanu Reeves '549': Keegan-Michael Key '550': Keira Knightley '551': Keke Palmer '552': Kelly Clarkson '553': Kelly Macdonald '554': Kelly Marie Tran '555': Kelly Reilly '556': Kelly Ripa '557': Kelvin Harrison Jr. '558': Keri Russell '559': Kerry Washington '560': Kevin Bacon '561': Kevin Costner '562': Kevin Hart '563': Kevin Spacey '564': Ki Hong Lee '565': Kiefer Sutherland '566': Kieran Culkin '567': Kiernan Shipka '568': Kim Dickens '569': Kim Kardashian '570': Kirsten Dunst '571': Kit Harington '572': Kourtney Kardashian '573': Kristen Bell '574': Kristen Stewart '575': Kristen Wiig '576': Kristin Davis '577': Krysten Ritter '578': Kyle Chandler '579': Kylie Jenner '580': Kylie Minogue '581': Lady Gaga '582': Lake Bell '583': Lakeith Stanfield '584': Lamar Jackson '585': Lana Del Rey '586': Laura Dern '587': Laura Harrier '588': Laura Linney '589': Laura Prepon '590': Laurence Fishburne '591': Laverne Cox '592': LeBron James '593': Lea Michele '594': Lea Seydoux '595': Lee Pace '596': Leighton Meester '597': Lena Headey '598': Leonardo Da Vinci '599': Leonardo DiCaprio '600': Leslie Mann '601': Leslie Odom Jr. '602': Lewis Hamilton '603': Liam Hemsworth '604': Liam Neeson '605': Lili Reinhart '606': Lily Aldridge '607': Lily Allen '608': Lily Collins '609': Lily James '610': Lily Rabe '611': Lily Tomlin '612': Lin-Manuel Miranda '613': Linda Cardellini '614': Lionel Messi '615': Lisa Bonet '616': Lisa Kudrow '617': Liv Tyler '618': Lizzo '619': Logan Lerman '620': Lorde '621': Lucy Boynton '622': Lucy Hale '623': Lucy Lawless '624': Lucy Liu '625': Luke Evans '626': Luke Perry '627': Luke Wilson '628': Lupita Nyong'o '629': Léa Seydoux '630': Mackenzie Davis '631': Madelaine Petsch '632': Mads Mikkelsen '633': Mae Whitman '634': Maggie Gyllenhaal '635': Maggie Q '636': Maggie Siff '637': Maggie Smith '638': Mahershala Ali '639': Mahira Khan '640': Maisie Richardson-Sellers '641': Maisie Williams '642': Mandy Moore '643': Mandy Patinkin '644': Marc Anthony '645': Margaret Qualley '646': Margot Robbie '647': Maria Sharapova '648': Marion Cotillard '649': Marisa Tomei '650': Mariska Hargitay '651': Mark Hamill '652': Mark Ruffalo '653': Mark Strong '654': Mark Wahlberg '655': Mark Zuckerberg '656': Marlon Brando '657': Martin Freeman '658': Martin Scorsese '659': Mary Elizabeth Winstead '660': Mary J. Blige '661': Mary Steenburgen '662': Mary-Louise Parker '663': Matt Bomer '664': Matt Damon '665': Matt LeBlanc '666': Matt Smith '667': Matthew Fox '668': Matthew Goode '669': Matthew Macfadyen '670': Matthew McConaughey '671': Matthew Perry '672': Matthew Rhys '673': Matthew Stafford '674': Max Minghella '675': Maya Angelou '676': Maya Hawke '677': Maya Rudolph '678': Megan Fox '679': Megan Rapinoe '680': Meghan Markle '681': Mel Gibson '682': Melanie Lynskey '683': Melissa Benoist '684': Melissa McCarthy '685': Melonie Diaz '686': Meryl Streep '687': Mia Wasikowska '688': Michael B. Jordan '689': Michael C. Hall '690': Michael Caine '691': Michael Cera '692': Michael Cudlitz '693': Michael Douglas '694': Michael Ealy '695': Michael Fassbender '696': Michael Jordan '697': Michael Keaton '698': Michael Pena '699': Michael Peña '700': Michael Phelps '701': Michael Shannon '702': Michael Sheen '703': Michael Stuhlbarg '704': Michelle Dockery '705': Michelle Monaghan '706': Michelle Obama '707': Michelle Pfeiffer '708': Michelle Rodriguez '709': Michelle Williams '710': Michelle Yeoh '711': Michiel Huisman '712': Mila Kunis '713': Miles Teller '714': Milla Jovovich '715': Millie Bobby Brown '716': Milo Ventimiglia '717': Mindy Kaling '718': Miranda Cosgrove '719': Miranda Kerr '720': Mireille Enos '721': Molly Ringwald '722': Morgan Freeman '723': Mélanie Laurent '724': Naomi Campbell '725': Naomi Harris '726': Naomi Scott '727': Naomi Watts '728': Naomie Harris '729': Nas '730': Natalie Dormer '731': Natalie Imbruglia '732': Natalie Morales '733': Natalie Portman '734': Nathalie Emmanuel '735': Nathalie Portman '736': Nathan Fillion '737': Naya Rivera '738': Neil Patrick Harris '739': Neil deGrasse Tyson '740': Neve Campbell '741': Neymar Jr. '742': Nicholas Braun '743': Nicholas Hoult '744': Nick Jonas '745': Nick Kroll '746': Nick Offerman '747': Nick Robinson '748': Nicole Kidman '749': Nikolaj Coster-Waldau '750': Nina Dobrev '751': Noah Centineo '752': Noomi Rapace '753': Norman Reedus '754': Novak Djokovic '755': Octavia Spencer '756': Odessa Young '757': Odette Annable '758': Olivia Colman '759': Olivia Cooke '760': Olivia Holt '761': Olivia Munn '762': Olivia Wilde '763': Oprah Winfrey '764': Orlando Bloom '765': Oscar Isaac '766': Owen Wilson '767': Pablo Picasso '768': Patrick Dempsey '769': Patrick Mahomes '770': Patrick Stewart '771': Patrick Wilson '772': Paul Bettany '773': Paul Dano '774': Paul Giamatti '775': Paul McCartney '776': Paul Rudd '777': Paul Wesley '778': Paula Patton '779': Pedro Almodóvar '780': Pedro Pascal '781': Penelope Cruz '782': Penélope Cruz '783': Pete Davidson '784': Peter Dinklage '785': Phoebe Dynevor '786': Phoebe Waller-Bridge '787': Pierce Brosnan '788': Portia de Rossi '789': Priyanka Chopra '790': Quentin Tarantino '791': Rachel Bilson '792': Rachel Brosnahan '793': Rachel McAdams '794': Rachel Weisz '795': Rafe Spall '796': Rainn Wilson '797': Ralph Fiennes '798': Rami Malek '799': Rashida Jones '800': Ray Liotta '801': Ray Romano '802': Rebecca Ferguson '803': Rebecca Hall '804': Reese Witherspoon '805': Regina Hall '806': Regina King '807': Renee Zellweger '808': Renée Zellweger '809': Rhys Ifans '810': Ricardo Montalban '811': Richard Armitage '812': Richard Gere '813': Richard Jenkins '814': Richard Madden '815': Ricky Gervais '816': Ricky Martin '817': Rihanna '818': Riley Keough '819': Rita Ora '820': River Phoenix '821': Riz Ahmed '822': Rob Lowe '823': Robert Carlyle '824': Robert De Niro '825': Robert Downey Jr. '826': Robert Pattinson '827': Robert Sheehan '828': Robin Tunney '829': Robin Williams '830': Roger Federer '831': Rooney Mara '832': Rosamund Pike '833': Rosario Dawson '834': Rose Byrne '835': Rose Leslie '836': Roselyn Sanchez '837': Ruby Rose '838': Rupert Grint '839': Russell Brand '840': Russell Crowe '841': Russell Wilson '842': Ruth Bader Ginsburg '843': Ruth Wilson '844': Ryan Eggold '845': Ryan Gosling '846': Ryan Murphy '847': Ryan Phillippe '848': Ryan Reynolds '849': Ryan Seacrest '850': Salma Hayek '851': Sam Claflin '852': Sam Heughan '853': Sam Rockwell '854': Sam Smith '855': Samara Weaving '856': Samuel L. Jackson '857': Sandra Bullock '858': Sandra Oh '859': Saoirse Ronan '860': Sarah Gadon '861': Sarah Hyland '862': Sarah Jessica Parker '863': Sarah Michelle Gellar '864': Sarah Paulson '865': Sarah Silverman '866': Sarah Wayne Callies '867': Sasha Alexander '868': Scarlett Johansson '869': Scott Speedman '870': Sean Bean '871': Sebastian Stan '872': Selena Gomez '873': Selma Blair '874': Serena Williams '875': Seth MacFarlane '876': Seth Meyers '877': Seth Rogen '878': Shailene Woodley '879': Shakira '880': Shania Twain '881': Sharlto Copley '882': Shawn Mendes '883': Shia LaBeouf '884': Shiri Appleby '885': Shohreh Aghdashloo '886': Shonda Rhimes '887': Sienna Miller '888': Sigourney Weaver '889': Simon Baker '890': Simon Cowell '891': Simon Pegg '892': Simone Biles '893': Sofia Boutella '894': Sofia Vergara '895': Sophie Turner '896': Sophie Wessex '897': Stanley Tucci '898': Stephen Amell '899': Stephen Colbert '900': Stephen Curry '901': Stephen Dorff '902': Sterling K. Brown '903': Sterling Knight '904': Steve Carell '905': Steven Yeun '906': Susan Sarandon '907': Taika Waititi '908': Taraji P. Henson '909': Taron Egerton '910': Taylor Hill '911': Taylor Kitsch '912': Taylor Lautner '913': Taylor Schilling '914': Taylor Swift '915': Teresa Palmer '916': Terrence Howard '917': Tessa Thompson '918': Thandie Newton '919': The Weeknd '920': Theo James '921': Thomas Brodie-Sangster '922': Thomas Jane '923': Tiger Woods '924': Tilda Swinton '925': Tim Burton '926': Tim Cook '927': Timothee Chalamet '928': Timothy Olyphant '929': Timothy Spall '930': Timothée Chalamet '931': Tina Fey '932': Tobey Maguire '933': Toby Jones '934': Toby Kebbell '935': Toby Regbo '936': Tom Brady '937': Tom Brokaw '938': Tom Cavanagh '939': Tom Cruise '940': Tom Ellis '941': Tom Felton '942': Tom Hanks '943': Tom Hardy '944': Tom Hiddleston '945': Tom Holland '946': Tom Hollander '947': Tom Hopper '948': Tom Selleck '949': Toni Collette '950': Tony Hale '951': Topher Grace '952': Tracee Ellis Ross '953': Tyra Banks '954': Tyrese Gibson '955': Uma Thurman '956': Usain Bolt '957': Uzo Aduba '958': Vanessa Hudgens '959': Vanessa Kirby '960': Vera Farmiga '961': Victoria Pedretti '962': Viggo Mortensen '963': Vin Diesel '964': Vince Vaughn '965': Vincent Cassel '966': Vincent D'Onofrio '967': Vincent Kartheiser '968': Viola Davis '969': Walton Goggins '970': Wes Anderson '971': Wes Bentley '972': Whoopi Goldberg '973': Will Ferrell '974': Will Poulter '975': Willem Dafoe '976': William Jackson Harper '977': William Shatner '978': Winona Ryder '979': Woody Harrelson '980': Yara Shahidi '981': Yvonne Strahovski '982': Zac Efron '983': Zach Braff '984': Zach Galifianakis '985': Zachary Levi '986': Zachary Quinto '987': Zayn Malik '988': Zazie Beetz '989': Zendaya '990': Zoe Kazan '991': Zoe Kravitz '992': Zoe Saldana '993': Zoey Deutch '994': Zooey Deschanel '995': Zoë Kravitz '996': Zoë Saldana splits: - name: train num_bytes: 193671657.464 num_examples: 18184 download_size: 190510261 dataset_size: 193671657.464 configs: - config_name: default data_files: - split: train path: data/train-* --- # Celebrity 1000 Top 1000 celebrities. 18,184 images. 256x256. Square cropped to face.
ncats/EpiSet4NER-v1	--- annotations_creators: - train: programmatically-generated - val: programmatically-generated - test: programmatically-generated, expert-validated language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M task_categories: - structure-prediction task_ids: - named-entity-recognition --- ## 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 - Repository: [Github](https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard) - Paper: Pending ### Dataset Summary EpiSet4NER is a bronze-standard dataset for epidemiological entity recognition of location, epidemiologic types (e.g. "prevalence", "annual incidence", "estimated occurrence"), and epidemiological rates (e.g. "1.7 per 1,000,000 live births", "2.1:1.000.000", "one in five million", "0.03%") created by the [Genetic and Rare Diseases Information Center (GARD)](https://rarediseases.info.nih.gov/), a program in [the National Center for Advancing Translational Sciences](https://ncats.nih.gov/), one of the 27 [National Institutes of Health](https://www.nih.gov/). It was labeled programmatically using spaCy NER and rule-based methods. This weakly-supervised teaching method allowed us to construct this imprecise dataset with minimal manual effort and achieve satisfactory performance on a multi-type token classification problem. The test set was manually corrected by 3 NCATS researchers and a GARD curator (genetic and rare disease expert). It was used to train [EpiExtract4GARD](https://huggingface.co/ncats/EpiExtract4GARD), a BioBERT-based model fine-tuned for NER. An [example](https://pubmed.ncbi.nlm.nih.gov/24237863/) of 'train' looks as follows. ``` { "id": "333", "tokens": ['Conclusions', 'The', 'birth', 'prevalence', 'of', 'CLD', 'in', 'the', 'northern', 'Netherlands', 'was', '21.1/10,000', 'births', '.'], "ner_tags": [0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 5, 6, 0], } ``` ### Data Fields The data fields are the same among all splits. - `id`: a `string` feature that indicates sentence number. - `tokens`: a `list` of `string` features. - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-EPI` (3), `I-EPI` (4),`B-STAT` (5),`I-STAT` (6). ### Data Splits \|name \|train \|validation\|test\| \|---------\|-----:\|----:\|----:\| \|EpiSet \# of abstracts\|456\|114\|50\| \|EpiSet \# tokens \|117888\|31262\|13910\| ## Dataset Creation ![EpiSet Creation Flowchart](https://raw.githubusercontent.com/ncats/epi4GARD/master/EpiExtract4GARD/datasets/EpiCustomV3/EpiSet%20Flowchart%20FINAL.png) Figure 1: Creation of EpiSet4NER by NIH/NCATS Comparing the programmatically labeled test set to the manually corrected test set allowed us to measure the precision, recall, and F1 of the programmatic labeling. Table 1: Programmatic labeling of EpiSet4NER \| Evaluation Level \| Entity \| Precision \| Recall \| F1 \| \|:----------------:\|:------------------------:\|:---------:\|:------:\|:-----:\| \| Entity-Level \| Overall \| 0.559 \| 0.662 \| 0.606 \| \| \| Location \| 0.597 \| 0.661 \| 0.627 \| \| \| Epidemiologic Type \| 0.854 \| 0.911 \| 0.882 \| \| \| Epidemiologic Rate \| 0.175 \| 0.255 \| 0.207 \| \| Token-Level \| Overall \| 0.805 \| 0.710 \| 0.755 \| \| \| Location \| 0.868 \| 0.713 \| 0.783 \| \| \| Epidemiologic Type \| 0.908 \| 0.908 \| 0.908 \| \| \| Epidemiologic Rate \| 0.739 \| 0.645 \| 0.689 \| An example of the text labeling: ![Text Labeling](https://raw.githubusercontent.com/ncats/epi4GARD/master/EpiExtract4GARD/datasets/EpiCustomV3/Text%20Labeling4.png) Figure 2: Text Labeling using spaCy and rule-based labeling. Ideal labeling is bolded on the left. Actual programmatic output is on the right. [\[Figure citation\]](https://pubmed.ncbi.nlm.nih.gov/33649778/) ### Curation Rationale To train ML/DL models that automate the process of rare disease epidemiological curation. This is crucial information to patients & families, researchers, grantors, and policy makers, primarily for funding purposes. ### Source Data 620 rare disease abstracts classified as epidemiological by a LSTM RNN rare disease epi classifier from 488 diseases. See Figure 1. #### Initial Data Collection and Normalization A random sample of 500 disease names were gathered from a list of ~6061 rare diseases tracked by GARD until ≥50 abstracts had been returned for each disease or the EBI RESTful API results were exhausted. Though we called ~25,000 abstracts from PubMed's db, only 7699 unique abstracts were returned for 488 diseases. Out of 7699 abstracts, only 620 were classified as epidemiological by the LSTM RNN epidemiological classifier. ### Annotations #### Annotation process Programmatic labeling. See [here](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/create_labeled_dataset_V2.ipynb) and then [here](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/modify_existing_labels.ipynb). The test set was manually corrected after creation. #### Who are the annotators? Programmatic labeling was done by [@William Kariampuzha](https://github.com/wzkariampuzha), one of the NCATS researchers. The test set was manually corrected by 2 more NCATS researchers and a GARD curator (genetic and rare disease expert). ### Personal and Sensitive Information None. These are freely available abstracts from PubMed. ## Considerations for Using the Data ### Social Impact of Dataset Assisting 25-30 millions Americans with rare diseases. Additionally can be useful for Orphanet or CDC researchers/curators. ### Discussion of Biases and Limitations - There were errors in the source file that contained rare disease synonyms of names, which may have led to some unrelated abstracts being included in the training, validation, and test sets. - The abstracts were gathered through the EBI API and is thus subject to any biases that the EBI API had. The NCBI API returns very different results as shown by an API analysis here. - The [long short-term memory recurrent neural network epi classifier](https://pubmed.ncbi.nlm.nih.gov/34457147/) was used to sift the 7699 rare disease abstracts. This model had a hold-out validation F1 score of 0.886 and a test F1 (which was compared against a GARD curator who used full-text articles to determine truth-value of epidemiological abstract) of 0.701. With 620 epi abstracts filtered from 7699 original rare disease abstracts, there are likely several false positives and false negative epi abstracts. - Tokenization was done by spaCy which may be a limitation (or not) for current and future models trained on this set. - The programmatic labeling was very imprecise as seen by Table 1. This is likely the largest limitation of the [BioBERT-based model](https://huggingface.co/ncats/EpiExtract4GARD) trained on this set. - The test set was difficult to validate even for general NCATS researchers, which is why we relied on a rare disease expert to verify our modifications. As this task of epidemiological information identification is quite difficult for non-expert humans to complete, this set, and especially a gold-standard dataset in the possible future, represents a challenging gauntlet for NLP systems, especially those focusing on numeracy, to compete on. ## Additional Information ### Dataset Curators [NIH GARD](https://rarediseases.info.nih.gov/about-gard/pages/23/about-gard) ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@William Kariampuzha](https://github.com/wzkariampuzha) at NCATS/Axle Informatics for adding this dataset.
ryan0712/ultra_no_robots	--- license: unknown ---
cmagganas/zero_shot_comparison	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: rationale dtype: string - name: task dtype: string - name: type dtype: string - name: decilm_generation dtype: string - name: mistral_generation dtype: string - name: mpt_generation dtype: string splits: - name: train num_bytes: 96015 num_examples: 30 download_size: 59751 dataset_size: 96015 configs: - config_name: default data_files: - split: train path: data/train-* ---
AnudeepPeela/starcoder-finetune	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: Input dtype: string - name: Output dtype: string splits: - name: train num_bytes: 892 num_examples: 8 - name: test num_bytes: 3328 num_examples: 20 download_size: 2251 dataset_size: 4220 --- # Dataset Card for "starcoder-finetune" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
abhi28577/nennepedia	--- license: openrail task_categories: - question-answering language: - en pretty_name: nennepedia size_categories: - n<1K --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### 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]
shi3z/anthropic_hh_rlhf_japanese	--- license: mit --- https://huggingface.co/datasets/Anthropic/hh-rlhf Japanese Translation
7eu7d7/HCP-Diffusion-datas	--- license: apache-2.0 --- Anime prompt dataset (动漫风格数据集): + danbooru-160000.parquet Natural scenes prompt dataset (真实风格数据集): + stable-diffusion-prompts-160000.parquet + stable-diffusion-prompts2-320000.parquet Artistic style dataset (艺术风格数据集): + Lexica.art.parquet
blinoff/restaurants_reviews	--- language: - ru multilinguality: - monolingual size_categories: - 10K<n<100K task_categories: - text-classification task_ids: - sentiment-classification --- ### Dataset Summary The dataset contains user reviews about restaurants. In total it contains 47,139 reviews. A review tagged with the <em>general</em> sentiment and sentiments on 3 aspects: <em>food, interior, service</em>. ### Data Fields Each sample contains the following fields: - review_id; - general; - food; - interior; - service; - text review text. ### Python ```python3 import pandas as pd df = pd.read_json('restaurants_reviews.jsonl', lines=True) df.sample(5) ```
cassanof/italian-conversations	--- dataset_info: features: - name: content dtype: string splits: - name: train num_bytes: 223555130.20512977 num_examples: 115237 download_size: 113639002 dataset_size: 223555130.20512977 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-staging-eval-project-e438add5-1e56-41ec-9c26-2ad4182383b0-6260	--- type: predictions tags: - autotrain - evaluation datasets: - autoevaluate/squad-sample eval_info: task: extractive_question_answering model: autoevaluate/extractive-question-answering metrics: [] dataset_name: autoevaluate/squad-sample dataset_config: autoevaluate--squad-sample dataset_split: test col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: autoevaluate/extractive-question-answering * Dataset: autoevaluate/squad-sample * Config: autoevaluate--squad-sample * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
elenahuang/primary-sector-top-1k	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 8620437 num_examples: 1000 download_size: 4571154 dataset_size: 8620437 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "primary-sector-top-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
llm-aes/doc-storygen-v2	--- dataset_info: features: - name: worker_id dtype: string - name: task_id dtype: string - name: task_response_id dtype: string - name: id dtype: int64 - name: premise dtype: string - name: plan1 dtype: string - name: plan2 dtype: string - name: Q1 dtype: string - name: Q2 dtype: string - name: Q3 dtype: string - name: Q4 dtype: string - name: Q5 dtype: string - name: Q6 dtype: string splits: - name: train num_bytes: 60995214 num_examples: 7000 download_size: 28333525 dataset_size: 60995214 configs: - config_name: default data_files: - split: train path: data/train-* ---
confit/esc50-csv	--- dataset_info: - config_name: fold-1 features: - name: filename dtype: string - name: label dtype: class_label: names: '0': dog '1': rooster '2': pig '3': cow '4': frog '5': cat '6': hen '7': insects '8': sheep '9': crow '10': rain '11': sea_waves '12': crackling_fire '13': crickets '14': chirping_birds '15': water_drops '16': wind '17': pouring_water '18': toilet_flush '19': thunderstorm '20': crying_baby '21': sneezing '22': clapping '23': breathing '24': coughing '25': footsteps '26': laughing '27': brushing_teeth '28': snoring '29': drinking_sipping '30': door_wood_knock '31': mouse_click '32': keyboard_typing '33': door_wood_creaks '34': can_opening '35': washing_machine '36': vacuum_cleaner '37': clock_alarm '38': clock_tick '39': glass_breaking '40': helicopter '41': chainsaw '42': siren '43': car_horn '44': engine '45': train '46': church_bells '47': airplane '48': fireworks '49': hand_saw splits: - name: train num_bytes: 11167 num_examples: 400 download_size: 7653 dataset_size: 11167 - config_name: fold-2 features: - name: filename dtype: string - name: label dtype: class_label: names: '0': dog '1': rooster '2': pig '3': cow '4': frog '5': cat '6': hen '7': insects '8': sheep '9': crow '10': rain '11': sea_waves '12': crackling_fire '13': crickets '14': chirping_birds '15': water_drops '16': wind '17': pouring_water '18': toilet_flush '19': thunderstorm '20': crying_baby '21': sneezing '22': clapping '23': breathing '24': coughing '25': footsteps '26': laughing '27': brushing_teeth '28': snoring '29': drinking_sipping '30': door_wood_knock '31': mouse_click '32': keyboard_typing '33': door_wood_creaks '34': can_opening '35': washing_machine '36': vacuum_cleaner '37': clock_alarm '38': clock_tick '39': glass_breaking '40': helicopter '41': chainsaw '42': siren '43': car_horn '44': engine '45': train '46': church_bells '47': airplane '48': fireworks '49': hand_saw splits: - name: train num_bytes: 11335 num_examples: 400 download_size: 7627 dataset_size: 11335 - config_name: fold-3 features: - name: filename dtype: string - name: label dtype: class_label: names: '0': dog '1': rooster '2': pig '3': cow '4': frog '5': cat '6': hen '7': insects '8': sheep '9': crow '10': rain '11': sea_waves '12': crackling_fire '13': crickets '14': chirping_birds '15': water_drops '16': wind '17': pouring_water '18': toilet_flush '19': thunderstorm '20': crying_baby '21': sneezing '22': clapping '23': breathing '24': coughing '25': footsteps '26': laughing '27': brushing_teeth '28': snoring '29': drinking_sipping '30': door_wood_knock '31': mouse_click '32': keyboard_typing '33': door_wood_creaks '34': can_opening '35': washing_machine '36': vacuum_cleaner '37': clock_alarm '38': clock_tick '39': glass_breaking '40': helicopter '41': chainsaw '42': siren '43': car_horn '44': engine '45': train '46': church_bells '47': airplane '48': fireworks '49': hand_saw splits: - name: train num_bytes: 11480 num_examples: 400 download_size: 7594 dataset_size: 11480 - config_name: fold-4 features: - name: filename dtype: string - name: label dtype: class_label: names: '0': dog '1': rooster '2': pig '3': cow '4': frog '5': cat '6': hen '7': insects '8': sheep '9': crow '10': rain '11': sea_waves '12': crackling_fire '13': crickets '14': chirping_birds '15': water_drops '16': wind '17': pouring_water '18': toilet_flush '19': thunderstorm '20': crying_baby '21': sneezing '22': clapping '23': breathing '24': coughing '25': footsteps '26': laughing '27': brushing_teeth '28': snoring '29': drinking_sipping '30': door_wood_knock '31': mouse_click '32': keyboard_typing '33': door_wood_creaks '34': can_opening '35': washing_machine '36': vacuum_cleaner '37': clock_alarm '38': clock_tick '39': glass_breaking '40': helicopter '41': chainsaw '42': siren '43': car_horn '44': engine '45': train '46': church_bells '47': airplane '48': fireworks '49': hand_saw splits: - name: train num_bytes: 11508 num_examples: 400 download_size: 7602 dataset_size: 11508 - config_name: fold-5 features: - name: filename dtype: string - name: label dtype: class_label: names: '0': dog '1': rooster '2': pig '3': cow '4': frog '5': cat '6': hen '7': insects '8': sheep '9': crow '10': rain '11': sea_waves '12': crackling_fire '13': crickets '14': chirping_birds '15': water_drops '16': wind '17': pouring_water '18': toilet_flush '19': thunderstorm '20': crying_baby '21': sneezing '22': clapping '23': breathing '24': coughing '25': footsteps '26': laughing '27': brushing_teeth '28': snoring '29': drinking_sipping '30': door_wood_knock '31': mouse_click '32': keyboard_typing '33': door_wood_creaks '34': can_opening '35': washing_machine '36': vacuum_cleaner '37': clock_alarm '38': clock_tick '39': glass_breaking '40': helicopter '41': chainsaw '42': siren '43': car_horn '44': engine '45': train '46': church_bells '47': airplane '48': fireworks '49': hand_saw splits: - name: train num_bytes: 11516 num_examples: 400 download_size: 7644 dataset_size: 11516 configs: - config_name: fold-1 data_files: - split: train path: fold-1/train-* - config_name: fold-2 data_files: - split: train path: fold-2/train-* - config_name: fold-3 data_files: - split: train path: fold-3/train-* - config_name: fold-4 data_files: - split: train path: fold-4/train-* - config_name: fold-5 data_files: - split: train path: fold-5/train-* ---
eengel7/sentiment_analysis_training	--- license: apache-2.0 ---
nyunai/samvaad-hi-v1-tulu-format	--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: text dtype: string splits: - name: train num_bytes: 936264338 num_examples: 101476 download_size: 403495470 dataset_size: 936264338 configs: - config_name: default data_files: - split: train path: data/train-* ---
laion/laion2B-en-safety	Invalid username or password.
nblinh63/twitter_dataset_1712693741	--- 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: 79823 num_examples: 200 download_size: 37919 dataset_size: 79823 configs: - config_name: default data_files: - split: train path: data/train-* ---
medmac01/OpenHermes-2-AR	--- dataset_info: features: - name: topic dtype: 'null' - name: conversations dtype: string - name: skip_prompt_formatting dtype: bool - name: model dtype: 'null' - name: avatarUrl dtype: 'null' - name: custom_instruction dtype: 'null' - name: views dtype: float64 - name: hash dtype: 'null' - name: language dtype: 'null' - name: idx dtype: 'null' - name: model_name dtype: 'null' - name: system_prompt dtype: 'null' - name: title dtype: 'null' - name: source dtype: string - name: id dtype: 'null' - name: category dtype: string splits: - name: train num_bytes: 1907745 num_examples: 1001 download_size: 968864 dataset_size: 1907745 configs: - config_name: default data_files: - split: train path: data/train-* ---
bigscience-data/roots_id_wikimedia	--- language: id license: cc-by-sa-3.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_id_wikimedia # wikimedia_filtered - Dataset uid: `wikimedia_filtered` ### Description ### Homepage ### Licensing ### Speaker Locations ### Sizes - 0.0005 % of total - 0.0835 % of id - 0.0126 % of ca - 0.0054 % of pt - 0.0005 % of indic-hi ### BigScience processing steps #### Filters applied to: id - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_id - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: ca - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_ca - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: pt - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_pt - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: indic-hi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_indic-hi - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300
phanvancongthanh/data_part03	--- dataset_info: features: - name: smiles dtype: string splits: - name: train num_bytes: 4911356191 num_examples: 109915148 download_size: 2471976257 dataset_size: 4911356191 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "data_part03" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
plgfro/Kaggles-Galaxy-Zoo-Dataset	--- license: apache-2.0 ---
tyzhu/squad_qa_num_v5_full_random_permute_4	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 6510358.253911806 num_examples: 4345 - name: validation num_bytes: 343184 num_examples: 300 download_size: 1336925 dataset_size: 6853542.253911806 --- # Dataset Card for "squad_qa_num_v5_full_random_permute_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TingChen-ppmc/Changsha_Dialect_Conversational_Speech_Corpus	--- dataset_info: features: - name: audio dtype: audio - name: gender dtype: string - name: speaker_id dtype: string - name: transcription dtype: string splits: - name: train num_bytes: 223664136.256 num_examples: 1488 download_size: 215320750 dataset_size: 223664136.256 configs: - config_name: default data_files: - split: train path: data/train-* --- # Corpus This dataset is built from Magicdata [ASR-CCHSHDIACSC: A CHINESE CHANGSHA DIALECT CONVERSATIONAL SPEECH CORPUS](https://magichub.com/datasets/changsha-dialect-conversational-speech-corpus/) This corpus is licensed under a [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](http://creativecommons.org/licenses/by-nc-nd/4.0/). Please refer to the license for further information. Modifications: The audio is split in sentences based on the time span on the transcription file. Sentences that span less than 1 second is discarded. Topics of conversation is removed. # Usage To load this dataset, use ```python from datasets import load_dataset dialect_corpus = load_dataset("TingChen-ppmc/Changsha_Dialect_Conversational_Speech_Corpus") ``` This dataset only has train split. To split out a test split, use ```python from datasets import load_dataset train_split = load_dataset("TingChen-ppmc/Changsha_Dialect_Conversational_Speech_Corpus", split="train") # where test=0.5 denotes 0.5 of the dataset will be split to test split corpus = train_split.train_test_split(test=0.5) ``` A sample data would be ```python # note this data is from the Nanchang Dialect corpus, the data format is shared {'audio': {'path': 'A0001_S001_0_G0001_0.WAV', 'array': array([-0.00030518, -0.00039673, -0.00036621, ..., -0.00064087, -0.00015259, -0.00042725]), 'sampling_rate': 16000}, 'gender': '女', 'speaker_id': 'G0001', 'transcription': '北京爱数智慧语音采集' } ``` [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
freshpearYoon/vr_train_free_11	--- 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: 6683391354 num_examples: 10000 download_size: 1198445542 dataset_size: 6683391354 configs: - config_name: default data_files: - split: train path: data/train-* ---
Rewcifer/best_outputs_3models	--- dataset_info: features: - name: true_findings dtype: string - name: generated_texts_1 dtype: string - name: generated_texts_2 dtype: string - name: generated_texts_3 dtype: string splits: - name: train num_bytes: 1861317 num_examples: 861 download_size: 799511 dataset_size: 1861317 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "best_outputs_3models" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kk2491/int_dataset	--- license: apache-2.0 ---
bigcode/the-stack-metadata	--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - other multilinguality: - multilingual pretty_name: The-Stack-Metadata size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: [] extra_gated_prompt: \|- ## Terms of Use for The Stack The Stack Metadata is a collection of additional information for and is part of The Stack dataset, - a collection of source code in over 300 programming languages. We ask that you read and acknowledge the following points before using the dataset: 1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. 2. The Stack is regularly updated to enact validated data removal requests. By clicking on "Access repository", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset’s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes. 3. To host, share, or otherwise provide access to The Stack dataset, you must include [these Terms of Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) and require users to agree to it. By clicking on "Access repository" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well. extra_gated_fields: Email: text I have read the License and agree with its terms: checkbox --- # Dataset Card for The Stack Metadata ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Changelog](#changelog) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Usage Example](#usage-example) - [Dataset Creation](#dataset-creation) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Additional Information](#additional-information) - [Terms of Use for The Stack](#terms-of-use-for-the-stack) ## Dataset Description - Homepage: https://www.bigcode-project.org/ - Repository: https://github.com/bigcode-project - Paper: https://arxiv.org/abs/2211.15533 - Leaderboard: N/A - Point of Contact: contact@bigcode-project.org ### Changelog \|Release\|Description\| \|-\|-\| \|v1.1\| This is the first release of the metadata. It is for The Stack v1.1\| \|v1.2\| Metadata dataset matching The Stack v1.2\| ### Dataset Summary This is a set of additional information for repositories used for The Stack. It contains file paths, detected licenes as well as some other information for the repositories. ### Supported Tasks and Leaderboards The main task is to recreate repository structure from the files of The Stack. Also, the set can be used for computing statistics and custom filtering or aggregation operations on The Stack. ## Dataset Structure ### Data Fields ![set structure](images/structure.png) The set is split into buckets by repositories. There are 944 buckets. Additionally to the fields in the image, `ri` contains `min_repo_event_datetime` which is the ealiest date and time of an event for a repo after Jan 1 2015. ![set usage](images/usage.png) As an example of an aggregation operation on The Stack, the image above shows conceptually a selection of stars ( and issues and PR count) for a file. Each unique file can be part of multiple repositories. So, The Stack releases unique files and aggregates meta information (e.g stars) from all repositories it belongs to. For example, for max_stars_count we take the maximum number of stars from all repositories the file is part of. The meta data will allow you to reconstruct repository directory structures. For this, for each repository form `ri` tabele it is needed to take all its files from `fi` table, find them in The Stack by file's `hexsha` and save those files' content under its path for a repository from `fi` table. For speed it is preferable to index The Stack by hexsha first. ### Usage Example Restore folder structure for python files in numpy repository ```python import datasets from pathlib import Path from tqdm.auto import tqdm import pandas as pd # assuming metadata is cloned into the local folder /data/hf_repos/the-stack-metadata # the stack is cloned into the local folder /data/hf_repos/the-stack-v1.1 # destination folder is in /repo_workdir/numpy_restored the_stack_meta_path = Path('/data/hf_repos/the-stack-metadata') the_stack_path = Path('/data/hf_repos/the-stack-v1.1') repo_dst_root = Path('/repo_workdir/numpy_restored') repo_name = 'numpy/numpy' # Get bucket with numpy repo info # meta_bucket_path = None #for fn in tqdm(list((the_stack_meta_path/'data').glob('*/ri.parquet'))): # df = pd.read_parquet(fn) # if any(df['name'] == repo_name): # meta_bucket_path = fn # break meta_bucket_path = the_stack_meta_path / 'data/255_944' # Get repository id from repo name ri_id = pd.read_parquet( meta_bucket_path / 'ri.parquet' ).query( f'`name` == "{repo_name}"' )['id'].to_list()[0] # Get files information for the reopository files_info = pd.read_parquet( meta_bucket_path / 'fi.parquet' ).query( f'`ri_id` == {ri_id} and `size` != 0 and `is_deleted` == False' ) # Convert DF with files information to a dictionary by language and then file hexsha # there can be more than one file with the same hexsha in the repo so we gather # all instances per unique hexsha files_info_dict = { k: v[['hexsha', 'path']].groupby('hexsha').apply(lambda x: list(x['path'])).to_dict() for k, v in files_info.groupby('lang_ex') } # Load Python part of The Stack ds = datasets.load_dataset( str(the_stack_path/'data/python'), num_proc=10, ignore_verifications=True ) # Save file content of the python files in the numpy reposirotry in their appropriate locations def save_file_content(example, files_info_dict, repo_dst_root): if example['hexsha'] in files_info_dict: for el in files_info_dict[example['hexsha']]: path = repo_dst_root / el path.parent.mkdir(parents=True, exist_ok=True) path.write_text(example['content']) ds.map( save_file_content, fn_kwargs={'files_info_dict': files_info_dict['Python'], 'repo_dst_root': repo_dst_root}, num_proc=10 ) ``` ## Dataset Creation Please refer to [the section](https://huggingface.co/datasets/bigcode/the-stack#dataset-creation) in The Stack. ## Considerations for Using the Data Please refer to [the section](https://huggingface.co/datasets/bigcode/the-stack#considerations-for-using-the-data) in The Stack. ## Additional Information Please refer to [the section](https://huggingface.co/datasets/bigcode/the-stack#additional-information) in The Stack. ## Terms of Use for The Stack Please refer to [the section](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) in The Stack.
davanstrien/WikiMuTe	--- configs: - config_name: default data_files: - split: train path: all.csv - config_name: self-filtering data_files: - split: train path: filtered_sf.csv - config_name: MusicCaps-filtering data_files: - split: train path: filtered_mc.csv license: cc-by-sa-3.0 language: - en ---
open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3	--- pretty_name: Evaluation run of Weyaxi/Luban-Marcoroni-13B-v3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Weyaxi/Luban-Marcoroni-13B-v3](https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3)\ \ 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_Weyaxi__Luban-Marcoroni-13B-v3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-29T14:08:44.787529](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3/blob/main/results_2023-10-29T14-08-44.787529.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\": 0.00776006711409396,\n\ \ \"em_stderr\": 0.0008986296432392762,\n \"f1\": 0.10252936241610805,\n\ \ \"f1_stderr\": 0.0019829740048614144,\n \"acc\": 0.4340313659926291,\n\ \ \"acc_stderr\": 0.010044205768767243\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.00776006711409396,\n \"em_stderr\": 0.0008986296432392762,\n\ \ \"f1\": 0.10252936241610805,\n \"f1_stderr\": 0.0019829740048614144\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.09931766489764973,\n \ \ \"acc_stderr\": 0.008238371412683977\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.01185004012485051\n\ \ }\n}\n```" repo_url: https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3 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_13T22_12_25.570871 path: - '/details_harness\|arc:challenge\|25_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-13T22-12-25.570871.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_29T14_08_44.787529 path: - '/details_harness\|drop\|3_2023-10-29T14-08-44.787529.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-29T14-08-44.787529.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_29T14_08_44.787529 path: - '/details_harness\|gsm8k\|5_2023-10-29T14-08-44.787529.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-29T14-08-44.787529.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hellaswag\|10_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-13T22-12-25.570871.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_13T22_12_25.570871 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-13T22-12-25.570871.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_29T14_08_44.787529 path: - '/details_harness\|winogrande\|5_2023-10-29T14-08-44.787529.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-29T14-08-44.787529.parquet' - config_name: results data_files: - split: 2023_09_13T22_12_25.570871 path: - results_2023-09-13T22-12-25.570871.parquet - split: 2023_10_29T14_08_44.787529 path: - results_2023-10-29T14-08-44.787529.parquet - split: latest path: - results_2023-10-29T14-08-44.787529.parquet --- # Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-13B-v3 ## Dataset Description - Homepage: - Repository: https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3 - 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 [Weyaxi/Luban-Marcoroni-13B-v3](https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3) 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_Weyaxi__Luban-Marcoroni-13B-v3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-29T14:08:44.787529](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3/blob/main/results_2023-10-29T14-08-44.787529.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": 0.00776006711409396, "em_stderr": 0.0008986296432392762, "f1": 0.10252936241610805, "f1_stderr": 0.0019829740048614144, "acc": 0.4340313659926291, "acc_stderr": 0.010044205768767243 }, "harness\|drop\|3": { "em": 0.00776006711409396, "em_stderr": 0.0008986296432392762, "f1": 0.10252936241610805, "f1_stderr": 0.0019829740048614144 }, "harness\|gsm8k\|5": { "acc": 0.09931766489764973, "acc_stderr": 0.008238371412683977 }, "harness\|winogrande\|5": { "acc": 0.7687450670876085, "acc_stderr": 0.01185004012485051 } } ``` ### 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]
lener_br	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - pt license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: lener-br pretty_name: leNER-br dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-ORGANIZACAO '2': I-ORGANIZACAO '3': B-PESSOA '4': I-PESSOA '5': B-TEMPO '6': I-TEMPO '7': B-LOCAL '8': I-LOCAL '9': B-LEGISLACAO '10': I-LEGISLACAO '11': B-JURISPRUDENCIA '12': I-JURISPRUDENCIA config_name: lener_br splits: - name: train num_bytes: 3984189 num_examples: 7828 - name: validation num_bytes: 719433 num_examples: 1177 - name: test num_bytes: 823708 num_examples: 1390 download_size: 2983137 dataset_size: 5527330 tags: - legal --- # Dataset Card for leNER-br ## 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: [leNER-BR homepage](https://cic.unb.br/~teodecampos/LeNER-Br/) - Repository: [leNER-BR repository](https://github.com/peluz/lener-br) - Paper: [leNER-BR: Long Form Question Answering](https://cic.unb.br/~teodecampos/LeNER-Br/luz_etal_propor2018.pdf) - Point of Contact: [Pedro H. Luz de Araujo](mailto:pedrohluzaraujo@gmail.com) ### Dataset Summary LeNER-Br is a Portuguese language dataset for named entity recognition applied to legal documents. LeNER-Br consists entirely of manually annotated legislation and legal cases texts and contains tags for persons, locations, time entities, organizations, legislation and legal cases. To compose the dataset, 66 legal documents from several Brazilian Courts were collected. Courts of superior and state levels were considered, such as Supremo Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas Gerais and Tribunal de Contas da União. In addition, four legislation documents were collected, such as "Lei Maria da Penha", giving a total of 70 documents ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Portuguese. ## Dataset Structure ### Data Instances An example from the dataset looks as follows: ``` { "id": "0", "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0], "tokens": [ "EMENTA", ":", "APELAÇÃO", "CÍVEL", "-", "AÇÃO", "DE", "INDENIZAÇÃO", "POR", "DANOS", "MORAIS", "-", "PRELIMINAR", "-", "ARGUIDA", "PELO", "MINISTÉRIO", "PÚBLICO", "EM", "GRAU", "RECURSAL"] } ``` ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "O", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-PESSOA", "I-PESSOA", "B-TEMPO", "I-TEMPO", "B-LOCAL", "I-LOCAL", "B-LEGISLACAO", "I-LEGISLACAO", "B-JURISPRUDENCIA", "I-JURISPRUDENCIA" ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: \| Train \| Val \| Test \| \| ------ \| ----- \| ---- \| \| 7828 \| 1177 \| 1390 \| ## 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 ``` @inproceedings{luz_etal_propor2018, author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and Renato R. R. {de Oliveira} and Matheus Stauffer and Samuel Couto and Paulo Bermejo}, title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text}, booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})}, publisher = {Springer}, series = {Lecture Notes on Computer Science ({LNCS})}, pages = {313--323}, year = {2018}, month = {September 24-26}, address = {Canela, RS, Brazil}, doi = {10.1007/978-3-319-99722-3_32}, url = {https://cic.unb.br/~teodecampos/LeNER-Br/}, } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.
natmin322/28k_vietnamese_voice_augmented_of_VigBigData	--- configs: - config_name: default data_files: - split: train_1 path: data/train_1-* - split: train_2 path: data/train_2-* - split: train_3 path: data/train_3-* - split: train_4 path: data/train_4-* - split: train_5 path: data/train_5-* - split: test path: data/test-* dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: train_1 num_bytes: 1433691842.0 num_examples: 5000 - name: train_2 num_bytes: 1026073200.0 num_examples: 5000 - name: train_3 num_bytes: 1113535830.0 num_examples: 5000 - name: train_4 num_bytes: 1489647293.0 num_examples: 5000 - name: train_5 num_bytes: 1416405046.0 num_examples: 5000 - name: test num_bytes: 886300388.18 num_examples: 3005 download_size: 6939675259 dataset_size: 7365653599.18 --- # Dataset Card for "28k_vietnamese_voice_augmented_of_VigBigData" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
one-sec-cv12/chunk_112	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 24829848720.625 num_examples: 258515 download_size: 22670676655 dataset_size: 24829848720.625 --- # Dataset Card for "chunk_112" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
result-muse256-muse512-wuerst-sdv15/909d509a	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 221 num_examples: 10 download_size: 1393 dataset_size: 221 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "909d509a" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mHossain/merge_new_para_detection_data_v5	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 8837101.8 num_examples: 50400 - name: test num_bytes: 981900.2 num_examples: 5600 download_size: 4451360 dataset_size: 9819002.0 --- # Dataset Card for "merge_new_para_detection_data_v5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
xrizs/test.v83i.coco-segmentation	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* dataset_info: features: - name: image dtype: image - name: annotation dtype: image splits: - name: train num_bytes: 815324785.5 num_examples: 1814 - name: val num_bytes: 205298969.0 num_examples: 453 download_size: 1020036030 dataset_size: 1020623754.5 --- # Dataset Card for "test.v83i.coco-segmentation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Elicke/Epicdarkguy	--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 793929.0 num_examples: 1 download_size: 776077 dataset_size: 793929.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
kgr123/quality_counter_3000	--- dataset_info: features: - name: context dtype: string - name: word dtype: string - name: claim dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 16636623 num_examples: 1929 - name: train num_bytes: 16474873 num_examples: 1935 - name: validation num_bytes: 16809536 num_examples: 1941 download_size: 11133065 dataset_size: 49921032 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* ---
TinyPixel/based_0	--- dataset_info: features: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 53106 num_examples: 352 download_size: 0 dataset_size: 53106 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "wizard" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_saucam__mistral-orpo-beta-NeuralBeagle14-7B-dare-ties	--- pretty_name: Evaluation run of saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties](https://huggingface.co/saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties)\ \ 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_saucam__mistral-orpo-beta-NeuralBeagle14-7B-dare-ties\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T11:57:13.922311](https://huggingface.co/datasets/open-llm-leaderboard/details_saucam__mistral-orpo-beta-NeuralBeagle14-7B-dare-ties/blob/main/results_2024-03-21T11-57-13.922311.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\": 0.64885588006924,\n\ \ \"acc_stderr\": 0.03206109245911583,\n \"acc_norm\": 0.6502812247567149,\n\ \ \"acc_norm_stderr\": 0.03270979476504535,\n \"mc1\": 0.3659730722154223,\n\ \ \"mc1_stderr\": 0.016862941684088376,\n \"mc2\": 0.5386604944982498,\n\ \ \"mc2_stderr\": 0.014994373950114138\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6262798634812287,\n \"acc_stderr\": 0.014137708601759088,\n\ \ \"acc_norm\": 0.6672354948805461,\n \"acc_norm_stderr\": 0.013769863046192307\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6683927504481179,\n\ \ \"acc_stderr\": 0.004698285350019214,\n \"acc_norm\": 0.859788886675961,\n\ \ \"acc_norm_stderr\": 0.003464963379379926\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621503\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6518518518518519,\n\ \ \"acc_stderr\": 0.04115324610336953,\n \"acc_norm\": 0.6518518518518519,\n\ \ \"acc_norm_stderr\": 0.04115324610336953\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.038234289699266046,\n\ \ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.038234289699266046\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n\ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6936416184971098,\n\ \ \"acc_stderr\": 0.03514942551267438,\n \"acc_norm\": 0.6936416184971098,\n\ \ \"acc_norm_stderr\": 0.03514942551267438\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\ \ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.025424835086924006,\n \"\ acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086924006\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4126984126984127,\n\ \ \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.4126984126984127,\n\ \ \"acc_norm_stderr\": 0.04403438954768176\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7870967741935484,\n\ \ \"acc_stderr\": 0.02328766512726855,\n \"acc_norm\": 0.7870967741935484,\n\ \ \"acc_norm_stderr\": 0.02328766512726855\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\ \ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586818,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586818\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.02338193534812142,\n\ \ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.02338193534812142\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n\ \ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857413,\n \ \ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857413\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\ \ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\ acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8330275229357799,\n \"acc_stderr\": 0.01599015488507338,\n \"\ acc_norm\": 0.8330275229357799,\n \"acc_norm_stderr\": 0.01599015488507338\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"\ acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676166,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676166\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7130044843049327,\n\ \ \"acc_stderr\": 0.030360379710291954,\n \"acc_norm\": 0.7130044843049327,\n\ \ \"acc_norm_stderr\": 0.030360379710291954\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\ \ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\ acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.49107142857142855,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8314176245210728,\n\ \ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\ \ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.02410571260775431,\n\ \ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.02410571260775431\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.34301675977653634,\n\ \ \"acc_stderr\": 0.015876912673057745,\n \"acc_norm\": 0.34301675977653634,\n\ \ \"acc_norm_stderr\": 0.015876912673057745\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\ \ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7202572347266881,\n\ \ \"acc_stderr\": 0.025494259350694912,\n \"acc_norm\": 0.7202572347266881,\n\ \ \"acc_norm_stderr\": 0.025494259350694912\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7808641975308642,\n \"acc_stderr\": 0.023016705640262196,\n\ \ \"acc_norm\": 0.7808641975308642,\n \"acc_norm_stderr\": 0.023016705640262196\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4576271186440678,\n\ \ \"acc_stderr\": 0.01272429655098019,\n \"acc_norm\": 0.4576271186440678,\n\ \ \"acc_norm_stderr\": 0.01272429655098019\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.028582709753898452,\n\ \ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.028582709753898452\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.673202614379085,\n \"acc_stderr\": 0.01897542792050721,\n \ \ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.01897542792050721\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142773,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142773\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8606965174129353,\n\ \ \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n\ \ \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3659730722154223,\n\ \ \"mc1_stderr\": 0.016862941684088376,\n \"mc2\": 0.5386604944982498,\n\ \ \"mc2_stderr\": 0.014994373950114138\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8121546961325967,\n \"acc_stderr\": 0.010977481103435091\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6338134950720242,\n \ \ \"acc_stderr\": 0.013270100238748835\n }\n}\n```" repo_url: https://huggingface.co/saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties 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_21T11_57_13.922311 path: - '/details_harness\|arc:challenge\|25_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-21T11-57-13.922311.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|gsm8k\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hellaswag\|10_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T11-57-13.922311.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T11-57-13.922311.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '/details_harness\|winogrande\|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-21T11-57-13.922311.parquet' - config_name: results data_files: - split: 2024_03_21T11_57_13.922311 path: - results_2024-03-21T11-57-13.922311.parquet - split: latest path: - results_2024-03-21T11-57-13.922311.parquet --- # Dataset Card for Evaluation run of saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties](https://huggingface.co/saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties) 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_saucam__mistral-orpo-beta-NeuralBeagle14-7B-dare-ties", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T11:57:13.922311](https://huggingface.co/datasets/open-llm-leaderboard/details_saucam__mistral-orpo-beta-NeuralBeagle14-7B-dare-ties/blob/main/results_2024-03-21T11-57-13.922311.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": 0.64885588006924, "acc_stderr": 0.03206109245911583, "acc_norm": 0.6502812247567149, "acc_norm_stderr": 0.03270979476504535, "mc1": 0.3659730722154223, "mc1_stderr": 0.016862941684088376, "mc2": 0.5386604944982498, "mc2_stderr": 0.014994373950114138 }, "harness\|arc:challenge\|25": { "acc": 0.6262798634812287, "acc_stderr": 0.014137708601759088, "acc_norm": 0.6672354948805461, "acc_norm_stderr": 0.013769863046192307 }, "harness\|hellaswag\|10": { "acc": 0.6683927504481179, "acc_stderr": 0.004698285350019214, "acc_norm": 0.859788886675961, "acc_norm_stderr": 0.003464963379379926 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621503, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621503 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6518518518518519, "acc_stderr": 0.04115324610336953, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.04115324610336953 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6710526315789473, "acc_stderr": 0.038234289699266046, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.038234289699266046 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6936416184971098, "acc_stderr": 0.03514942551267438, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.03514942551267438 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086924006, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086924006 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4126984126984127, "acc_stderr": 0.04403438954768176, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.04403438954768176 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7870967741935484, "acc_stderr": 0.02328766512726855, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.02328766512726855 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252607, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252607 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586818, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586818 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8808290155440415, "acc_stderr": 0.02338193534812142, "acc_norm": 0.8808290155440415, "acc_norm_stderr": 0.02338193534812142 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657266, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657266 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.35555555555555557, "acc_stderr": 0.029185714949857413, "acc_norm": 0.35555555555555557, "acc_norm_stderr": 0.029185714949857413 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6848739495798319, "acc_stderr": 0.030176808288974337, "acc_norm": 0.6848739495798319, "acc_norm_stderr": 0.030176808288974337 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3576158940397351, "acc_stderr": 0.03913453431177258, "acc_norm": 0.3576158940397351, "acc_norm_stderr": 0.03913453431177258 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8330275229357799, "acc_stderr": 0.01599015488507338, "acc_norm": 0.8330275229357799, "acc_norm_stderr": 0.01599015488507338 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5185185185185185, "acc_stderr": 0.03407632093854051, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.03407632093854051 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8137254901960784, "acc_stderr": 0.027325470966716312, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.027325470966716312 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676166, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676166 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7130044843049327, "acc_stderr": 0.030360379710291954, "acc_norm": 0.7130044843049327, "acc_norm_stderr": 0.030360379710291954 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.816793893129771, "acc_stderr": 0.03392770926494733, "acc_norm": 0.816793893129771, "acc_norm_stderr": 0.03392770926494733 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.04236511258094633, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.04236511258094633 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7225433526011561, "acc_stderr": 0.02410571260775431, "acc_norm": 0.7225433526011561, "acc_norm_stderr": 0.02410571260775431 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.34301675977653634, "acc_stderr": 0.015876912673057745, "acc_norm": 0.34301675977653634, "acc_norm_stderr": 0.015876912673057745 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292456, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292456 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7202572347266881, "acc_stderr": 0.025494259350694912, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.025494259350694912 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7808641975308642, "acc_stderr": 0.023016705640262196, "acc_norm": 0.7808641975308642, "acc_norm_stderr": 0.023016705640262196 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4576271186440678, "acc_stderr": 0.01272429655098019, "acc_norm": 0.4576271186440678, "acc_norm_stderr": 0.01272429655098019 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6691176470588235, "acc_stderr": 0.028582709753898452, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.028582709753898452 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.673202614379085, "acc_stderr": 0.01897542792050721, "acc_norm": 0.673202614379085, "acc_norm_stderr": 0.01897542792050721 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7387755102040816, "acc_stderr": 0.028123429335142773, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142773 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8606965174129353, "acc_stderr": 0.024484487162913973, "acc_norm": 0.8606965174129353, "acc_norm_stderr": 0.024484487162913973 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640038, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640038 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3659730722154223, "mc1_stderr": 0.016862941684088376, "mc2": 0.5386604944982498, "mc2_stderr": 0.014994373950114138 }, "harness\|winogrande\|5": { "acc": 0.8121546961325967, "acc_stderr": 0.010977481103435091 }, "harness\|gsm8k\|5": { "acc": 0.6338134950720242, "acc_stderr": 0.013270100238748835 } } ``` ## 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]
tyzhu/find_last_sent_train_500_eval_20	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 1144818 num_examples: 904 - name: validation num_bytes: 20896 num_examples: 20 download_size: 499234 dataset_size: 1165714 --- # Dataset Card for "find_last_sent_train_500_eval_20" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
veezbo/akkadian_english_corpus	--- license: mit task_categories: - text-generation language: - en pretty_name: English-translated Akkadian Corpus size_categories: - 1K<n<10K --- # Akkadian English Corpus This dataset is a cleaned English-translated Akkadian language dataset. This dataset can and has been used for text generation tasks, for example to fine-tune LLMs. ## How it was generated Please visit my [repo](https://github.com/veezbo/akkadian_english_corpus) on Github which explains the steps that were taken to prepare this dataset for a text generation task. At a high level, these are steps that were taken: - Sourced a high-quality dataset of English-translated Akkadian by experts - Enforced a minimum line length - Removed duplicate lines - Removed textual notes and other generic notes within parantheses - Inserted translation notes and literal notes in place (preserving grammar and adding clarity to the corpus) ## Credit Credit for the aggregation of the raw data belongs to the [Akkademia](https://github.com/gaigutherz/Akkademia/tree/master) project. Specifically, the exact data file used as the starting dataset is linked [here](https://github.com/gaigutherz/Akkademia/blob/master/NMT_input/train.en) and was also used to train their SOTA neural machine translation Akkadian->English model as described in their recent [paper](https://academic.oup.com/pnasnexus/article/2/5/pgad096/7147349) Gutherz et al. 2023 [1]. Credit for the original source of the raw data belongs to the incredible Open Richly Annotated Cuneiform Corpus ([ORACC](http://oracc.org)) project [2]. Specifically, as noted by the Akkademia project above, the RINAP 1, 3, 4, and 5 datasets are the source of the original raw data. ## Citations [1] Gai Gutherz, Shai Gordin, Luis Sáenz, Omer Levy, Jonathan Berant, Translating Akkadian to English with neural machine translation, PNAS Nexus, Volume 2, Issue 5, May 2023, pgad096, https://doi.org/10.1093/pnasnexus/pgad096 [2] Jamie Novotny, Eleanor Robson, Steve Tinney, Niek Veldhuis, et al. Open Richly Annotated Cuneiform Corpus, http://oracc.org
Penguin-N/github-issues	--- dataset_info: features: - name: id dtype: int64 - name: labels_url dtype: string - name: body dtype: string - name: updated_at dtype: string - name: number dtype: int64 - name: milestone struct: - name: closed_at dtype: 'null' - name: closed_issues dtype: int64 - name: created_at dtype: string - name: creator struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: description dtype: string - name: due_on dtype: 'null' - name: html_url dtype: string - name: id dtype: int64 - name: labels_url dtype: string - name: node_id dtype: string - name: number dtype: int64 - name: open_issues dtype: int64 - name: state dtype: string - name: title dtype: string - name: updated_at dtype: string - name: url dtype: string - name: repository_url dtype: string - name: draft dtype: bool - name: labels list: - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: id dtype: int64 - name: name dtype: string - name: node_id dtype: string - name: url dtype: string - name: created_at dtype: string - name: comments_url dtype: string - name: assignee struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: timeline_url dtype: string - name: title dtype: string - name: events_url dtype: string - name: active_lock_reason dtype: 'null' - name: user struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: assignees list: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: performed_via_github_app dtype: 'null' - name: state_reason dtype: string - name: author_association dtype: string - name: closed_at dtype: string - name: pull_request struct: - name: diff_url dtype: string - name: html_url dtype: string - name: merged_at dtype: string - name: patch_url dtype: string - name: url dtype: string - name: node_id dtype: string - name: comments sequence: string - name: reactions struct: - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: confused dtype: int64 - name: eyes dtype: int64 - name: heart dtype: int64 - name: hooray dtype: int64 - name: laugh dtype: int64 - name: rocket dtype: int64 - name: total_count dtype: int64 - name: url dtype: string - name: state dtype: string - name: locked dtype: bool - name: url dtype: string - name: html_url dtype: string - name: is_pull_request dtype: bool splits: - name: train num_bytes: 23420904 num_examples: 3000 download_size: 6966686 dataset_size: 23420904 configs: - config_name: default data_files: - split: train path: data/train-* ---
Nexdata/47811_Sentences_Intention_Annotation_Data_in_Interactive_Scenes	--- license: cc-by-nc-nd-4.0 --- ## Description Intent-like single-sentence annotated textual data, the data size is 47811 sentences, annotated with intent classes, including slot and slot value information; the intent field includes music, weather, date, schedule, home equipment, etc.; it is applied to intent recognition research and related fields. For more details, please refer to the link: https://www.nexdata.ai/dataset/1085?source=Huggingface # Specifications ## Storage format Json ## Data content 47,811 sentences of 16 domains, and each sentence is marked with intention ## Annotation Manually write sentences for each intent type, and then mark the slot; ## Language Chinese ## Accuracy Rate 99% ## Application scenario intention understanding in speech interaction # Licensing Information Commercial License
juancopi81/test-audio	--- dataset_info: features: - name: audio dtype: audio - name: playlist_title dtype: string - name: url dtype: string - name: title dtype: string - name: description dtype: string splits: - name: train num_bytes: 1052419.0 num_examples: 3 download_size: 0 dataset_size: 1052419.0 --- # Dataset Card for "test-audio" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
liuyanchen1015/MULTI_VALUE_sst2_drop_aux_wh	--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 393 num_examples: 4 - name: test num_bytes: 600 num_examples: 6 - name: train num_bytes: 7762 num_examples: 62 download_size: 10707 dataset_size: 8755 --- # Dataset Card for "MULTI_VALUE_sst2_drop_aux_wh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/sakakibara_satomi_idolmastercinderellagirls	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sakakibara_satomi/榊原里美 (THE iDOLM@STER: Cinderella Girls) This is the dataset of sakakibara_satomi/榊原里美 (THE iDOLM@STER: Cinderella Girls), containing 71 images and their tags. The core tags of this character are `grey_hair, breasts, long_hair, large_breasts, purple_eyes, drill_hair, braid, twintails`, 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 \| 71 \| 46.40 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sakakibara_satomi_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 71 \| 38.68 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sakakibara_satomi_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 134 \| 67.24 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sakakibara_satomi_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 71 \| 44.32 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sakakibara_satomi_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 134 \| 76.05 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sakakibara_satomi_idolmastercinderellagirls/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/sakakibara_satomi_idolmastercinderellagirls', 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 \| 14 \| ![](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, cleavage, open_mouth, necklace, hairband, looking_at_viewer, :d, blush, microphone \| \| 1 \| 10 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, solo, dress, necklace, blush, simple_background, twin_braids, looking_at_viewer, open_mouth, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| cleavage \| open_mouth \| necklace \| hairband \| looking_at_viewer \| :d \| blush \| microphone \| dress \| simple_background \| twin_braids \| white_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:-----------\|:-------------\|:-----------\|:-----------\|:--------------------\|:-----\|:--------\|:-------------\|:--------\|:--------------------\|:--------------\|:-------------------\| \| 0 \| 14 \| ![](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 \| \| \| \| \| \| 1 \| 10 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| \| X \| X \| \| X \| \| X \| \| X \| X \| X \| X \|
manu/french_librispeech_text_only	--- dataset_info: features: - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 62120933 num_examples: 258213 download_size: 37959942 dataset_size: 62120933 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "french_librispeech_text_only" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tyzhu/squad_qa_context_v5_full_recite_ans_sent_random_permute_rerun_4	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 4850217.0 num_examples: 2385 - name: validation num_bytes: 631113 num_examples: 300 download_size: 1204825 dataset_size: 5481330.0 --- # Dataset Card for "squad_qa_context_v5_full_recite_ans_sent_random_permute_rerun_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DopeorNope/new_instruct6	--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: instruction dtype: string - name: tag dtype: string splits: - name: train num_bytes: 525389527 num_examples: 127811 download_size: 259744648 dataset_size: 525389527 configs: - config_name: default data_files: - split: train path: data/train-* ---
Elfsong/Mercury	--- dataset_info: features: - name: slug_name dtype: string - name: meta_info struct: - name: data struct: - name: question struct: - name: categoryTitle dtype: string - name: content dtype: string - name: difficulty dtype: string - name: questionFrontendId dtype: string - name: questionId dtype: string - name: questionTitle dtype: string - name: questionTitleSlug dtype: string - name: similarQuestions dtype: string - name: stats dtype: string - name: topicTags list: - name: name dtype: string - name: slug dtype: string - name: id dtype: string - name: difficulty dtype: string - name: pretty_content sequence: string - name: solutions list: - name: hash dtype: int64 - name: runtime dtype: string - name: solution dtype: string - name: prompt dtype: string - name: generator_code dtype: string - name: convert_online dtype: string - name: convert_offline dtype: string - name: evaluate_offline dtype: string - name: entry_point dtype: string - name: test_cases dtype: string splits: - name: train num_bytes: 24879611 num_examples: 1633 - name: eval num_bytes: 7028101 num_examples: 256 download_size: 10526574 dataset_size: 31907712 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* language: - en size_categories: - 1K<n<10K --- # Welcome to Mercury 🪐! ## It is a code efficiency benchmark. ## Please consider citing our paper: https://arxiv.org/abs/2402.07844
dchatca/vn-economic-articles-summary-remove-1	--- dataset_info: features: - name: Title dtype: string - name: Content dtype: string - name: Sum-Content dtype: string - name: text dtype: string splits: - name: train num_bytes: 12177542.253918495 num_examples: 1148 - name: test num_bytes: 1357774.7460815047 num_examples: 128 download_size: 6349365 dataset_size: 13535317.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
kamilakesbi/cv_for_spd_fr_augmented	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 48000 - name: speakers sequence: string - name: timestamps_start sequence: float64 - name: timestamps_end sequence: float64 splits: - name: train num_bytes: 174115378.0 num_examples: 120 - name: validation num_bytes: 39951832.0 num_examples: 24 - name: test num_bytes: 40056748.0 num_examples: 24 download_size: 236615435 dataset_size: 254123958.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
ademax/ocr_scan_vi	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 628707658.0172987 num_examples: 10000 - name: test num_bytes: 32755668.982701264 num_examples: 521 download_size: 660712028 dataset_size: 661463327.0 --- # Dataset Card for "ocr_scan_vi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/rudewell_emilia_maougakuinnofutekigousha	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Rudewell Emilia/エミリア・ルードウェル (Maou Gakuin no Futekigousha) This is the dataset of Rudewell Emilia/エミリア・ルードウェル (Maou Gakuin no Futekigousha), containing 36 images and their tags. The core tags of this character are `long_hair, purple_hair, purple_eyes, hair_between_eyes, ponytail, pink_eyes, asymmetrical_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 \| 36 \| 25.88 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rudewell_emilia_maougakuinnofutekigousha/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 36 \| 25.87 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rudewell_emilia_maougakuinnofutekigousha/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 81 \| 48.36 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rudewell_emilia_maougakuinnofutekigousha/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/rudewell_emilia_maougakuinnofutekigousha', 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 \| 5 \| ![](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, closed_mouth, solo, frown, looking_at_viewer, necklace, asymmetrical_bangs, portrait, upper_body \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| closed_mouth \| solo \| frown \| looking_at_viewer \| necklace \| asymmetrical_bangs \| portrait \| upper_body \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:-------\|:--------\|:--------------------\|:-----------\|:---------------------\|:-----------\|:-------------\| \| 0 \| 5 \| ![](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 \|
LexiconShiftInnovations/sinhala_facebook_stories	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 15882438 num_examples: 776 download_size: 6524881 dataset_size: 15882438 configs: - config_name: default data_files: - split: train path: data/train-* ---
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/56d4a1b5	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 184 num_examples: 10 download_size: 1335 dataset_size: 184 --- # Dataset Card for "56d4a1b5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zeroman1318/daegu-ai-06	--- configs: - config_name: default data_files: - split: train path: data.csv --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> ## 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]
tyzhu/squad_qa_context_v5_full_recite_ans_sent_random_permute_rerun_8	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 4850217.0 num_examples: 2385 - name: validation num_bytes: 631113 num_examples: 300 download_size: 1204825 dataset_size: 5481330.0 --- # Dataset Card for "squad_qa_context_v5_full_recite_ans_sent_random_permute_rerun_8" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
chloecchng/biomedical_cpgQA	--- license: apache-2.0 task_categories: - question-answering language: - en tags: - biology - medical size_categories: - 1K<n<10K --- # Dataset Card for the Biomedical Domain ### Dataset Summary This dataset was obtain through github (https://github.com/mmahbub/cpgQA/blob/main/dataset/cpgQA-v1.0.csv?plain=1) to Huggin Face for easier access while fine tuning. ### Languages English (en) ## Dataset Structure The dataset is in a CSV format, with each row representing a single review. The following columns are included: * Title: Categorises the QA. * Context: Gives a context of the QA. * Question: The question asked. * Answer: The expected and appropriate answer to the question asked.
kiennguyen1703/DiroxCompany	--- license: apache-2.0 language: - en ---
magixxixx/shopee-product-reviews-on-computer-category	--- task_categories: - text-classification language: - tl - en tags: - shopee - reviews pretty_name: Shopee reviews on computer category, 20k positives and 20k negatives size_categories: - 10K<n<100K ---
CAiRE/prosocial-dialog-eng_Latn-bloomz-rlhf	--- dataset_info: features: - name: context dtype: string - name: response dtype: string - name: rots sequence: string - name: safety_label dtype: string - name: safety_annotations sequence: string - name: safety_annotation_reasons sequence: string - name: source dtype: string - name: etc dtype: string - name: dialogue_id dtype: int64 - name: response_id dtype: int64 - name: episode_done dtype: bool - name: agent_response dtype: string splits: - name: train num_bytes: 144916538 num_examples: 120236 - name: validation num_bytes: 29794902 num_examples: 20416 - name: test num_bytes: 36311173 num_examples: 25029 download_size: 107425838 dataset_size: 211022613 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
kaleemWaheed/twitter_dataset_1712994766	--- 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: 23267 num_examples: 51 download_size: 11096 dataset_size: 23267 configs: - config_name: default data_files: - split: train path: data/train-* ---
TinyPixel/lima-u	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1804087 num_examples: 780 download_size: 1044055 dataset_size: 1804087 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "lima-u" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
haoranxu/WMT22-Test	--- dataset_info: - config_name: cs-en features: - name: cs-en struct: - name: cs dtype: string - name: en dtype: string splits: - name: test num_bytes: 325040 num_examples: 1448 download_size: 224193 dataset_size: 325040 - config_name: de-en features: - name: de-en struct: - name: de dtype: string - name: en dtype: string splits: - name: test num_bytes: 403424 num_examples: 1984 download_size: 267107 dataset_size: 403424 - config_name: en-cs features: - name: en-cs struct: - name: cs dtype: string - name: en dtype: string splits: - name: test num_bytes: 422875 num_examples: 2037 download_size: 281086 dataset_size: 422875 - config_name: en-de features: - name: en-de struct: - name: de dtype: string - name: en dtype: string splits: - name: test num_bytes: 442576 num_examples: 2037 download_size: 280415 dataset_size: 442576 - config_name: en-is features: - name: en-is struct: - name: en dtype: string - name: is dtype: string splits: - name: test num_bytes: 310807 num_examples: 1000 download_size: 197437 dataset_size: 310807 - config_name: en-ru features: - name: en-ru struct: - name: en dtype: string - name: ru dtype: string splits: - name: test num_bytes: 598414 num_examples: 2037 download_size: 333784 dataset_size: 598414 - config_name: en-zh features: - name: en-zh struct: - name: en dtype: string - name: zh dtype: string splits: - name: test num_bytes: 383751 num_examples: 2037 download_size: 257805 dataset_size: 383751 - config_name: is-en features: - name: is-en struct: - name: en dtype: string - name: is dtype: string splits: - name: test num_bytes: 248029 num_examples: 1000 download_size: 152885 dataset_size: 248029 - config_name: ru-en features: - name: ru-en struct: - name: en dtype: string - name: ru dtype: string splits: - name: test num_bytes: 579656 num_examples: 2016 download_size: 340830 dataset_size: 579656 - config_name: zh-en features: - name: zh-en struct: - name: en dtype: string - name: zh dtype: string splits: - name: test num_bytes: 526074 num_examples: 1875 download_size: 333078 dataset_size: 526074 configs: - config_name: cs-en data_files: - split: test path: cs-en/test-* - config_name: de-en data_files: - split: test path: de-en/test-* - config_name: en-cs data_files: - split: test path: en-cs/test-* - config_name: en-de data_files: - split: test path: en-de/test-* - config_name: en-is data_files: - split: test path: en-is/test-* - config_name: en-ru data_files: - split: test path: en-ru/test-* - config_name: en-zh data_files: - split: test path: en-zh/test-* - config_name: is-en data_files: - split: test path: is-en/test-* - config_name: ru-en data_files: - split: test path: ru-en/test-* - config_name: zh-en data_files: - split: test path: zh-en/test-* --- # Dataset Card for "WMT22-Test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mb7419/legal-advice-reddit	--- dataset_info: features: - name: ques_title dtype: string - name: ques_text dtype: string - name: ques_created dtype: string - name: ques_score dtype: int64 - name: ans_text dtype: string - name: ans_created dtype: string - name: ans_score dtype: float64 - name: dominant_topic_name dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 228451850 num_examples: 115359 - name: validation num_bytes: 49046245 num_examples: 24720 - name: test num_bytes: 49058903 num_examples: 24720 download_size: 197856704 dataset_size: 326556998 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
shrinath-suresh/so_5k_with_short_answer_cleaned	--- license: apache-2.0 ---
Rami/Diabetic_Retinopathy_Preprocessed_Dataset_256x256	--- language: - en size_categories: - 1K<n<10K task_categories: - image-classification tags: - medical configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 354568127.0 num_examples: 2750 download_size: 0 dataset_size: 354568127.0 --- This is dataset comes from this [Kaggle Dataset](https://www.kaggle.com/datasets/sachinkumar413/diabetic-retinopathy-dataset/) from the user [Sachin Kumar](https://www.kaggle.com/sachinkumar413). - The goal of the dataset is for the Varun AIM Projects to easily start running and download the dataset on their local computer in the HF libraries as the directory I strongly recommedn to use.
DBQ/Mr.Porter.Product.prices.Hungary	--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual source_datasets: - original task_categories: - text-classification - image-classification - feature-extraction - image-segmentation - image-to-image - image-to-text - object-detection - summarization - zero-shot-image-classification pretty_name: Hungary - Mr Porter - Product-level price list tags: - webscraping - ecommerce - Mr Porter - fashion - fashion product - image - fashion image configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: website_name dtype: string - name: competence_date dtype: string - name: country_code dtype: string - name: currency_code dtype: string - name: brand dtype: string - name: category1_code dtype: string - name: category2_code dtype: string - name: category3_code dtype: string - name: product_code dtype: int64 - name: title dtype: string - name: itemurl dtype: string - name: imageurl dtype: string - name: full_price dtype: float64 - name: price dtype: float64 - name: full_price_eur dtype: float64 - name: price_eur dtype: float64 - name: flg_discount dtype: int64 splits: - name: train num_bytes: 9097423 num_examples: 27686 download_size: 2070861 dataset_size: 9097423 --- # Mr Porter web scraped data ## About the website Mr Porter operates within the online luxury fashion retail industry in the EMEA region, particularly focusing on the arena of mens fashion in Hungary. This industry has been seeing notable growth owing to increasing internet penetration and the rising consumer interest in high-end fashion. Ecommerce has now become a significant component of the luxury fashion business model. The dataset studied particularly consists of product-list page (PLP) data for Mr Porter in Hungary, which is a valuable source of insight into consumer preferences for luxury mens fashion and the overall performance of the industry in this market. ## Link to dataset [Hungary - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Hungary/r/recevRWtytAuVgwUz)

cr7Por/my_controlnet

--- dataset_info: features: - name: image dtype: image - name: image_crop dtype: image - name: image_caption dtype: string splits: - name: train num_bytes: 135354742.0 num_examples: 435 download_size: 135278720 dataset_size: 135354742.0 --- # Dataset Card for "my_controlnet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ZeeshanChaudharee/example

--- license: apache-2.0 ---

ilhamxx/xdata_invoices

--- license: unknown ---

winddude/reddit_finance_43_250k

--- license: gpl-3.0 language: - en tags: - finance - investing - crypto - reddit --- # reddit finance 43 250k `reddit_finance_43_250k` is a collection of 250k post/comment pairs from 43 financial, investing and crypto subreddits. Post must have all been text, with a length of 250chars, and a positive score. Each subreddit is narrowed down to the 70th qunatile before being mergered with their top 3 comments and than the other subs. Further score based methods are used to select the top 250k post/comment pairs. The code to recreate the dataset is here: <https://github.com/getorca/ProfitsBot_V0_OLLM/tree/main/ds_builder> The trained lora model is here: <https://huggingface.co/winddude/pb_lora_7b_v0.1>

version-control/data-2

--- dataset_info: features: - name: version dtype: string - name: code dtype: string - name: apis sequence: string - name: full_version dtype: string - name: repo_name dtype: string - name: hexsha dtype: string splits: - name: torch num_bytes: 8549368 num_examples: 697 - name: tensorflow num_bytes: 4122692 num_examples: 276 - name: scipy num_bytes: 4668643 num_examples: 193 - name: pandas num_bytes: 6791523 num_examples: 483 - name: sklearn num_bytes: 2050255 num_examples: 170 - name: numpy num_bytes: 29978447 num_examples: 1757 - name: matplotlib num_bytes: 3453619 num_examples: 251 download_size: 21315897 dataset_size: 59614547 configs: - config_name: default data_files: - split: torch path: data/torch-* - split: tensorflow path: data/tensorflow-* - split: scipy path: data/scipy-* - split: pandas path: data/pandas-* - split: sklearn path: data/sklearn-* - split: numpy path: data/numpy-* - split: matplotlib path: data/matplotlib-* ---

carnival13/massive_eval_DA_tokenized

--- dataset_info: features: - name: pass_label dtype: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 23064510 num_examples: 24160 download_size: 5097845 dataset_size: 23064510 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "massive_eval_DA_tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Giacinta/weibo

--- license: apache-2.0 task_categories: - text-classification language: - zh tags: - medical pretty_name: weibo size_categories: - n<1K configs: - config_name: default data_files: - split: train path: PYH微博抽样数据.csv ---

KShivendu/wikipedia-1k-cohere-openai-embeddings

--- language: en license: mit dataset_info: features: - name: id dtype: int32 - name: title dtype: string - name: text dtype: string - name: url dtype: string - name: wiki_id dtype: int32 - name: views dtype: float32 - name: paragraph_id dtype: int32 - name: langs dtype: int32 - name: cohere sequence: float32 - name: openai sequence: float64 splits: - name: train num_bytes: 15850870 num_examples: 1000 download_size: 13208079 dataset_size: 15850870 tags: - openai - cohere - wikipedia --- Smaller version of https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings that includes Cohere as well as OpenAI embeddings (`text-embedding-ada-002`) 100k version of this dataset will be released soon.

psm151/wav2vec2-large-xlsr-turkish-demo-colab

--- license: openrail ---

CyberHarem/yat_sen_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yat_sen/逸仙/逸仙 (Azur Lane) This is the dataset of yat_sen/逸仙/逸仙 (Azur Lane), containing 86 images and their tags. The core tags of this character are `long_hair, bangs, hair_ornament, black_hair, breasts, red_eyes, hair_flower, large_breasts, blunt_bangs, 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 | 86 | 150.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yat_sen_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 86 | 77.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yat_sen_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 200 | 151.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yat_sen_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 86 | 128.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yat_sen_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 200 | 224.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yat_sen_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/yat_sen_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 | 7 | ![](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, china_dress, cleavage_cutout, detached_sleeves, flower, looking_at_viewer, solo, white_dress, bare_shoulders, black_thighhighs, smile, blush, bridal_gauntlets, covered_navel, gloves, sitting, white_background | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, bare_shoulders, china_dress, detached_sleeves, flower, looking_at_viewer, smile, solo, white_dress, closed_mouth, sleeveless_dress, black_gloves, black_thighhighs, cleavage_cutout, official_alternate_costume, oil-paper_umbrella, pelvic_curtain, thighs, brown_gloves, full_body, holding_umbrella, sitting | | 2 | 8 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, flower, looking_at_viewer, solo, white_dress, cleavage, sitting, white_thighhighs, blush, closed_mouth, long_sleeves, chinese_clothes, official_alternate_costume, smile, collarbone, holding_umbrella, feet, full_body, no_shoes, see-through_dress | | 3 | 11 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, smile, solo, flower, looking_at_viewer, red_dress, closed_mouth, long_sleeves, brown_hair, wide_sleeves, jewelry, sitting, china_dress, hair_bun, holding_fan | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | china_dress | cleavage_cutout | detached_sleeves | flower | looking_at_viewer | solo | white_dress | bare_shoulders | black_thighhighs | smile | blush | bridal_gauntlets | covered_navel | gloves | sitting | white_background | closed_mouth | sleeveless_dress | black_gloves | official_alternate_costume | oil-paper_umbrella | pelvic_curtain | thighs | brown_gloves | full_body | holding_umbrella | cleavage | white_thighhighs | long_sleeves | chinese_clothes | collarbone | feet | no_shoes | see-through_dress | red_dress | brown_hair | wide_sleeves | jewelry | hair_bun | holding_fan | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:------------------|:-------------------|:---------|:--------------------|:-------|:--------------|:-----------------|:-------------------|:--------|:--------|:-------------------|:----------------|:---------|:----------|:-------------------|:---------------|:-------------------|:---------------|:-----------------------------|:---------------------|:-----------------|:---------|:---------------|:------------|:-------------------|:-----------|:-------------------|:---------------|:------------------|:-------------|:-------|:-----------|:--------------------|:------------|:-------------|:---------------|:----------|:-----------|:--------------| | 0 | 7 | ![](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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | | | | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | 2 | 8 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | | X | X | X | X | | | X | X | | | | X | | X | | | X | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | 3 | 11 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | | X | X | X | | | | X | | | | | X | | X | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X |

open-llm-leaderboard/details_OpenModels4all__gemma-1.1-7b-it

--- pretty_name: Evaluation run of OpenModels4all/gemma-1.1-7b-it dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [OpenModels4all/gemma-1.1-7b-it](https://huggingface.co/OpenModels4all/gemma-1.1-7b-it)\ \ 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_OpenModels4all__gemma-1.1-7b-it\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-09T11:06:14.950936](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenModels4all__gemma-1.1-7b-it/blob/main/results_2024-04-09T11-06-14.950936.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\": 0.6014747509936101,\n\ \ \"acc_stderr\": 0.03326656009179332,\n \"acc_norm\": 0.6064901781756107,\n\ \ \"acc_norm_stderr\": 0.03393142550082237,\n \"mc1\": 0.34149326805385555,\n\ \ \"mc1_stderr\": 0.016600688619950826,\n \"mc2\": 0.5039555794689907,\n\ \ \"mc2_stderr\": 0.01643070151671278\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5656996587030717,\n \"acc_stderr\": 0.01448470304885736,\n\ \ \"acc_norm\": 0.5998293515358362,\n \"acc_norm_stderr\": 0.014317197787809174\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5862378012348137,\n\ \ \"acc_stderr\": 0.004915003499517829,\n \"acc_norm\": 0.7620991834295957,\n\ \ \"acc_norm_stderr\": 0.004249278842903416\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5111111111111111,\n\ \ \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.5111111111111111,\n\ \ \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \ \ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\ \ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6226415094339622,\n \"acc_stderr\": 0.029832808114796,\n\ \ \"acc_norm\": 0.6226415094339622,\n \"acc_norm_stderr\": 0.029832808114796\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6597222222222222,\n\ \ \"acc_stderr\": 0.039621355734862175,\n \"acc_norm\": 0.6597222222222222,\n\ \ \"acc_norm_stderr\": 0.039621355734862175\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\ : 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.5953757225433526,\n\ \ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n\ \ \"acc_norm_stderr\": 0.03742461193887248\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\":\ \ 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n\ \ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.032469569197899575,\n\ \ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.032469569197899575\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n\ \ \"acc_stderr\": 0.04615186962583702,\n \"acc_norm\": 0.40350877192982454,\n\ \ \"acc_norm_stderr\": 0.04615186962583702\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.46296296296296297,\n \"acc_stderr\": 0.02568056464005688,\n \"\ acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.02568056464005688\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7451612903225806,\n\ \ \"acc_stderr\": 0.024790118459332208,\n \"acc_norm\": 0.7451612903225806,\n\ \ \"acc_norm_stderr\": 0.024790118459332208\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175007,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175007\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\ : {\n \"acc\": 0.696969696969697,\n \"acc_stderr\": 0.03588624800091707,\n\ \ \"acc_norm\": 0.696969696969697,\n \"acc_norm_stderr\": 0.03588624800091707\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7575757575757576,\n \"acc_stderr\": 0.030532892233932046,\n \"\ acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.030532892233932046\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.02649905770139744,\n\ \ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.02649905770139744\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5974358974358974,\n \"acc_stderr\": 0.02486499515976775,\n \ \ \"acc_norm\": 0.5974358974358974,\n \"acc_norm_stderr\": 0.02486499515976775\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.362962962962963,\n \"acc_stderr\": 0.02931820364520686,\n \ \ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.02931820364520686\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.030489911417673227,\n\ \ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.030489911417673227\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8091743119266055,\n \"acc_stderr\": 0.01684767640009109,\n \"\ acc_norm\": 0.8091743119266055,\n \"acc_norm_stderr\": 0.01684767640009109\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4537037037037037,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7401960784313726,\n \"acc_stderr\": 0.03077855467869327,\n \"\ acc_norm\": 0.7401960784313726,\n \"acc_norm_stderr\": 0.03077855467869327\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7426160337552743,\n \"acc_stderr\": 0.0284588209914603,\n \ \ \"acc_norm\": 0.7426160337552743,\n \"acc_norm_stderr\": 0.0284588209914603\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7085201793721974,\n\ \ \"acc_stderr\": 0.030500283176545854,\n \"acc_norm\": 0.7085201793721974,\n\ \ \"acc_norm_stderr\": 0.030500283176545854\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6946564885496184,\n \"acc_stderr\": 0.04039314978724561,\n\ \ \"acc_norm\": 0.6946564885496184,\n \"acc_norm_stderr\": 0.04039314978724561\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990946,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990946\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\ \ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\ \ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7300613496932515,\n \"acc_stderr\": 0.034878251684978906,\n\ \ \"acc_norm\": 0.7300613496932515,\n \"acc_norm_stderr\": 0.034878251684978906\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\ \ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8418803418803419,\n\ \ \"acc_stderr\": 0.0239023255495604,\n \"acc_norm\": 0.8418803418803419,\n\ \ \"acc_norm_stderr\": 0.0239023255495604\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7650063856960408,\n\ \ \"acc_stderr\": 0.015162024152278445,\n \"acc_norm\": 0.7650063856960408,\n\ \ \"acc_norm_stderr\": 0.015162024152278445\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.025816756791584204,\n\ \ \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.025816756791584204\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23687150837988827,\n\ \ \"acc_stderr\": 0.01421957078810399,\n \"acc_norm\": 0.23687150837988827,\n\ \ \"acc_norm_stderr\": 0.01421957078810399\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6699346405228758,\n \"acc_stderr\": 0.026925654653615697,\n\ \ \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.026925654653615697\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6527331189710611,\n\ \ \"acc_stderr\": 0.027040745502307336,\n \"acc_norm\": 0.6527331189710611,\n\ \ \"acc_norm_stderr\": 0.027040745502307336\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6358024691358025,\n \"acc_stderr\": 0.02677492989972234,\n\ \ \"acc_norm\": 0.6358024691358025,\n \"acc_norm_stderr\": 0.02677492989972234\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.45390070921985815,\n \"acc_stderr\": 0.02970045324729147,\n \ \ \"acc_norm\": 0.45390070921985815,\n \"acc_norm_stderr\": 0.02970045324729147\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4485006518904824,\n\ \ \"acc_stderr\": 0.012702317490559806,\n \"acc_norm\": 0.4485006518904824,\n\ \ \"acc_norm_stderr\": 0.012702317490559806\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.03035969707904612,\n\ \ \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03035969707904612\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5898692810457516,\n \"acc_stderr\": 0.019898412717635903,\n \ \ \"acc_norm\": 0.5898692810457516,\n \"acc_norm_stderr\": 0.019898412717635903\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.0287951855742913,\n\ \ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.0287951855742913\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n\ \ \"acc_stderr\": 0.027686913588013028,\n \"acc_norm\": 0.8109452736318408,\n\ \ \"acc_norm_stderr\": 0.027686913588013028\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5060240963855421,\n\ \ \"acc_stderr\": 0.03892212195333047,\n \"acc_norm\": 0.5060240963855421,\n\ \ \"acc_norm_stderr\": 0.03892212195333047\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.0312678171466318,\n\ \ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.0312678171466318\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.34149326805385555,\n\ \ \"mc1_stderr\": 0.016600688619950826,\n \"mc2\": 0.5039555794689907,\n\ \ \"mc2_stderr\": 0.01643070151671278\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6992896606156275,\n \"acc_stderr\": 0.01288801049470473\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4177407126611069,\n \ \ \"acc_stderr\": 0.013584820638504828\n }\n}\n```" repo_url: https://huggingface.co/OpenModels4all/gemma-1.1-7b-it 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_09T11_06_14.950936 path: - '**/details_harness|arc:challenge|25_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-09T11-06-14.950936.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|gsm8k|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hellaswag|10_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-09T11-06-14.950936.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-management|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-09T11-06-14.950936.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|truthfulqa:mc|0_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-09T11-06-14.950936.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_09T11_06_14.950936 path: - '**/details_harness|winogrande|5_2024-04-09T11-06-14.950936.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-09T11-06-14.950936.parquet' - config_name: results data_files: - split: 2024_04_09T11_06_14.950936 path: - results_2024-04-09T11-06-14.950936.parquet - split: latest path: - results_2024-04-09T11-06-14.950936.parquet --- # Dataset Card for Evaluation run of OpenModels4all/gemma-1.1-7b-it  Dataset automatically created during the evaluation run of model [OpenModels4all/gemma-1.1-7b-it](https://huggingface.co/OpenModels4all/gemma-1.1-7b-it) 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_OpenModels4all__gemma-1.1-7b-it", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-09T11:06:14.950936](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenModels4all__gemma-1.1-7b-it/blob/main/results_2024-04-09T11-06-14.950936.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": 0.6014747509936101, "acc_stderr": 0.03326656009179332, "acc_norm": 0.6064901781756107, "acc_norm_stderr": 0.03393142550082237, "mc1": 0.34149326805385555, "mc1_stderr": 0.016600688619950826, "mc2": 0.5039555794689907, "mc2_stderr": 0.01643070151671278 }, "harness|arc:challenge|25": { "acc": 0.5656996587030717, "acc_stderr": 0.01448470304885736, "acc_norm": 0.5998293515358362, "acc_norm_stderr": 0.014317197787809174 }, "harness|hellaswag|10": { "acc": 0.5862378012348137, "acc_stderr": 0.004915003499517829, "acc_norm": 0.7620991834295957, "acc_norm_stderr": 0.004249278842903416 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5111111111111111, "acc_stderr": 0.04318275491977976, "acc_norm": 0.5111111111111111, "acc_norm_stderr": 0.04318275491977976 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6226415094339622, "acc_stderr": 0.029832808114796, "acc_norm": 0.6226415094339622, "acc_norm_stderr": 0.029832808114796 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6597222222222222, "acc_stderr": 0.039621355734862175, "acc_norm": 0.6597222222222222, "acc_norm_stderr": 0.039621355734862175 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5953757225433526, "acc_stderr": 0.03742461193887248, "acc_norm": 0.5953757225433526, "acc_norm_stderr": 0.03742461193887248 }, "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": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5574468085106383, "acc_stderr": 0.032469569197899575, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.032469569197899575 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.40350877192982454, "acc_stderr": 0.04615186962583702, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.04615186962583702 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.46296296296296297, "acc_stderr": 0.02568056464005688, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.02568056464005688 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4444444444444444, "acc_stderr": 0.044444444444444495, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.044444444444444495 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7451612903225806, "acc_stderr": 0.024790118459332208, "acc_norm": 0.7451612903225806, "acc_norm_stderr": 0.024790118459332208 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175007, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175007 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.696969696969697, "acc_stderr": 0.03588624800091707, "acc_norm": 0.696969696969697, "acc_norm_stderr": 0.03588624800091707 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7575757575757576, "acc_stderr": 0.030532892233932046, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.030532892233932046 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8393782383419689, "acc_stderr": 0.02649905770139744, "acc_norm": 0.8393782383419689, "acc_norm_stderr": 0.02649905770139744 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5974358974358974, "acc_stderr": 0.02486499515976775, "acc_norm": 0.5974358974358974, "acc_norm_stderr": 0.02486499515976775 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.362962962962963, "acc_stderr": 0.02931820364520686, "acc_norm": 0.362962962962963, "acc_norm_stderr": 0.02931820364520686 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6722689075630253, "acc_stderr": 0.030489911417673227, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.030489911417673227 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.32450331125827814, "acc_stderr": 0.03822746937658752, "acc_norm": 0.32450331125827814, "acc_norm_stderr": 0.03822746937658752 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8091743119266055, "acc_stderr": 0.01684767640009109, "acc_norm": 0.8091743119266055, "acc_norm_stderr": 0.01684767640009109 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4537037037037037, "acc_stderr": 0.033953227263757976, "acc_norm": 0.4537037037037037, "acc_norm_stderr": 0.033953227263757976 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7401960784313726, "acc_stderr": 0.03077855467869327, "acc_norm": 0.7401960784313726, "acc_norm_stderr": 0.03077855467869327 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7426160337552743, "acc_stderr": 0.0284588209914603, "acc_norm": 0.7426160337552743, "acc_norm_stderr": 0.0284588209914603 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7085201793721974, "acc_stderr": 0.030500283176545854, "acc_norm": 0.7085201793721974, "acc_norm_stderr": 0.030500283176545854 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6946564885496184, "acc_stderr": 0.04039314978724561, "acc_norm": 0.6946564885496184, "acc_norm_stderr": 0.04039314978724561 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990946, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990946 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7685185185185185, "acc_stderr": 0.04077494709252626, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.04077494709252626 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7300613496932515, "acc_stderr": 0.034878251684978906, "acc_norm": 0.7300613496932515, "acc_norm_stderr": 0.034878251684978906 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness|hendrycksTest-management|5": { "acc": 0.7669902912621359, "acc_stderr": 0.04185832598928315, "acc_norm": 0.7669902912621359, "acc_norm_stderr": 0.04185832598928315 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8418803418803419, "acc_stderr": 0.0239023255495604, "acc_norm": 0.8418803418803419, "acc_norm_stderr": 0.0239023255495604 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7650063856960408, "acc_stderr": 0.015162024152278445, "acc_norm": 0.7650063856960408, "acc_norm_stderr": 0.015162024152278445 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6416184971098265, "acc_stderr": 0.025816756791584204, "acc_norm": 0.6416184971098265, "acc_norm_stderr": 0.025816756791584204 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23687150837988827, "acc_stderr": 0.01421957078810399, "acc_norm": 0.23687150837988827, "acc_norm_stderr": 0.01421957078810399 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6699346405228758, "acc_stderr": 0.026925654653615697, "acc_norm": 0.6699346405228758, "acc_norm_stderr": 0.026925654653615697 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6527331189710611, "acc_stderr": 0.027040745502307336, "acc_norm": 0.6527331189710611, "acc_norm_stderr": 0.027040745502307336 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6358024691358025, "acc_stderr": 0.02677492989972234, "acc_norm": 0.6358024691358025, "acc_norm_stderr": 0.02677492989972234 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.45390070921985815, "acc_stderr": 0.02970045324729147, "acc_norm": 0.45390070921985815, "acc_norm_stderr": 0.02970045324729147 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4485006518904824, "acc_stderr": 0.012702317490559806, "acc_norm": 0.4485006518904824, "acc_norm_stderr": 0.012702317490559806 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5147058823529411, "acc_stderr": 0.03035969707904612, "acc_norm": 0.5147058823529411, "acc_norm_stderr": 0.03035969707904612 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5898692810457516, "acc_stderr": 0.019898412717635903, "acc_norm": 0.5898692810457516, "acc_norm_stderr": 0.019898412717635903 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7183673469387755, "acc_stderr": 0.0287951855742913, "acc_norm": 0.7183673469387755, "acc_norm_stderr": 0.0287951855742913 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8109452736318408, "acc_stderr": 0.027686913588013028, "acc_norm": 0.8109452736318408, "acc_norm_stderr": 0.027686913588013028 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-virology|5": { "acc": 0.5060240963855421, "acc_stderr": 0.03892212195333047, "acc_norm": 0.5060240963855421, "acc_norm_stderr": 0.03892212195333047 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7894736842105263, "acc_stderr": 0.0312678171466318, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.0312678171466318 }, "harness|truthfulqa:mc|0": { "mc1": 0.34149326805385555, "mc1_stderr": 0.016600688619950826, "mc2": 0.5039555794689907, "mc2_stderr": 0.01643070151671278 }, "harness|winogrande|5": { "acc": 0.6992896606156275, "acc_stderr": 0.01288801049470473 }, "harness|gsm8k|5": { "acc": 0.4177407126611069, "acc_stderr": 0.013584820638504828 } } ``` ## 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]

sieu-n/alpaca_eval_multilingual

--- license: cc-by-nc-4.0 --- ### Usage ``` load_dataset("krenerd/alpaca_eval_multilingual", "alpaca_eval") # or alpaca_eval_en load_dataset("krenerd/alpaca_eval_multilingual", "alpaca_eval_ko") load_dataset("krenerd/alpaca_eval_multilingual", "alpaca_eval_ja") ``` ### Method The dataset was translated by GPT-4 API using the following prompt. ``` ja = ChatPromptTemplate.from_messages( [ SystemMessagePromptTemplate.from_template( "You are a helpful assistant fluent in English and Japanese." ), HumanMessagePromptTemplate.from_template( "Translate the following text to Japanese. Show the answer only. このテキストを直訳するのではなく、その意味を保持しつつ、より自然なリクエストに言い換えて翻訳してください text=```{instruction}```" ), ] ) ko = ChatPromptTemplate.from_messages( [ SystemMessagePromptTemplate.from_template( "You are a helpful assistant fluent in English and Korean." ), HumanMessagePromptTemplate.from_template( "Translate the following text to Korean. Show the answer only. 말 그대로 번역하지 말고, 의미가 유지되는 한에서 자연스러운 요청으로 번역해줘. text=```{instruction}```" ), ] ) ``` Script: https://gist.github.com/sieu-n/88542733914f80f780359f5c82c99a62

llm-blender/mix-instruct

--- license: mit task_categories: - text-generation language: - en pretty_name: mix-instruct size_categories: - 100K<n<1M --- # MixInstruct ## Introduction This is the official realease of dataset **MixInstruct** for project **LLM-Blender**. This dataset contains 11 responses from the current popular instruction following-LLMs that includes: 1. [Stanford Alpaca](https://huggingface.co/chavinlo/alpaca-native) 2. [FastChat Vicuna](https://huggingface.co/eachadea/vicuna-13b-1.1) 3. [Dolly V2](https://huggingface.co/databricks/dolly-v2-12b) 4. [StableLM](https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b) 5. [Open Assistant](https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5) 6. [Koala](https://huggingface.co/TheBloke/koala-7B-HF) 7. [Baize](https://huggingface.co/mosesjun0h/llama-7b-hf-baize-lora-bf16) 8. [Flan-T5](https://huggingface.co/google/flan-t5-xxl) 9. [ChatGLM](https://huggingface.co/THUDM/chatglm-6b) 10. [MOSS](https://huggingface.co/fnlp/moss-moon-003-sft) 11. [Moasic MPT](https://huggingface.co/mosaicml/mpt-7b-instruct) We evaluate each response with auto metrics including BLEU, ROUGE, BERTScore, BARTScore. And provide pairwise comparison results by prompting ChatGPT for the $4771$ examples in the test split. (The rest $229$ examples contain contents filtered by the API). ## Data Format ```json [ { "id": "unified_chip2/69962", "instruction": "", "input": "I've always wondered what the difference is between a skeptic and a denier.", "output": "A skeptic is someone who questions the validity of something, while a denier is someone who outright rejects something without evidence or reason.", "candidates": [ { "decoding_method": "top_p_sampling", "model": "oasst-sft-4-pythia-12b-epoch-3.5", "text": "A skeptic is someone who doubts or expresses ...", "scores": { "logprobs": -0.02404022216796875, "bleu": 5.656152750894142, "bertscore": 0.7549101114273071, "rouge1": 0.2857142857142857, "rouge2": 0.1272727272727273, "rougeL": 0.23214285714285715, "rougeLsum": 0.23214285714285715 } }, ... ], }, ... ] ``` Examples evaluted by ChatGPT will contain another filed **cmp_results**. The options contains: 1. A is better 2. B is better 3. Same good 4. Same bad ```json "cmp_results": { "model_A,model_B": "A is better", ... }, ``` Each cmp_results field is encoded into a str in a json format. Please first use `json.loads(item['cmp_results'])` to get the cmp_results for each item. "null" denotes no cmp_results from ChatGPT avaliable. ## Eval Results ### Auto Metrics - train | Models (down) / Metircs (right) | logprobs | rougeL | rouge2 | rougeLsum | rouge1 | bleu | bertscore | bleurt | bartscore | |:----------------------------------|:------------|:----------------|:----------------|:----------------|:----------------|:----------------|:----------------|:----------------|:-------------| | alpaca-native | -6.1247 | 0.248 | 0.1414 | 0.2986 | 0.3347 | 8.057 | 0.7196 | -0.5092 | -3.5335 | | chatglm-6b | -10.1263 | 0.2231 | 0.1212 | 0.2743 | 0.3074 | 6.2597 | 0.7043 | -0.6071 | -3.4975 | | dolly-v2-12b | -24.8508 | 0.1245 | 0.0502 | 0.1625 | 0.1836 | 2.1062 | 0.6244 | -0.8562 | -3.8145 | | flan-t5-xxl | -1.0717 | 0.1202 | 0.0456 | 0.1334 | 0.1489 | 1.8418 | 0.6514 | -1.2176 | -4.537 | | koala-7B-HF | -10.8323 | 0.1533 | 0.0683 | 0.1909 | 0.2165 | 3.2848 | 0.6436 | -0.8284 | -3.8326 | | llama-7b-hf-baize-lora-bf16 | -24.8867 | 0.1539 | 0.0797 | 0.2042 | 0.2276 | 3.4928 | 0.6564 | -0.6575 | -3.496 | | moss-moon-003-sft | -796.1366 | 0.1599 | 0.0898 | 0.2135 | 0.236 | 3.944 | 0.6689 | -0.5617 | -3.3404 | | mpt-7b | -174.1702 | 0.1118 | 0.0447 | 0.1517 | 0.1683 | 1.7698 | 0.618 | -0.9525 | -3.9119 | | mpt-7b-instruct | -156.8005 | 0.1225 | 0.0538 | 0.1669 | 0.1861 | 2.1041 | 0.6327 | -0.8176 | -3.6996 | | oasst-sft-4-pythia-12b-epoch-3.5 | -4.7714 | 0.2902 | 0.1763 | 0.3447 | 0.386 | 10.6599 | 0.748 | -0.3762 | -3.4221 | | stablelm-tuned-alpha-7b | -1268.9396 | 0.1336 | 0.0544 | 0.1714 | 0.1948 | 2.6348 | 0.6355 | -0.9585 | -4.0795 | | vicuna-13b-1.1 | -11.1528 | 0.211 | 0.1219 | 0.2671 | 0.3003 | 6.3697 | 0.6928 | -0.6194 | -3.4233 | | Best Model Metric Perf | -1.0717 | 0.2902 | 0.1763 | 0.3447 | 0.386 | 10.6599 | 0.748 | -0.3762 | -3.3404 | | Oracle | 0.0 | 0.3611 | 0.2471 | 0.4242 | 0.4706 | 15.8557 | 0.7783 | 0.0723 | 0.0 | | Oracle-Best_Model Gap | 1.0717 | 0.0709 | 0.0708 | 0.0794 | 0.0846 | 5.1958 | 0.0303 | 0.4484 | 3.3404 | - val | Models (down) / Metircs (right) | logprobs | rouge1 | rouge2 | rougeLsum | rougeL | bleu | bertscore | bleurt | bartscore | |:----------------------------------|:------------|:----------------|:----------------|:----------------|:----------------|:----------------|:----------------|:----------------|:---------------| | alpaca-native | -3.3832 | 0.3342 | 0.1452 | 0.299 | 0.2503 | 8.1749 | 0.7198 | -0.5076 | -3.5517 | | chatglm-6b | -4.7033 | 0.3066 | 0.1216 | 0.2743 | 0.2241 | 6.3323 | 0.7053 | -0.6091 | -3.51 | | dolly-v2-12b | -9.1237 | 0.1843 | 0.0511 | 0.1633 | 0.1254 | 2.1368 | 0.6257 | -0.852 | -3.8121 | | flan-t5-xxl | -1.0077 | 0.1497 | 0.0464 | 0.1342 | 0.1212 | 1.8653 | 0.652 | -1.2089 | -4.5407 | | koala-7B-HF | -6.015 | 0.2154 | 0.068 | 0.1903 | 0.1538 | 3.2596 | 0.6425 | -0.8298 | -3.8456 | | llama-7b-hf-baize-lora-bf16 | -12.2594 | 0.2261 | 0.0803 | 0.2034 | 0.1543 | 3.5462 | 0.6562 | -0.6604 | -3.4831 | | moss-moon-003-sft | -357.3054 | 0.2053 | 0.0678 | 0.1851 | 0.1361 | 2.9639 | 0.648 | -0.7261 | -3.6317 | | mpt-7b | -171.9416 | 0.1663 | 0.0447 | 0.1499 | 0.1111 | 1.7555 | 0.617 | -0.964 | -3.9189 | | mpt-7b-instruct | -157.1143 | 0.1841 | 0.054 | 0.1652 | 0.1224 | 2.1252 | 0.6307 | -0.8275 | -3.7183 | | oasst-ft-4-pythia-12b-epoch-3.5 | -1.6194 | 0.3835 | 0.1761 | 0.3434 | 0.2896 | 10.5858 | 0.7479 | -0.378 | -3.4366 | | stablelm-tuned-alpha-7b | -869.6767 | 0.192 | 0.0529 | 0.1688 | 0.1317 | 2.5687 | 0.6314 | -0.9618 | -4.1008 | | vicuna-13b-1.1 | -5.6143 | 0.3029 | 0.1242 | 0.2701 | 0.2142 | 6.5299 | 0.695 | -0.6212 | -3.4332 | | Best Model Metric Perf | -1.0077 | 0.3835 | 0.1761 | 0.3434 | 0.2896 | 10.5858 | 0.7479 | -0.378 | -3.4332 | | Oracle | 0.0 | 0.4712 | 0.2488 | 0.4258 | 0.3642 | 15.9896 | 0.7794 | 0.0726 | 0.0 | | Oracle-Best_Model Gap | 1.0077 | 0.0877 | 0.0728 | 0.0824 | 0.0746 | 5.4038 | 0.0315 | 0.4506 | 3.4332 | - test | Models (down) / Metircs (right) | logprobs | rougeL | rougeLsum | rouge1 | rouge2 | bleu | bertscore | bleurt | bartscore | |:----------------------------------|:------------|:----------------|:----------------|:----------------|:----------------|:----------------|:----------------|:----------------|:---------------| | alpaca-native | -3.458 | 0.2421 | 0.2915 | 0.3276 | 0.1362 | 7.6478 | 0.7146 | -0.5307 | -3.5696 | | chatglm-6b | -4.7418 | 0.2225 | 0.2734 | 0.3063 | 0.1192 | 6.0493 | 0.7038 | -0.6167 | -3.5193 | | dolly-v2-12b | -9.1266 | 0.1236 | 0.1606 | 0.1811 | 0.0495 | 2.062 | 0.6226 | -0.8654 | -3.8331 | | flan-t5-xxl | -0.9924 | 0.1172 | 0.1296 | 0.1444 | 0.0432 | 1.6066 | 0.6492 | -1.2288 | -4.5717 | | koala-7B-HF | -6.1159 | 0.1507 | 0.1871 | 0.2131 | 0.0662 | 3.0983 | 0.6396 | -0.8354 | -3.8496 | | llama-7b-hf-baize-lora-bf16 | -11.9519 | 0.1521 | 0.2022 | 0.2253 | 0.0781 | 3.4005 | 0.6557 | -0.663 | -3.526 | | moss-moon-003-sft | -356.8774 | 0.1365 | 0.1863 | 0.2062 | 0.0686 | 2.9561 | 0.6485 | -0.7261 | -3.6461 | | mpt-7b | -176.2144 | 0.1106 | 0.1498 | 0.1663 | 0.0439 | 1.7392 | 0.6165 | -0.9636 | -3.9419 | | mpt-7b-instruct | -156.0153 | 0.121 | 0.1647 | 0.1837 | 0.0524 | 2.0692 | 0.6321 | -0.8232 | -3.7208 | | oasst-sft-4-pythia-12b-epoch-3.5 | -1.6749 | 0.2873 | 0.341 | 0.3813 | 0.1738 | 10.5046 | 0.7468 | -0.3908 | -3.4486 | | stablelm-tuned-alpha-7b | -831.595 | 0.1306 | 0.1672 | 0.1904 | 0.0524 | 2.5044 | 0.6247 | -0.9832 | -4.1208 | | vicuna-13b-1.1 | -5.6914 | 0.2122 | 0.2677 | 0.3012 | 0.1223 | 6.3584 | 0.696 | -0.6146 | -3.4368 | | Best Model Metric Perf | -0.9924 | 0.2873 | 0.341 | 0.3813 | 0.1738 | 10.5046 | 0.7468 | -0.3908 | -3.4368 | | Oracle | 0.0 | 0.3585 | 0.4201 | 0.466 | 0.2438 | 15.4971 | 0.7767 | 0.0679 | 0.0 | | Oracle-Best_Model Gap | 0.9924 | 0.0712 | 0.0791 | 0.0847 | 0.07 | 4.9925 | 0.0299 | 0.4587 | 3.4368 | ### ChatGPT CMPTS (4771 examples) | **Methods** | BERTScore | BARTScore | BLEURT | GPT-Rank | Beat Vic(%) | Beat OA(%) | Top-1(%) | Top-2(%) | Top-3(%) | |:-----------------:|:---------:|:---------:|:---------:|:--------:|:----------:|:----------:|:----------:|:----------:|:----------:| | Open Assistant | **74.68** | -3.45 | **-0.39** | **3.90** | **62.78** | N/A | 17.35 | 35.67 | 51.98 | | Vicuna | 69.60 | **-3.44** | -0.61 | 4.13 | N/A | **64.77** | **25.47** | **41.23** | **52.88** | | Alpaca | 71.46 | -3.57 | -0.53 | 4.62 | 56.70 | 61.35 | 15.41 | 29.81 | 44.46 | | Baize | 65.57 | -3.53 | -0.66 | 4.86 | 52.76 | 56.40 | 14.23 | 26.91 | 38.80 | | moss | 64.85 | -3.65 | -0.73 | 5.09 | 51.62 | 51.79 | 15.93 | 27.52 | 38.27 | | ChatGLM | 70.38 | -3.52 | -0.62 | 5.63 | 44.04 | 45.67 | 9.41 | 19.37 | 28.78 | | Koala | 63.96 | -3.85 | -0.84 | 6.76 | 39.93 | 39.01 | 8.15 | 15.72 | 22.55 | | Dolly v2 | 62.26 | -3.83 | -0.87 | 6.90 | 33.33 | 31.44 | 5.16 | 10.06 | 16.45 | | Mosaic MPT | 63.21 | -3.72 | -0.82 | 7.19 | 30.87 | 30.16 | 5.39 | 10.61 | 16.24 | | StableLM | 62.47 | -4.12 | -0.98 | 8.71 | 21.55 | 19.87 | 2.33 | 4.74 | 7.96 | | Flan-T5 | 64.92 | -4.57 | -1.23 | 8.81 | 23.89 | 19.93 | 1.30 | 2.87 | 5.32 | | Oracle(BERTScore) | **77.67** | -3.17 | -0.27 | 3.88 | 54.41 | 38.84 | 20.16 | 38.11 | 53.49 | | Oracle(BLEURT) | 75.02 | -3.15 | **-0.15** | 3.77 | 55.61 | 45.80 | 21.48 | 39.84 | 55.36 | | Oracle(BARTScore) | 73.23 | **-2.87** | -0.38 | 3.69 | 50.32 | 57.01 | 26.10 | 43.70 | 57.33 | | Oracle(ChatGPT) | 70.32 | -3.33 | -0.51 | **1.00** | **100.00** | **100.00** | **100.00** | **100.00** | **100.00** |

LambdaTests/VQAv2_sample_validation_benchmarks_partition_global_6_loca_6

--- dataset_info: features: - name: id dtype: int64 - name: response dtype: string splits: - name: train num_bytes: 43 num_examples: 1 download_size: 0 dataset_size: 43 --- # Dataset Card for "VQAv2_sample_validation_benchmarks_partition_global_6_loca_6" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

textminr/topic-labeling

--- language: - en - de size_categories: - n<1K configs: - config_name: gutenberg default: true data_files: - split: train path: - "gutenberg/train.jsonl" - config_name: generic data_files: - split: train path: - "base/train.jsonl" - "mn-ds/train.jsonl" task_categories: - text-classification pretty_name: Topic Labeling --- # Topic Labeling This is a carefully crafted dataset for topic labeling. It maps a series of words (generated by topic models like LDA, Top2Vec etc.) to a topic label so that LLMs like T5, Mistral etc. can be fine-tuned on this dataset to generate topic labels for a given text. Parts of this dataset have been labeled manually or using GPT-4. Source datasets: - [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) - [textminr/mn-ds](https://huggingface.co/datasets/textminr/mn-ds) Additional sources: - [Project Gutenberg](https://www.gutenberg.org/)

amogh-sinha/text

--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 24588.0 num_examples: 3 - name: test num_bytes: 8196 num_examples: 1 download_size: 22034 dataset_size: 32784.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/tuye_arknights

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tuye/トゥイエ/图耶 (Arknights) This is the dataset of tuye/トゥイエ/图耶 (Arknights), containing 69 images and their tags. The core tags of this character are `animal_ears, dark_skin, dark-skinned_female, purple_eyes, white_hair, long_hair, hair_between_eyes, horse_ears, grey_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 | 69 | 125.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tuye_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 69 | 106.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tuye_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 184 | 210.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tuye_arknights/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/tuye_arknights', 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 | 35 | ![](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, long_sleeves, looking_at_viewer, yellow_jacket, open_jacket, yellow_coat, black_gloves, holding, thigh_strap, black_dress, closed_mouth, fingerless_gloves, smile, twintails, umbrella | | 1 | 12 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, long_sleeves, looking_at_viewer, solo, official_alternate_costume, short_hair, closed_mouth, white_ascot, white_gloves, white_thighhighs, black_dress, flower, garter_straps, smile, black_footwear, white_background, holding, shoes, simple_background, sitting | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | long_sleeves | looking_at_viewer | yellow_jacket | open_jacket | yellow_coat | black_gloves | holding | thigh_strap | black_dress | closed_mouth | fingerless_gloves | smile | twintails | umbrella | official_alternate_costume | short_hair | white_ascot | white_gloves | white_thighhighs | flower | garter_straps | black_footwear | white_background | shoes | simple_background | sitting | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:--------------------|:----------------|:--------------|:--------------|:---------------|:----------|:--------------|:--------------|:---------------|:--------------------|:--------|:------------|:-----------|:-----------------------------|:-------------|:--------------|:---------------|:-------------------|:---------|:----------------|:-----------------|:-------------------|:--------|:--------------------|:----------| | 0 | 35 | ![](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 | X | X | | | | | | | | | | | | | | 1 | 12 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | | | | | X | | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X |

Vaibhav9401/toxic25m

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: llama_finetune_text dtype: string splits: - name: train num_bytes: 20143312184 num_examples: 25159680 download_size: 3446911922 dataset_size: 20143312184 --- # Dataset Card for "toxic25m" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ChrisWilson/twitter_dataset_1710354721

--- 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: 33402 num_examples: 89 download_size: 26069 dataset_size: 33402 configs: - config_name: default data_files: - split: train path: data/train-* ---

HarryAJMK418/NewEmINTER

--- license: openrail ---

romariocamilo/noah.mp3

--- license: openrail ---

Djacon/ru_goemotions

--- language: - ru license: - mit multilinguality: - monolingual task_categories: - text-classification task_ids: - multi-class-classification - multi-label-classification pretty_name: RuGoEmotions tags: - emotion --- # Dataset Card for GoEmotions ## 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 Summary The RuGoEmotions dataset contains 34k Reddit comments labeled for 9 emotion categories (joy, interest, surprice, sadness, anger, disgust, fear, guilt and neutral). The dataset already with predefined train/val/test splits ### Supported Tasks and Leaderboards This dataset is intended for multi-class, multi-label emotion classification. ### Languages The data is in Russian. ## Dataset Structure ### Data Instances Each instance is a reddit comment with one or more emotion annotations (or neutral). ### Data Fields The configuration includes: - `text`: the reddit comment - `labels`: the emotion annotations ### Data Splits The simplified data includes a set of train/val/test splits with 26.9k, 3.29k, and 3.37k examples respectively. ## Dataset Creation ### Curation Rationale From the paper abstract: > Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. ### Source Data #### Initial Data Collection and Normalization Data was collected from Reddit comments via a variety of automated methods discussed in 3.1 of the paper. #### Who are the source language producers? English-speaking Reddit users. ### Annotations #### Who are the annotators? Annotations were produced by 3 English-speaking crowdworkers in India. ### Personal and Sensitive Information This dataset includes the original usernames of the Reddit users who posted each comment. Although Reddit usernames are typically disasociated from personal real-world identities, this is not always the case. It may therefore be possible to discover the identities of the individuals who created this content in some cases. ## Considerations for Using the Data ### Social Impact of Dataset Emotion detection is a worthwhile problem which can potentially lead to improvements such as better human/computer interaction. However, emotion detection algorithms (particularly in computer vision) have been abused in some cases to make erroneous inferences in human monitoring and assessment applications such as hiring decisions, insurance pricing, and student attentiveness (see [this article](https://www.unite.ai/ai-now-institute-warns-about-misuse-of-emotion-detection-software-and-other-ethical-issues/)). ### Discussion of Biases From the authors' github page: > Potential biases in the data include: Inherent biases in Reddit and user base biases, the offensive/vulgar word lists used for data filtering, inherent or unconscious bias in assessment of offensive identity labels, annotators were all native English speakers from India. All these likely affect labelling, precision, and recall for a trained model. Anyone using this dataset should be aware of these limitations of the dataset. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators Researchers at Amazon Alexa, Google Research, and Stanford. See the [author list](https://arxiv.org/abs/2005.00547). ### Licensing Information The GitHub repository which houses this dataset has an [Apache License 2.0](https://github.com/google-research/google-research/blob/master/LICENSE). ### Citation Information @inproceedings{demszky2020goemotions, author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith}, booktitle = {58th Annual Meeting of the Association for Computational Linguistics (ACL)}, title = {{GoEmotions: A Dataset of Fine-Grained Emotions}}, year = {2020} } ### Contributions Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.

xjlulu/ntu_adl_intent

--- license: apache-2.0 task_categories: - text-classification language: - en ---

CyberHarem/tsukumo_yatsuhashi_touhou

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tsukumo_yatsuhashi/九十九八橋 (Touhou) This is the dataset of tsukumo_yatsuhashi/九十九八橋 (Touhou), containing 253 images and their tags. The core tags of this character are `brown_hair, short_hair, hairband, brown_eyes, purple_hairband`, 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 | 253 | 190.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukumo_yatsuhashi_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 253 | 149.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukumo_yatsuhashi_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 452 | 250.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukumo_yatsuhashi_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 253 | 183.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukumo_yatsuhashi_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 452 | 296.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsukumo_yatsuhashi_touhou/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/tsukumo_yatsuhashi_touhou', 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 | 12 | ![](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, barefoot, black_skirt, smile, solo, white_shirt, collared_shirt, long_sleeves, simple_background, closed_mouth, eighth_note, frilled_skirt, full_body, white_background, looking_at_viewer, staff_(music), instrument | | 1 | 15 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, long_sleeves, shirt, skirt, smile, solo, looking_at_viewer, eighth_note, open_mouth | | 2 | 10 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, beamed_eighth_notes, black_skirt, collared_shirt, long_sleeves, looking_at_viewer, solo, white_shirt, bangs, beamed_sixteenth_notes, frilled_skirt, open_mouth, quarter_note, :d, hair_between_eyes, simple_background, staff_(music), blush, cowboy_shot, instrument | | 3 | 17 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 2girls, shirt, smile, long_sleeves, open_mouth, sisters, barefoot, eighth_note, purple_hair, biwa_lute, hair_flower, looking_at_viewer, black_skirt | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | barefoot | black_skirt | smile | solo | white_shirt | collared_shirt | long_sleeves | simple_background | closed_mouth | eighth_note | frilled_skirt | full_body | white_background | looking_at_viewer | staff_(music) | instrument | shirt | skirt | open_mouth | beamed_eighth_notes | bangs | beamed_sixteenth_notes | quarter_note | :d | hair_between_eyes | blush | cowboy_shot | 2girls | sisters | purple_hair | biwa_lute | hair_flower | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:--------------|:--------|:-------|:--------------|:-----------------|:---------------|:--------------------|:---------------|:--------------|:----------------|:------------|:-------------------|:--------------------|:----------------|:-------------|:--------|:--------|:-------------|:----------------------|:--------|:-------------------------|:---------------|:-----|:--------------------|:--------|:--------------|:---------|:----------|:--------------|:------------|:--------------| | 0 | 12 | ![](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 | X | X | X | | | | | | | | | | | | | | | | | | 1 | 15 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | | X | X | | | X | | | X | | | | X | | | X | X | X | | | | | | | | | | | | | | | 2 | 10 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | | X | X | X | X | X | | | X | | | X | X | X | | | X | X | X | X | X | X | X | X | X | | | | | | | 3 | 17 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | | X | X | X | | | | X | | | X | | | | X | | | X | | X | | | | | | | | | X | X | X | X | X |

theojiang/contrastive_conditional_vid_diff_std_1_6_webvid-test

--- dataset_info: features: - name: original_image dtype: image - name: edit_prompt dtype: string - name: edited_image dtype: image splits: - name: train num_bytes: 73413092.0 num_examples: 2000 download_size: 72714847 dataset_size: 73413092.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

HamdanXI/arb-eng-parallel-1mill-splitted

--- dataset_info: features: - name: arabic dtype: string - name: english dtype: string splits: - name: train num_bytes: 343460673.8616423 num_examples: 800000 - name: validation num_bytes: 42932584.23270529 num_examples: 100000 - name: test num_bytes: 42932584.23270529 num_examples: 100000 download_size: 240888206 dataset_size: 429325842.3270529 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

AlekseyKorshuk/light-wild-chatml

--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: do_train dtype: bool - name: role dtype: string splits: - name: train num_bytes: 17574962 num_examples: 11556 download_size: 6961878 dataset_size: 17574962 --- # Dataset Card for "light-wild-chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Greich/linkedin_posts

--- license: odbl ---

AmelieSchreiber/binding_sites_random_split_by_family

--- license: mit --- This is a refined version of a dataset obtained from UniProt ([see here](https://www.uniprot.org/uniprotkb?facets=reviewed%3Atrue%2Cproteins_with%3A9&fields=accession%2Cprotein_families%2Cft_binding%2Cft_act_site%2Csequence&query=%28family%3A*%29+AND+%28ft_binding%3A*%29&view=table)). The data was first sorted by family, then random families were selected until approximately 20% of the data was separates out for test data. Next, each sequences longer than 1000 residues was segmented into non-overlapping sections of 1000 amino acids or less. Any sequences with only partial binding site annotations were thrown out (any sequences with `<`, `>`, or `?`).

apollo-research/sae-Skylion007-openwebtext-tokenizer-gpt2

--- dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 36178777200.0 num_examples: 8824092 download_size: 17731169875 dataset_size: 36178777200.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

tonyassi/celebrity-1000

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': Aaron Eckhart '1': Aaron Paul '2': Aaron Rodgers '3': Aaron Taylor-Johnson '4': Abbi Jacobson '5': Abhishek Bachchan '6': Abigail Breslin '7': Abigail Spencer '8': Adam Brody '9': Adam Devine '10': Adam Driver '11': Adam Lambert '12': Adam Levine '13': Adam Sandler '14': Adam Scott '15': Adele '16': Adrian Grenier '17': Adèle Exarchopoulos '18': Aidan Gillen '19': Aidan Turner '20': Aishwarya Rai '21': Aja Naomi King '22': Alden Ehrenreich '23': Aldis Hodge '24': Alec Baldwin '25': Alex Morgan '26': Alex Pettyfer '27': Alex Rodriguez '28': Alexander Skarsgård '29': Alexandra Daddario '30': Alfre Woodard '31': Alia Shawkat '32': Alice Braga '33': Alice Eve '34': Alicia Keys '35': Alicia Vikander '36': Alison Brie '37': Allison Janney '38': Allison Williams '39': Alyson Hannigan '40': Amanda Peet '41': Amanda Seyfried '42': Amandla Stenberg '43': Amber Heard '44': America Ferrera '45': Amy Adams '46': Amy Poehler '47': Amy Schumer '48': Ana de Armas '49': Andie MacDowell '50': Andrew Garfield '51': Andrew Lincoln '52': Andrew Scott '53': Andy Garcia '54': Andy Samberg '55': Andy Serkis '56': Angela Bassett '57': Angelina Jolie '58': Anna Camp '59': Anna Faris '60': Anna Kendrick '61': Anna Paquin '62': AnnaSophia Robb '63': Annabelle Wallis '64': Anne Hathaway '65': Anne Marie '66': Anne-Marie '67': Ansel Elgort '68': Anson Mount '69': Anthony Hopkins '70': Anthony Joshua '71': Anthony Mackie '72': Antonio Banderas '73': Anya Taylor-Joy '74': Ariana Grande '75': Armie Hammer '76': Ashley Judd '77': Ashton Kutcher '78': Aubrey Plaza '79': Auli'i Cravalho '80': Awkwafina '81': Barack Obama '82': Bella Hadid '83': Bella Thorne '84': Ben Barnes '85': Ben Mendelsohn '86': Ben Stiller '87': Ben Whishaw '88': Benedict Cumberbatch '89': Benedict Wong '90': Benicio del Toro '91': Bill Gates '92': Bill Hader '93': Bill Murray '94': Bill Pullman '95': Bill Skarsgård '96': Billie Eilish '97': Billie Lourd '98': Billy Crudup '99': Billy Porter '100': Blake Lively '101': Bob Odenkirk '102': Bonnie Wright '103': Boyd Holbrook '104': Brad Pitt '105': Bradley Cooper '106': Brendan Fraser '107': Brian Cox '108': Brie Larson '109': Brittany Snow '110': Bryan Cranston '111': Bryce Dallas Howard '112': Busy Philipps '113': Caitriona Balfe '114': Cameron Diaz '115': Camila Cabello '116': Camila Mendes '117': Cardi B '118': Carey Mulligan '119': Carla Gugino '120': Carrie Underwood '121': Casey Affleck '122': Cate Blanchett '123': Catherine Keener '124': Catherine Zeta-Jones '125': Celine Dion '126': Chace Crawford '127': Chadwick Boseman '128': Channing Tatum '129': Charlie Cox '130': Charlie Day '131': Charlie Hunnam '132': Charlie Plummer '133': Charlize Theron '134': Chiara Ferragni '135': Chiwetel Ejiofor '136': Chloe Bennet '137': Chloe Grace Moretz '138': Chloe Sevigny '139': Chloë Grace Moretz '140': Chloë Sevigny '141': Chris Cooper '142': Chris Evans '143': Chris Hemsworth '144': Chris Martin '145': Chris Messina '146': Chris Noth '147': Chris O'Dowd '148': Chris Pine '149': Chris Pratt '150': Chris Tucker '151': Chrissy Teigen '152': Christian Bale '153': Christian Slater '154': Christina Aguilera '155': Christina Applegate '156': Christina Hendricks '157': Christina Milian '158': Christina Ricci '159': Christine Baranski '160': Christoph Waltz '161': Christopher Plummer '162': Christopher Walken '163': Cillian Murphy '164': Claire Foy '165': Clive Owen '166': Clive Standen '167': Cobie Smulders '168': Colin Farrell '169': Colin Firth '170': Colin Hanks '171': Connie Britton '172': Conor McGregor '173': Constance Wu '174': Constance Zimmer '175': Courteney Cox '176': Cristiano Ronaldo '177': Daisy Ridley '178': Dak Prescott '179': Dakota Fanning '180': Dakota Johnson '181': Damian Lewis '182': Dan Stevens '183': Danai Gurira '184': Dane DeHaan '185': Daniel Craig '186': Daniel Dae Kim '187': Daniel Day-Lewis '188': Daniel Gillies '189': Daniel Kaluuya '190': Daniel Mays '191': Daniel Radcliffe '192': Danny DeVito '193': Darren Criss '194': Dave Bautista '195': Dave Franco '196': Dave Grohl '197': Daveed Diggs '198': David Attenborough '199': David Beckham '200': David Duchovny '201': David Harbour '202': David Oyelowo '203': David Schwimmer '204': David Tennant '205': David Thewlis '206': Dax Shepard '207': Debra Messing '208': Demi Lovato '209': Dennis Quaid '210': Denzel Washington '211': Dermot Mulroney '212': Dev Patel '213': Diane Keaton '214': Diane Kruger '215': Diane Lane '216': Diego Boneta '217': Diego Luna '218': Djimon Hounsou '219': Dolly Parton '220': Domhnall Gleeson '221': Dominic Cooper '222': Dominic Monaghan '223': Dominic West '224': Don Cheadle '225': Donald Glover '226': Donald Sutherland '227': Donald Trump '228': Dua Lipa '229': Dwayne "The Rock" Johnson '230': Dwayne Johnson '231': Dylan O'Brien '232': Ed Harris '233': Ed Helms '234': Ed Sheeran '235': Eddie Murphy '236': Eddie Redmayne '237': Edgar Ramirez '238': Edward Norton '239': Eiza Gonzalez '240': Eiza González '241': Elijah Wood '242': Elisabeth Moss '243': Elisha Cuthbert '244': Eliza Coupe '245': Elizabeth Banks '246': Elizabeth Debicki '247': Elizabeth Lail '248': Elizabeth McGovern '249': Elizabeth Moss '250': Elizabeth Olsen '251': Elle Fanning '252': Ellen DeGeneres '253': Ellen Page '254': Ellen Pompeo '255': Ellie Goulding '256': Elon Musk '257': Emile Hirsch '258': Emilia Clarke '259': Emilia Fox '260': Emily Beecham '261': Emily Blunt '262': Emily Browning '263': Emily Deschanel '264': Emily Hampshire '265': Emily Mortimer '266': Emily Ratajkowski '267': Emily VanCamp '268': Emily Watson '269': Emma Bunton '270': Emma Chamberlain '271': Emma Corrin '272': Emma Mackey '273': Emma Roberts '274': Emma Stone '275': Emma Thompson '276': Emma Watson '277': Emmanuelle Chriqui '278': Emmy Rossum '279': Eoin Macken '280': Eric Bana '281': Ethan Hawke '282': Eva Green '283': Eva Longoria '284': Eva Mendes '285': Evan Peters '286': Evan Rachel Wood '287': Evangeline Lilly '288': Ewan McGregor '289': Ezra Miller '290': Felicity Huffman '291': Felicity Jones '292': Finn Wolfhard '293': Florence Pugh '294': Florence Welch '295': Forest Whitaker '296': Freddie Highmore '297': Freddie Prinze Jr. '298': Freema Agyeman '299': Freida Pinto '300': Freya Allan '301': Gabrielle Union '302': Gael Garcia Bernal '303': Gael García Bernal '304': Gal Gadot '305': Garrett Hedlund '306': Gary Oldman '307': Gemma Arterton '308': Gemma Chan '309': Gemma Whelan '310': George Clooney '311': George Lucas '312': Gerard Butler '313': Giancarlo Esposito '314': Giannis Antetokounmpo '315': Gigi Hadid '316': Gillian Anderson '317': Gillian Jacobs '318': Gina Carano '319': Gina Gershon '320': Gina Rodriguez '321': Ginnifer Goodwin '322': Gisele Bundchen '323': Glenn Close '324': Grace Kelly '325': Greg Kinnear '326': Greta Gerwig '327': Greta Scacchi '328': Greta Thunberg '329': Gugu Mbatha-Raw '330': Guy Ritchie '331': Gwen Stefani '332': Gwendoline Christie '333': Gwyneth Paltrow '334': Hafthor Bjornsson '335': Hailee Steinfeld '336': Hailey Bieber '337': Haley Joel Osment '338': Halle Berry '339': Hannah Simone '340': Harrison Ford '341': Harry Styles '342': Harvey Weinstein '343': Hayden Panettiere '344': Hayley Atwell '345': Helen Hunt '346': Helen Mirren '347': Helena Bonham Carter '348': Henry Cavill '349': Henry Golding '350': Hilary Swank '351': Himesh Patel '352': Hozier '353': Hugh Bonneville '354': Hugh Dancy '355': Hugh Grant '356': Hugh Jackman '357': Hugh Laurie '358': Ian Somerhalder '359': Idris Elba '360': Imelda Staunton '361': Imogen Poots '362': Ioan Gruffudd '363': Isabella Rossellini '364': Isabelle Huppert '365': Isla Fisher '366': Issa Rae '367': Iwan Rheon '368': J.K. Rowling '369': J.K. Simmons '370': Jack Black '371': Jack Reynor '372': Jack Whitehall '373': Jackie Chan '374': Jada Pinkett Smith '375': Jaden Smith '376': Jaimie Alexander '377': Jake Gyllenhaal '378': Jake Johnson '379': Jake T. Austin '380': James Cameron '381': James Corden '382': James Franco '383': James Marsden '384': James McAvoy '385': James Norton '386': Jamie Bell '387': Jamie Chung '388': Jamie Dornan '389': Jamie Foxx '390': Jamie Lee Curtis '391': Jamie Oliver '392': Jane Fonda '393': Jane Krakowski '394': Jane Levy '395': Jane Lynch '396': Jane Seymour '397': Janelle Monáe '398': January Jones '399': Jared Leto '400': Jason Bateman '401': Jason Clarke '402': Jason Derulo '403': Jason Isaacs '404': Jason Momoa '405': Jason Mraz '406': Jason Schwartzman '407': Jason Segel '408': Jason Statham '409': Jason Sudeikis '410': Javier Bardem '411': Jay Baruchel '412': Jay-Z '413': Jeff Bezos '414': Jeff Bridges '415': Jeff Daniels '416': Jeff Goldblum '417': Jeffrey Dean Morgan '418': Jeffrey Donovan '419': Jeffrey Wright '420': Jemima Kirke '421': Jenna Coleman '422': Jenna Fischer '423': Jenna Ortega '424': Jennifer Aniston '425': Jennifer Connelly '426': Jennifer Coolidge '427': Jennifer Esposito '428': Jennifer Garner '429': Jennifer Hudson '430': Jennifer Lawrence '431': Jennifer Lopez '432': Jennifer Love Hewitt '433': Jenny Slate '434': Jeremy Irons '435': Jeremy Renner '436': Jeremy Strong '437': Jerry Seinfeld '438': Jesse Eisenberg '439': Jesse Metcalfe '440': Jesse Plemons '441': Jesse Tyler Ferguson '442': Jesse Williams '443': Jessica Alba '444': Jessica Biel '445': Jessica Chastain '446': Jessica Lange '447': Jessie Buckley '448': Jim Carrey '449': Jim Parsons '450': Joan Collins '451': Joan Cusack '452': Joanne Froggatt '453': Joaquin Phoenix '454': Jodie Comer '455': Jodie Foster '456': Joe Jonas '457': Joe Keery '458': Joel Edgerton '459': Joel Kinnaman '460': Joel McHale '461': John Boyega '462': John C. Reilly '463': John Cena '464': John Cho '465': John Cleese '466': John Corbett '467': John David Washington '468': John Goodman '469': John Hawkes '470': John Krasinski '471': John Legend '472': John Leguizamo '473': John Lithgow '474': John Malkovich '475': John Mayer '476': John Mulaney '477': John Oliver '478': John Slattery '479': John Travolta '480': John Turturro '481': Johnny Depp '482': Johnny Knoxville '483': Jon Bernthal '484': Jon Favreau '485': Jon Hamm '486': Jonah Hill '487': Jonathan Groff '488': Jonathan Majors '489': Jonathan Pryce '490': Jonathan Rhys Meyers '491': Jordan Peele '492': Jordana Brewster '493': Joseph Fiennes '494': Joseph Gordon-Levitt '495': Josh Allen '496': Josh Brolin '497': Josh Gad '498': Josh Hartnett '499': Josh Hutcherson '500': Josh Radnor '501': Jude Law '502': Judy Dench '503': Judy Greer '504': Julia Garner '505': Julia Louis-Dreyfus '506': Julia Roberts '507': Julia Stiles '508': Julian Casablancas '509': Julian McMahon '510': Julianna Margulies '511': Julianne Hough '512': Julianne Moore '513': Julianne Nicholson '514': Juliette Binoche '515': Juliette Lewis '516': Juno Temple '517': Jurnee Smollett '518': Justin Bartha '519': Justin Bieber '520': Justin Hartley '521': Justin Herbert '522': Justin Long '523': Justin Theroux '524': Justin Timberlake '525': KJ Apa '526': Kaitlyn Dever '527': Kaley Cuoco '528': Kanye West '529': Karl Urban '530': Kat Dennings '531': Kate Beckinsale '532': Kate Bosworth '533': Kate Hudson '534': Kate Mara '535': Kate Middleton '536': Kate Upton '537': Kate Walsh '538': Kate Winslet '539': Katee Sackhoff '540': Katherine Heigl '541': Katherine Langford '542': Katherine Waterston '543': Kathryn Hahn '544': Katie Holmes '545': Katie McGrath '546': Katy Perry '547': Kaya Scodelario '548': Keanu Reeves '549': Keegan-Michael Key '550': Keira Knightley '551': Keke Palmer '552': Kelly Clarkson '553': Kelly Macdonald '554': Kelly Marie Tran '555': Kelly Reilly '556': Kelly Ripa '557': Kelvin Harrison Jr. '558': Keri Russell '559': Kerry Washington '560': Kevin Bacon '561': Kevin Costner '562': Kevin Hart '563': Kevin Spacey '564': Ki Hong Lee '565': Kiefer Sutherland '566': Kieran Culkin '567': Kiernan Shipka '568': Kim Dickens '569': Kim Kardashian '570': Kirsten Dunst '571': Kit Harington '572': Kourtney Kardashian '573': Kristen Bell '574': Kristen Stewart '575': Kristen Wiig '576': Kristin Davis '577': Krysten Ritter '578': Kyle Chandler '579': Kylie Jenner '580': Kylie Minogue '581': Lady Gaga '582': Lake Bell '583': Lakeith Stanfield '584': Lamar Jackson '585': Lana Del Rey '586': Laura Dern '587': Laura Harrier '588': Laura Linney '589': Laura Prepon '590': Laurence Fishburne '591': Laverne Cox '592': LeBron James '593': Lea Michele '594': Lea Seydoux '595': Lee Pace '596': Leighton Meester '597': Lena Headey '598': Leonardo Da Vinci '599': Leonardo DiCaprio '600': Leslie Mann '601': Leslie Odom Jr. '602': Lewis Hamilton '603': Liam Hemsworth '604': Liam Neeson '605': Lili Reinhart '606': Lily Aldridge '607': Lily Allen '608': Lily Collins '609': Lily James '610': Lily Rabe '611': Lily Tomlin '612': Lin-Manuel Miranda '613': Linda Cardellini '614': Lionel Messi '615': Lisa Bonet '616': Lisa Kudrow '617': Liv Tyler '618': Lizzo '619': Logan Lerman '620': Lorde '621': Lucy Boynton '622': Lucy Hale '623': Lucy Lawless '624': Lucy Liu '625': Luke Evans '626': Luke Perry '627': Luke Wilson '628': Lupita Nyong'o '629': Léa Seydoux '630': Mackenzie Davis '631': Madelaine Petsch '632': Mads Mikkelsen '633': Mae Whitman '634': Maggie Gyllenhaal '635': Maggie Q '636': Maggie Siff '637': Maggie Smith '638': Mahershala Ali '639': Mahira Khan '640': Maisie Richardson-Sellers '641': Maisie Williams '642': Mandy Moore '643': Mandy Patinkin '644': Marc Anthony '645': Margaret Qualley '646': Margot Robbie '647': Maria Sharapova '648': Marion Cotillard '649': Marisa Tomei '650': Mariska Hargitay '651': Mark Hamill '652': Mark Ruffalo '653': Mark Strong '654': Mark Wahlberg '655': Mark Zuckerberg '656': Marlon Brando '657': Martin Freeman '658': Martin Scorsese '659': Mary Elizabeth Winstead '660': Mary J. Blige '661': Mary Steenburgen '662': Mary-Louise Parker '663': Matt Bomer '664': Matt Damon '665': Matt LeBlanc '666': Matt Smith '667': Matthew Fox '668': Matthew Goode '669': Matthew Macfadyen '670': Matthew McConaughey '671': Matthew Perry '672': Matthew Rhys '673': Matthew Stafford '674': Max Minghella '675': Maya Angelou '676': Maya Hawke '677': Maya Rudolph '678': Megan Fox '679': Megan Rapinoe '680': Meghan Markle '681': Mel Gibson '682': Melanie Lynskey '683': Melissa Benoist '684': Melissa McCarthy '685': Melonie Diaz '686': Meryl Streep '687': Mia Wasikowska '688': Michael B. Jordan '689': Michael C. Hall '690': Michael Caine '691': Michael Cera '692': Michael Cudlitz '693': Michael Douglas '694': Michael Ealy '695': Michael Fassbender '696': Michael Jordan '697': Michael Keaton '698': Michael Pena '699': Michael Peña '700': Michael Phelps '701': Michael Shannon '702': Michael Sheen '703': Michael Stuhlbarg '704': Michelle Dockery '705': Michelle Monaghan '706': Michelle Obama '707': Michelle Pfeiffer '708': Michelle Rodriguez '709': Michelle Williams '710': Michelle Yeoh '711': Michiel Huisman '712': Mila Kunis '713': Miles Teller '714': Milla Jovovich '715': Millie Bobby Brown '716': Milo Ventimiglia '717': Mindy Kaling '718': Miranda Cosgrove '719': Miranda Kerr '720': Mireille Enos '721': Molly Ringwald '722': Morgan Freeman '723': Mélanie Laurent '724': Naomi Campbell '725': Naomi Harris '726': Naomi Scott '727': Naomi Watts '728': Naomie Harris '729': Nas '730': Natalie Dormer '731': Natalie Imbruglia '732': Natalie Morales '733': Natalie Portman '734': Nathalie Emmanuel '735': Nathalie Portman '736': Nathan Fillion '737': Naya Rivera '738': Neil Patrick Harris '739': Neil deGrasse Tyson '740': Neve Campbell '741': Neymar Jr. '742': Nicholas Braun '743': Nicholas Hoult '744': Nick Jonas '745': Nick Kroll '746': Nick Offerman '747': Nick Robinson '748': Nicole Kidman '749': Nikolaj Coster-Waldau '750': Nina Dobrev '751': Noah Centineo '752': Noomi Rapace '753': Norman Reedus '754': Novak Djokovic '755': Octavia Spencer '756': Odessa Young '757': Odette Annable '758': Olivia Colman '759': Olivia Cooke '760': Olivia Holt '761': Olivia Munn '762': Olivia Wilde '763': Oprah Winfrey '764': Orlando Bloom '765': Oscar Isaac '766': Owen Wilson '767': Pablo Picasso '768': Patrick Dempsey '769': Patrick Mahomes '770': Patrick Stewart '771': Patrick Wilson '772': Paul Bettany '773': Paul Dano '774': Paul Giamatti '775': Paul McCartney '776': Paul Rudd '777': Paul Wesley '778': Paula Patton '779': Pedro Almodóvar '780': Pedro Pascal '781': Penelope Cruz '782': Penélope Cruz '783': Pete Davidson '784': Peter Dinklage '785': Phoebe Dynevor '786': Phoebe Waller-Bridge '787': Pierce Brosnan '788': Portia de Rossi '789': Priyanka Chopra '790': Quentin Tarantino '791': Rachel Bilson '792': Rachel Brosnahan '793': Rachel McAdams '794': Rachel Weisz '795': Rafe Spall '796': Rainn Wilson '797': Ralph Fiennes '798': Rami Malek '799': Rashida Jones '800': Ray Liotta '801': Ray Romano '802': Rebecca Ferguson '803': Rebecca Hall '804': Reese Witherspoon '805': Regina Hall '806': Regina King '807': Renee Zellweger '808': Renée Zellweger '809': Rhys Ifans '810': Ricardo Montalban '811': Richard Armitage '812': Richard Gere '813': Richard Jenkins '814': Richard Madden '815': Ricky Gervais '816': Ricky Martin '817': Rihanna '818': Riley Keough '819': Rita Ora '820': River Phoenix '821': Riz Ahmed '822': Rob Lowe '823': Robert Carlyle '824': Robert De Niro '825': Robert Downey Jr. '826': Robert Pattinson '827': Robert Sheehan '828': Robin Tunney '829': Robin Williams '830': Roger Federer '831': Rooney Mara '832': Rosamund Pike '833': Rosario Dawson '834': Rose Byrne '835': Rose Leslie '836': Roselyn Sanchez '837': Ruby Rose '838': Rupert Grint '839': Russell Brand '840': Russell Crowe '841': Russell Wilson '842': Ruth Bader Ginsburg '843': Ruth Wilson '844': Ryan Eggold '845': Ryan Gosling '846': Ryan Murphy '847': Ryan Phillippe '848': Ryan Reynolds '849': Ryan Seacrest '850': Salma Hayek '851': Sam Claflin '852': Sam Heughan '853': Sam Rockwell '854': Sam Smith '855': Samara Weaving '856': Samuel L. Jackson '857': Sandra Bullock '858': Sandra Oh '859': Saoirse Ronan '860': Sarah Gadon '861': Sarah Hyland '862': Sarah Jessica Parker '863': Sarah Michelle Gellar '864': Sarah Paulson '865': Sarah Silverman '866': Sarah Wayne Callies '867': Sasha Alexander '868': Scarlett Johansson '869': Scott Speedman '870': Sean Bean '871': Sebastian Stan '872': Selena Gomez '873': Selma Blair '874': Serena Williams '875': Seth MacFarlane '876': Seth Meyers '877': Seth Rogen '878': Shailene Woodley '879': Shakira '880': Shania Twain '881': Sharlto Copley '882': Shawn Mendes '883': Shia LaBeouf '884': Shiri Appleby '885': Shohreh Aghdashloo '886': Shonda Rhimes '887': Sienna Miller '888': Sigourney Weaver '889': Simon Baker '890': Simon Cowell '891': Simon Pegg '892': Simone Biles '893': Sofia Boutella '894': Sofia Vergara '895': Sophie Turner '896': Sophie Wessex '897': Stanley Tucci '898': Stephen Amell '899': Stephen Colbert '900': Stephen Curry '901': Stephen Dorff '902': Sterling K. Brown '903': Sterling Knight '904': Steve Carell '905': Steven Yeun '906': Susan Sarandon '907': Taika Waititi '908': Taraji P. Henson '909': Taron Egerton '910': Taylor Hill '911': Taylor Kitsch '912': Taylor Lautner '913': Taylor Schilling '914': Taylor Swift '915': Teresa Palmer '916': Terrence Howard '917': Tessa Thompson '918': Thandie Newton '919': The Weeknd '920': Theo James '921': Thomas Brodie-Sangster '922': Thomas Jane '923': Tiger Woods '924': Tilda Swinton '925': Tim Burton '926': Tim Cook '927': Timothee Chalamet '928': Timothy Olyphant '929': Timothy Spall '930': Timothée Chalamet '931': Tina Fey '932': Tobey Maguire '933': Toby Jones '934': Toby Kebbell '935': Toby Regbo '936': Tom Brady '937': Tom Brokaw '938': Tom Cavanagh '939': Tom Cruise '940': Tom Ellis '941': Tom Felton '942': Tom Hanks '943': Tom Hardy '944': Tom Hiddleston '945': Tom Holland '946': Tom Hollander '947': Tom Hopper '948': Tom Selleck '949': Toni Collette '950': Tony Hale '951': Topher Grace '952': Tracee Ellis Ross '953': Tyra Banks '954': Tyrese Gibson '955': Uma Thurman '956': Usain Bolt '957': Uzo Aduba '958': Vanessa Hudgens '959': Vanessa Kirby '960': Vera Farmiga '961': Victoria Pedretti '962': Viggo Mortensen '963': Vin Diesel '964': Vince Vaughn '965': Vincent Cassel '966': Vincent D'Onofrio '967': Vincent Kartheiser '968': Viola Davis '969': Walton Goggins '970': Wes Anderson '971': Wes Bentley '972': Whoopi Goldberg '973': Will Ferrell '974': Will Poulter '975': Willem Dafoe '976': William Jackson Harper '977': William Shatner '978': Winona Ryder '979': Woody Harrelson '980': Yara Shahidi '981': Yvonne Strahovski '982': Zac Efron '983': Zach Braff '984': Zach Galifianakis '985': Zachary Levi '986': Zachary Quinto '987': Zayn Malik '988': Zazie Beetz '989': Zendaya '990': Zoe Kazan '991': Zoe Kravitz '992': Zoe Saldana '993': Zoey Deutch '994': Zooey Deschanel '995': Zoë Kravitz '996': Zoë Saldana splits: - name: train num_bytes: 193671657.464 num_examples: 18184 download_size: 190510261 dataset_size: 193671657.464 configs: - config_name: default data_files: - split: train path: data/train-* --- # Celebrity 1000 Top 1000 celebrities. 18,184 images. 256x256. Square cropped to face.

ncats/EpiSet4NER-v1

--- annotations_creators: - train: programmatically-generated - val: programmatically-generated - test: programmatically-generated, expert-validated language_creators: - found language: - en license: - other multilinguality: - monolingual size_categories: - 100K<n<1M task_categories: - structure-prediction task_ids: - named-entity-recognition --- ## 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 - **Repository:** [Github](https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard) - **Paper:** Pending ### Dataset Summary EpiSet4NER is a bronze-standard dataset for epidemiological entity recognition of location, epidemiologic types (e.g. "prevalence", "annual incidence", "estimated occurrence"), and epidemiological rates (e.g. "1.7 per 1,000,000 live births", "2.1:1.000.000", "one in five million", "0.03%") created by the [Genetic and Rare Diseases Information Center (GARD)](https://rarediseases.info.nih.gov/), a program in [the National Center for Advancing Translational Sciences](https://ncats.nih.gov/), one of the 27 [National Institutes of Health](https://www.nih.gov/). It was labeled programmatically using spaCy NER and rule-based methods. This weakly-supervised teaching method allowed us to construct this imprecise dataset with minimal manual effort and achieve satisfactory performance on a multi-type token classification problem. The test set was manually corrected by 3 NCATS researchers and a GARD curator (genetic and rare disease expert). It was used to train [EpiExtract4GARD](https://huggingface.co/ncats/EpiExtract4GARD), a BioBERT-based model fine-tuned for NER. An [example](https://pubmed.ncbi.nlm.nih.gov/24237863/) of 'train' looks as follows. ``` { "id": "333", "tokens": ['Conclusions', 'The', 'birth', 'prevalence', 'of', 'CLD', 'in', 'the', 'northern', 'Netherlands', 'was', '21.1/10,000', 'births', '.'], "ner_tags": [0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 5, 6, 0], } ``` ### Data Fields The data fields are the same among all splits. - `id`: a `string` feature that indicates sentence number. - `tokens`: a `list` of `string` features. - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-EPI` (3), `I-EPI` (4),`B-STAT` (5),`I-STAT` (6). ### Data Splits |name |train |validation|test| |---------|-----:|----:|----:| |EpiSet \# of abstracts|456|114|50| |EpiSet \# tokens |117888|31262|13910| ## Dataset Creation ![EpiSet Creation Flowchart](https://raw.githubusercontent.com/ncats/epi4GARD/master/EpiExtract4GARD/datasets/EpiCustomV3/EpiSet%20Flowchart%20FINAL.png) *Figure 1:* Creation of EpiSet4NER by NIH/NCATS Comparing the programmatically labeled test set to the manually corrected test set allowed us to measure the precision, recall, and F1 of the programmatic labeling. *Table 1:* Programmatic labeling of EpiSet4NER | Evaluation Level | Entity | Precision | Recall | F1 | |:----------------:|:------------------------:|:---------:|:------:|:-----:| | Entity-Level | Overall | 0.559 | 0.662 | 0.606 | | | Location | 0.597 | 0.661 | 0.627 | | | Epidemiologic Type | 0.854 | 0.911 | 0.882 | | | Epidemiologic Rate | 0.175 | 0.255 | 0.207 | | Token-Level | Overall | 0.805 | 0.710 | 0.755 | | | Location | 0.868 | 0.713 | 0.783 | | | Epidemiologic Type | 0.908 | 0.908 | 0.908 | | | Epidemiologic Rate | 0.739 | 0.645 | 0.689 | An example of the text labeling: ![Text Labeling](https://raw.githubusercontent.com/ncats/epi4GARD/master/EpiExtract4GARD/datasets/EpiCustomV3/Text%20Labeling4.png) *Figure 2:* Text Labeling using spaCy and rule-based labeling. Ideal labeling is bolded on the left. Actual programmatic output is on the right. [\[Figure citation\]](https://pubmed.ncbi.nlm.nih.gov/33649778/) ### Curation Rationale To train ML/DL models that automate the process of rare disease epidemiological curation. This is crucial information to patients & families, researchers, grantors, and policy makers, primarily for funding purposes. ### Source Data 620 rare disease abstracts classified as epidemiological by a LSTM RNN rare disease epi classifier from 488 diseases. See Figure 1. #### Initial Data Collection and Normalization A random sample of 500 disease names were gathered from a list of ~6061 rare diseases tracked by GARD until ≥50 abstracts had been returned for each disease or the EBI RESTful API results were exhausted. Though we called ~25,000 abstracts from PubMed's db, only 7699 unique abstracts were returned for 488 diseases. Out of 7699 abstracts, only 620 were classified as epidemiological by the LSTM RNN epidemiological classifier. ### Annotations #### Annotation process Programmatic labeling. See [here](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/create_labeled_dataset_V2.ipynb) and then [here](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/modify_existing_labels.ipynb). The test set was manually corrected after creation. #### Who are the annotators? Programmatic labeling was done by [@William Kariampuzha](https://github.com/wzkariampuzha), one of the NCATS researchers. The test set was manually corrected by 2 more NCATS researchers and a GARD curator (genetic and rare disease expert). ### Personal and Sensitive Information None. These are freely available abstracts from PubMed. ## Considerations for Using the Data ### Social Impact of Dataset Assisting 25-30 millions Americans with rare diseases. Additionally can be useful for Orphanet or CDC researchers/curators. ### Discussion of Biases and Limitations - There were errors in the source file that contained rare disease synonyms of names, which may have led to some unrelated abstracts being included in the training, validation, and test sets. - The abstracts were gathered through the EBI API and is thus subject to any biases that the EBI API had. The NCBI API returns very different results as shown by an API analysis here. - The [long short-term memory recurrent neural network epi classifier](https://pubmed.ncbi.nlm.nih.gov/34457147/) was used to sift the 7699 rare disease abstracts. This model had a hold-out validation F1 score of 0.886 and a test F1 (which was compared against a GARD curator who used full-text articles to determine truth-value of epidemiological abstract) of 0.701. With 620 epi abstracts filtered from 7699 original rare disease abstracts, there are likely several false positives and false negative epi abstracts. - Tokenization was done by spaCy which may be a limitation (or not) for current and future models trained on this set. - The programmatic labeling was very imprecise as seen by Table 1. This is likely the largest limitation of the [BioBERT-based model](https://huggingface.co/ncats/EpiExtract4GARD) trained on this set. - The test set was difficult to validate even for general NCATS researchers, which is why we relied on a rare disease expert to verify our modifications. As this task of epidemiological information identification is quite difficult for non-expert humans to complete, this set, and especially a gold-standard dataset in the possible future, represents a challenging gauntlet for NLP systems, especially those focusing on numeracy, to compete on. ## Additional Information ### Dataset Curators [NIH GARD](https://rarediseases.info.nih.gov/about-gard/pages/23/about-gard) ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@William Kariampuzha](https://github.com/wzkariampuzha) at NCATS/Axle Informatics for adding this dataset.

ryan0712/ultra_no_robots

--- license: unknown ---

cmagganas/zero_shot_comparison

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: rationale dtype: string - name: task dtype: string - name: type dtype: string - name: decilm_generation dtype: string - name: mistral_generation dtype: string - name: mpt_generation dtype: string splits: - name: train num_bytes: 96015 num_examples: 30 download_size: 59751 dataset_size: 96015 configs: - config_name: default data_files: - split: train path: data/train-* ---

AnudeepPeela/starcoder-finetune

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: Input dtype: string - name: Output dtype: string splits: - name: train num_bytes: 892 num_examples: 8 - name: test num_bytes: 3328 num_examples: 20 download_size: 2251 dataset_size: 4220 --- # Dataset Card for "starcoder-finetune" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

abhi28577/nennepedia

--- license: openrail task_categories: - question-answering language: - en pretty_name: nennepedia size_categories: - n<1K --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### 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]

shi3z/anthropic_hh_rlhf_japanese

--- license: mit --- https://huggingface.co/datasets/Anthropic/hh-rlhf Japanese Translation

7eu7d7/HCP-Diffusion-datas

--- license: apache-2.0 --- Anime prompt dataset (动漫风格数据集): + danbooru-160000.parquet Natural scenes prompt dataset (真实风格数据集): + stable-diffusion-prompts-160000.parquet + stable-diffusion-prompts2-320000.parquet Artistic style dataset (艺术风格数据集): + Lexica.art.parquet

blinoff/restaurants_reviews

--- language: - ru multilinguality: - monolingual size_categories: - 10K<n<100K task_categories: - text-classification task_ids: - sentiment-classification --- ### Dataset Summary The dataset contains user reviews about restaurants. In total it contains 47,139 reviews. A review tagged with the <em>general</em> sentiment and sentiments on 3 aspects: <em>food, interior, service</em>. ### Data Fields Each sample contains the following fields: - **review_id**; - **general**; - **food**; - **interior**; - **service**; - **text** review text. ### Python ```python3 import pandas as pd df = pd.read_json('restaurants_reviews.jsonl', lines=True) df.sample(5) ```

cassanof/italian-conversations

--- dataset_info: features: - name: content dtype: string splits: - name: train num_bytes: 223555130.20512977 num_examples: 115237 download_size: 113639002 dataset_size: 223555130.20512977 configs: - config_name: default data_files: - split: train path: data/train-* ---

autoevaluate/autoeval-staging-eval-project-e438add5-1e56-41ec-9c26-2ad4182383b0-6260

--- type: predictions tags: - autotrain - evaluation datasets: - autoevaluate/squad-sample eval_info: task: extractive_question_answering model: autoevaluate/extractive-question-answering metrics: [] dataset_name: autoevaluate/squad-sample dataset_config: autoevaluate--squad-sample dataset_split: test col_mapping: context: context question: question answers-text: answers.text answers-answer_start: answers.answer_start --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Question Answering * Model: autoevaluate/extractive-question-answering * Dataset: autoevaluate/squad-sample * Config: autoevaluate--squad-sample * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

elenahuang/primary-sector-top-1k

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 8620437 num_examples: 1000 download_size: 4571154 dataset_size: 8620437 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "primary-sector-top-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

llm-aes/doc-storygen-v2

--- dataset_info: features: - name: worker_id dtype: string - name: task_id dtype: string - name: task_response_id dtype: string - name: id dtype: int64 - name: premise dtype: string - name: plan1 dtype: string - name: plan2 dtype: string - name: Q1 dtype: string - name: Q2 dtype: string - name: Q3 dtype: string - name: Q4 dtype: string - name: Q5 dtype: string - name: Q6 dtype: string splits: - name: train num_bytes: 60995214 num_examples: 7000 download_size: 28333525 dataset_size: 60995214 configs: - config_name: default data_files: - split: train path: data/train-* ---

confit/esc50-csv

--- dataset_info: - config_name: fold-1 features: - name: filename dtype: string - name: label dtype: class_label: names: '0': dog '1': rooster '2': pig '3': cow '4': frog '5': cat '6': hen '7': insects '8': sheep '9': crow '10': rain '11': sea_waves '12': crackling_fire '13': crickets '14': chirping_birds '15': water_drops '16': wind '17': pouring_water '18': toilet_flush '19': thunderstorm '20': crying_baby '21': sneezing '22': clapping '23': breathing '24': coughing '25': footsteps '26': laughing '27': brushing_teeth '28': snoring '29': drinking_sipping '30': door_wood_knock '31': mouse_click '32': keyboard_typing '33': door_wood_creaks '34': can_opening '35': washing_machine '36': vacuum_cleaner '37': clock_alarm '38': clock_tick '39': glass_breaking '40': helicopter '41': chainsaw '42': siren '43': car_horn '44': engine '45': train '46': church_bells '47': airplane '48': fireworks '49': hand_saw splits: - name: train num_bytes: 11167 num_examples: 400 download_size: 7653 dataset_size: 11167 - config_name: fold-2 features: - name: filename dtype: string - name: label dtype: class_label: names: '0': dog '1': rooster '2': pig '3': cow '4': frog '5': cat '6': hen '7': insects '8': sheep '9': crow '10': rain '11': sea_waves '12': crackling_fire '13': crickets '14': chirping_birds '15': water_drops '16': wind '17': pouring_water '18': toilet_flush '19': thunderstorm '20': crying_baby '21': sneezing '22': clapping '23': breathing '24': coughing '25': footsteps '26': laughing '27': brushing_teeth '28': snoring '29': drinking_sipping '30': door_wood_knock '31': mouse_click '32': keyboard_typing '33': door_wood_creaks '34': can_opening '35': washing_machine '36': vacuum_cleaner '37': clock_alarm '38': clock_tick '39': glass_breaking '40': helicopter '41': chainsaw '42': siren '43': car_horn '44': engine '45': train '46': church_bells '47': airplane '48': fireworks '49': hand_saw splits: - name: train num_bytes: 11335 num_examples: 400 download_size: 7627 dataset_size: 11335 - config_name: fold-3 features: - name: filename dtype: string - name: label dtype: class_label: names: '0': dog '1': rooster '2': pig '3': cow '4': frog '5': cat '6': hen '7': insects '8': sheep '9': crow '10': rain '11': sea_waves '12': crackling_fire '13': crickets '14': chirping_birds '15': water_drops '16': wind '17': pouring_water '18': toilet_flush '19': thunderstorm '20': crying_baby '21': sneezing '22': clapping '23': breathing '24': coughing '25': footsteps '26': laughing '27': brushing_teeth '28': snoring '29': drinking_sipping '30': door_wood_knock '31': mouse_click '32': keyboard_typing '33': door_wood_creaks '34': can_opening '35': washing_machine '36': vacuum_cleaner '37': clock_alarm '38': clock_tick '39': glass_breaking '40': helicopter '41': chainsaw '42': siren '43': car_horn '44': engine '45': train '46': church_bells '47': airplane '48': fireworks '49': hand_saw splits: - name: train num_bytes: 11480 num_examples: 400 download_size: 7594 dataset_size: 11480 - config_name: fold-4 features: - name: filename dtype: string - name: label dtype: class_label: names: '0': dog '1': rooster '2': pig '3': cow '4': frog '5': cat '6': hen '7': insects '8': sheep '9': crow '10': rain '11': sea_waves '12': crackling_fire '13': crickets '14': chirping_birds '15': water_drops '16': wind '17': pouring_water '18': toilet_flush '19': thunderstorm '20': crying_baby '21': sneezing '22': clapping '23': breathing '24': coughing '25': footsteps '26': laughing '27': brushing_teeth '28': snoring '29': drinking_sipping '30': door_wood_knock '31': mouse_click '32': keyboard_typing '33': door_wood_creaks '34': can_opening '35': washing_machine '36': vacuum_cleaner '37': clock_alarm '38': clock_tick '39': glass_breaking '40': helicopter '41': chainsaw '42': siren '43': car_horn '44': engine '45': train '46': church_bells '47': airplane '48': fireworks '49': hand_saw splits: - name: train num_bytes: 11508 num_examples: 400 download_size: 7602 dataset_size: 11508 - config_name: fold-5 features: - name: filename dtype: string - name: label dtype: class_label: names: '0': dog '1': rooster '2': pig '3': cow '4': frog '5': cat '6': hen '7': insects '8': sheep '9': crow '10': rain '11': sea_waves '12': crackling_fire '13': crickets '14': chirping_birds '15': water_drops '16': wind '17': pouring_water '18': toilet_flush '19': thunderstorm '20': crying_baby '21': sneezing '22': clapping '23': breathing '24': coughing '25': footsteps '26': laughing '27': brushing_teeth '28': snoring '29': drinking_sipping '30': door_wood_knock '31': mouse_click '32': keyboard_typing '33': door_wood_creaks '34': can_opening '35': washing_machine '36': vacuum_cleaner '37': clock_alarm '38': clock_tick '39': glass_breaking '40': helicopter '41': chainsaw '42': siren '43': car_horn '44': engine '45': train '46': church_bells '47': airplane '48': fireworks '49': hand_saw splits: - name: train num_bytes: 11516 num_examples: 400 download_size: 7644 dataset_size: 11516 configs: - config_name: fold-1 data_files: - split: train path: fold-1/train-* - config_name: fold-2 data_files: - split: train path: fold-2/train-* - config_name: fold-3 data_files: - split: train path: fold-3/train-* - config_name: fold-4 data_files: - split: train path: fold-4/train-* - config_name: fold-5 data_files: - split: train path: fold-5/train-* ---

eengel7/sentiment_analysis_training

--- license: apache-2.0 ---

nyunai/samvaad-hi-v1-tulu-format

--- dataset_info: features: - name: messages list: - name: content dtype: string - name: role dtype: string - name: text dtype: string splits: - name: train num_bytes: 936264338 num_examples: 101476 download_size: 403495470 dataset_size: 936264338 configs: - config_name: default data_files: - split: train path: data/train-* ---

laion/laion2B-en-safety

Invalid username or password.

nblinh63/twitter_dataset_1712693741

--- 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: 79823 num_examples: 200 download_size: 37919 dataset_size: 79823 configs: - config_name: default data_files: - split: train path: data/train-* ---

medmac01/OpenHermes-2-AR

--- dataset_info: features: - name: topic dtype: 'null' - name: conversations dtype: string - name: skip_prompt_formatting dtype: bool - name: model dtype: 'null' - name: avatarUrl dtype: 'null' - name: custom_instruction dtype: 'null' - name: views dtype: float64 - name: hash dtype: 'null' - name: language dtype: 'null' - name: idx dtype: 'null' - name: model_name dtype: 'null' - name: system_prompt dtype: 'null' - name: title dtype: 'null' - name: source dtype: string - name: id dtype: 'null' - name: category dtype: string splits: - name: train num_bytes: 1907745 num_examples: 1001 download_size: 968864 dataset_size: 1907745 configs: - config_name: default data_files: - split: train path: data/train-* ---

bigscience-data/roots_id_wikimedia

--- language: id license: cc-by-sa-3.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_id_wikimedia # wikimedia_filtered - Dataset uid: `wikimedia_filtered` ### Description ### Homepage ### Licensing ### Speaker Locations ### Sizes - 0.0005 % of total - 0.0835 % of id - 0.0126 % of ca - 0.0054 % of pt - 0.0005 % of indic-hi ### BigScience processing steps #### Filters applied to: id - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_id - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: ca - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_ca - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: pt - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_pt - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: indic-hi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_indic-hi - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300

phanvancongthanh/data_part03

--- dataset_info: features: - name: smiles dtype: string splits: - name: train num_bytes: 4911356191 num_examples: 109915148 download_size: 2471976257 dataset_size: 4911356191 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "data_part03" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

plgfro/Kaggles-Galaxy-Zoo-Dataset

--- license: apache-2.0 ---

tyzhu/squad_qa_num_v5_full_random_permute_4

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 6510358.253911806 num_examples: 4345 - name: validation num_bytes: 343184 num_examples: 300 download_size: 1336925 dataset_size: 6853542.253911806 --- # Dataset Card for "squad_qa_num_v5_full_random_permute_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

TingChen-ppmc/Changsha_Dialect_Conversational_Speech_Corpus

--- dataset_info: features: - name: audio dtype: audio - name: gender dtype: string - name: speaker_id dtype: string - name: transcription dtype: string splits: - name: train num_bytes: 223664136.256 num_examples: 1488 download_size: 215320750 dataset_size: 223664136.256 configs: - config_name: default data_files: - split: train path: data/train-* --- # Corpus This dataset is built from Magicdata [ASR-CCHSHDIACSC: A CHINESE CHANGSHA DIALECT CONVERSATIONAL SPEECH CORPUS](https://magichub.com/datasets/changsha-dialect-conversational-speech-corpus/) This corpus is licensed under a [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](http://creativecommons.org/licenses/by-nc-nd/4.0/). Please refer to the license for further information. Modifications: The audio is split in sentences based on the time span on the transcription file. Sentences that span less than 1 second is discarded. Topics of conversation is removed. # Usage To load this dataset, use ```python from datasets import load_dataset dialect_corpus = load_dataset("TingChen-ppmc/Changsha_Dialect_Conversational_Speech_Corpus") ``` This dataset only has train split. To split out a test split, use ```python from datasets import load_dataset train_split = load_dataset("TingChen-ppmc/Changsha_Dialect_Conversational_Speech_Corpus", split="train") # where test=0.5 denotes 0.5 of the dataset will be split to test split corpus = train_split.train_test_split(test=0.5) ``` A sample data would be ```python # note this data is from the Nanchang Dialect corpus, the data format is shared {'audio': {'path': 'A0001_S001_0_G0001_0.WAV', 'array': array([-0.00030518, -0.00039673, -0.00036621, ..., -0.00064087, -0.00015259, -0.00042725]), 'sampling_rate': 16000}, 'gender': '女', 'speaker_id': 'G0001', 'transcription': '北京爱数智慧语音采集' } ``` [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

freshpearYoon/vr_train_free_11

--- 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: 6683391354 num_examples: 10000 download_size: 1198445542 dataset_size: 6683391354 configs: - config_name: default data_files: - split: train path: data/train-* ---

Rewcifer/best_outputs_3models

--- dataset_info: features: - name: true_findings dtype: string - name: generated_texts_1 dtype: string - name: generated_texts_2 dtype: string - name: generated_texts_3 dtype: string splits: - name: train num_bytes: 1861317 num_examples: 861 download_size: 799511 dataset_size: 1861317 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "best_outputs_3models" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kk2491/int_dataset

--- license: apache-2.0 ---

bigcode/the-stack-metadata

--- annotations_creators: [] language_creators: - crowdsourced - expert-generated language: - code license: - other multilinguality: - multilingual pretty_name: The-Stack-Metadata size_categories: - unknown source_datasets: [] task_categories: - text-generation task_ids: [] extra_gated_prompt: |- ## Terms of Use for The Stack The Stack Metadata is a collection of additional information for and is part of The Stack dataset, - a collection of source code in over 300 programming languages. We ask that you read and acknowledge the following points before using the dataset: 1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point. 2. The Stack is regularly updated to enact validated data removal requests. By clicking on "Access repository", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset’s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes. 3. To host, share, or otherwise provide access to The Stack dataset, you must include [these Terms of Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) and require users to agree to it. By clicking on "Access repository" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well. extra_gated_fields: Email: text I have read the License and agree with its terms: checkbox --- # Dataset Card for The Stack Metadata ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Changelog](#changelog) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Usage Example](#usage-example) - [Dataset Creation](#dataset-creation) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Additional Information](#additional-information) - [Terms of Use for The Stack](#terms-of-use-for-the-stack) ## Dataset Description - **Homepage:** https://www.bigcode-project.org/ - **Repository:** https://github.com/bigcode-project - **Paper:** https://arxiv.org/abs/2211.15533 - **Leaderboard:** N/A - **Point of Contact:** contact@bigcode-project.org ### Changelog |Release|Description| |-|-| |v1.1| This is the first release of the metadata. It is for The Stack v1.1| |v1.2| Metadata dataset matching The Stack v1.2| ### Dataset Summary This is a set of additional information for repositories used for The Stack. It contains file paths, detected licenes as well as some other information for the repositories. ### Supported Tasks and Leaderboards The main task is to recreate repository structure from the files of The Stack. Also, the set can be used for computing statistics and custom filtering or aggregation operations on The Stack. ## Dataset Structure ### Data Fields ![set structure](images/structure.png) The set is split into buckets by repositories. There are 944 buckets. Additionally to the fields in the image, `ri` contains `min_repo_event_datetime` which is the ealiest date and time of an event for a repo after Jan 1 2015. ![set usage](images/usage.png) As an example of an aggregation operation on The Stack, the image above shows conceptually a selection of stars ( and issues and PR count) for a file. Each unique file can be part of multiple repositories. So, The Stack releases unique files and aggregates meta information (e.g stars) from all repositories it belongs to. For example, for max_stars_count we take the maximum number of stars from all repositories the file is part of. The meta data will allow you to reconstruct repository directory structures. For this, for each repository form `ri` tabele it is needed to take all its files from `fi` table, find them in The Stack by file's `hexsha` and save those files' content under its path for a repository from `fi` table. For speed it is preferable to index The Stack by hexsha first. ### Usage Example Restore folder structure for python files in numpy repository ```python import datasets from pathlib import Path from tqdm.auto import tqdm import pandas as pd # assuming metadata is cloned into the local folder /data/hf_repos/the-stack-metadata # the stack is cloned into the local folder /data/hf_repos/the-stack-v1.1 # destination folder is in /repo_workdir/numpy_restored the_stack_meta_path = Path('/data/hf_repos/the-stack-metadata') the_stack_path = Path('/data/hf_repos/the-stack-v1.1') repo_dst_root = Path('/repo_workdir/numpy_restored') repo_name = 'numpy/numpy' # Get bucket with numpy repo info # meta_bucket_path = None #for fn in tqdm(list((the_stack_meta_path/'data').glob('*/ri.parquet'))): # df = pd.read_parquet(fn) # if any(df['name'] == repo_name): # meta_bucket_path = fn # break meta_bucket_path = the_stack_meta_path / 'data/255_944' # Get repository id from repo name ri_id = pd.read_parquet( meta_bucket_path / 'ri.parquet' ).query( f'`name` == "{repo_name}"' )['id'].to_list()[0] # Get files information for the reopository files_info = pd.read_parquet( meta_bucket_path / 'fi.parquet' ).query( f'`ri_id` == {ri_id} and `size` != 0 and `is_deleted` == False' ) # Convert DF with files information to a dictionary by language and then file hexsha # there can be more than one file with the same hexsha in the repo so we gather # all instances per unique hexsha files_info_dict = { k: v[['hexsha', 'path']].groupby('hexsha').apply(lambda x: list(x['path'])).to_dict() for k, v in files_info.groupby('lang_ex') } # Load Python part of The Stack ds = datasets.load_dataset( str(the_stack_path/'data/python'), num_proc=10, ignore_verifications=True ) # Save file content of the python files in the numpy reposirotry in their appropriate locations def save_file_content(example, files_info_dict, repo_dst_root): if example['hexsha'] in files_info_dict: for el in files_info_dict[example['hexsha']]: path = repo_dst_root / el path.parent.mkdir(parents=True, exist_ok=True) path.write_text(example['content']) ds.map( save_file_content, fn_kwargs={'files_info_dict': files_info_dict['Python'], 'repo_dst_root': repo_dst_root}, num_proc=10 ) ``` ## Dataset Creation Please refer to [the section](https://huggingface.co/datasets/bigcode/the-stack#dataset-creation) in The Stack. ## Considerations for Using the Data Please refer to [the section](https://huggingface.co/datasets/bigcode/the-stack#considerations-for-using-the-data) in The Stack. ## Additional Information Please refer to [the section](https://huggingface.co/datasets/bigcode/the-stack#additional-information) in The Stack. ## Terms of Use for The Stack Please refer to [the section](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) in The Stack.

davanstrien/WikiMuTe

--- configs: - config_name: default data_files: - split: train path: all.csv - config_name: self-filtering data_files: - split: train path: filtered_sf.csv - config_name: MusicCaps-filtering data_files: - split: train path: filtered_mc.csv license: cc-by-sa-3.0 language: - en ---

open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3

--- pretty_name: Evaluation run of Weyaxi/Luban-Marcoroni-13B-v3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Weyaxi/Luban-Marcoroni-13B-v3](https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3)\ \ 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_Weyaxi__Luban-Marcoroni-13B-v3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-29T14:08:44.787529](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3/blob/main/results_2023-10-29T14-08-44.787529.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\": 0.00776006711409396,\n\ \ \"em_stderr\": 0.0008986296432392762,\n \"f1\": 0.10252936241610805,\n\ \ \"f1_stderr\": 0.0019829740048614144,\n \"acc\": 0.4340313659926291,\n\ \ \"acc_stderr\": 0.010044205768767243\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.00776006711409396,\n \"em_stderr\": 0.0008986296432392762,\n\ \ \"f1\": 0.10252936241610805,\n \"f1_stderr\": 0.0019829740048614144\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09931766489764973,\n \ \ \"acc_stderr\": 0.008238371412683977\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.01185004012485051\n\ \ }\n}\n```" repo_url: https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3 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_13T22_12_25.570871 path: - '**/details_harness|arc:challenge|25_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-13T22-12-25.570871.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_29T14_08_44.787529 path: - '**/details_harness|drop|3_2023-10-29T14-08-44.787529.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-29T14-08-44.787529.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_29T14_08_44.787529 path: - '**/details_harness|gsm8k|5_2023-10-29T14-08-44.787529.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-29T14-08-44.787529.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hellaswag|10_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-13T22-12-25.570871.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-management|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-13T22-12-25.570871.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_13T22_12_25.570871 path: - '**/details_harness|truthfulqa:mc|0_2023-09-13T22-12-25.570871.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-13T22-12-25.570871.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_29T14_08_44.787529 path: - '**/details_harness|winogrande|5_2023-10-29T14-08-44.787529.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-29T14-08-44.787529.parquet' - config_name: results data_files: - split: 2023_09_13T22_12_25.570871 path: - results_2023-09-13T22-12-25.570871.parquet - split: 2023_10_29T14_08_44.787529 path: - results_2023-10-29T14-08-44.787529.parquet - split: latest path: - results_2023-10-29T14-08-44.787529.parquet --- # Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-13B-v3 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3 - **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 [Weyaxi/Luban-Marcoroni-13B-v3](https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3) 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_Weyaxi__Luban-Marcoroni-13B-v3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-29T14:08:44.787529](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3/blob/main/results_2023-10-29T14-08-44.787529.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": 0.00776006711409396, "em_stderr": 0.0008986296432392762, "f1": 0.10252936241610805, "f1_stderr": 0.0019829740048614144, "acc": 0.4340313659926291, "acc_stderr": 0.010044205768767243 }, "harness|drop|3": { "em": 0.00776006711409396, "em_stderr": 0.0008986296432392762, "f1": 0.10252936241610805, "f1_stderr": 0.0019829740048614144 }, "harness|gsm8k|5": { "acc": 0.09931766489764973, "acc_stderr": 0.008238371412683977 }, "harness|winogrande|5": { "acc": 0.7687450670876085, "acc_stderr": 0.01185004012485051 } } ``` ### 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]

lener_br

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - pt license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: lener-br pretty_name: leNER-br dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-ORGANIZACAO '2': I-ORGANIZACAO '3': B-PESSOA '4': I-PESSOA '5': B-TEMPO '6': I-TEMPO '7': B-LOCAL '8': I-LOCAL '9': B-LEGISLACAO '10': I-LEGISLACAO '11': B-JURISPRUDENCIA '12': I-JURISPRUDENCIA config_name: lener_br splits: - name: train num_bytes: 3984189 num_examples: 7828 - name: validation num_bytes: 719433 num_examples: 1177 - name: test num_bytes: 823708 num_examples: 1390 download_size: 2983137 dataset_size: 5527330 tags: - legal --- # Dataset Card for leNER-br ## 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:** [leNER-BR homepage](https://cic.unb.br/~teodecampos/LeNER-Br/) - **Repository:** [leNER-BR repository](https://github.com/peluz/lener-br) - **Paper:** [leNER-BR: Long Form Question Answering](https://cic.unb.br/~teodecampos/LeNER-Br/luz_etal_propor2018.pdf) - **Point of Contact:** [Pedro H. Luz de Araujo](mailto:pedrohluzaraujo@gmail.com) ### Dataset Summary LeNER-Br is a Portuguese language dataset for named entity recognition applied to legal documents. LeNER-Br consists entirely of manually annotated legislation and legal cases texts and contains tags for persons, locations, time entities, organizations, legislation and legal cases. To compose the dataset, 66 legal documents from several Brazilian Courts were collected. Courts of superior and state levels were considered, such as Supremo Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas Gerais and Tribunal de Contas da União. In addition, four legislation documents were collected, such as "Lei Maria da Penha", giving a total of 70 documents ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Portuguese. ## Dataset Structure ### Data Instances An example from the dataset looks as follows: ``` { "id": "0", "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0], "tokens": [ "EMENTA", ":", "APELAÇÃO", "CÍVEL", "-", "AÇÃO", "DE", "INDENIZAÇÃO", "POR", "DANOS", "MORAIS", "-", "PRELIMINAR", "-", "ARGUIDA", "PELO", "MINISTÉRIO", "PÚBLICO", "EM", "GRAU", "RECURSAL"] } ``` ### Data Fields - `id`: id of the sample - `tokens`: the tokens of the example text - `ner_tags`: the NER tags of each token The NER tags correspond to this list: ``` "O", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-PESSOA", "I-PESSOA", "B-TEMPO", "I-TEMPO", "B-LOCAL", "I-LOCAL", "B-LEGISLACAO", "I-LEGISLACAO", "B-JURISPRUDENCIA", "I-JURISPRUDENCIA" ``` The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: | Train | Val | Test | | ------ | ----- | ---- | | 7828 | 1177 | 1390 | ## 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 ``` @inproceedings{luz_etal_propor2018, author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and Renato R. R. {de Oliveira} and Matheus Stauffer and Samuel Couto and Paulo Bermejo}, title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text}, booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})}, publisher = {Springer}, series = {Lecture Notes on Computer Science ({LNCS})}, pages = {313--323}, year = {2018}, month = {September 24-26}, address = {Canela, RS, Brazil}, doi = {10.1007/978-3-319-99722-3_32}, url = {https://cic.unb.br/~teodecampos/LeNER-Br/}, } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.

natmin322/28k_vietnamese_voice_augmented_of_VigBigData

--- configs: - config_name: default data_files: - split: train_1 path: data/train_1-* - split: train_2 path: data/train_2-* - split: train_3 path: data/train_3-* - split: train_4 path: data/train_4-* - split: train_5 path: data/train_5-* - split: test path: data/test-* dataset_info: features: - name: audio dtype: audio - name: sentence dtype: string splits: - name: train_1 num_bytes: 1433691842.0 num_examples: 5000 - name: train_2 num_bytes: 1026073200.0 num_examples: 5000 - name: train_3 num_bytes: 1113535830.0 num_examples: 5000 - name: train_4 num_bytes: 1489647293.0 num_examples: 5000 - name: train_5 num_bytes: 1416405046.0 num_examples: 5000 - name: test num_bytes: 886300388.18 num_examples: 3005 download_size: 6939675259 dataset_size: 7365653599.18 --- # Dataset Card for "28k_vietnamese_voice_augmented_of_VigBigData" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

one-sec-cv12/chunk_112

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 24829848720.625 num_examples: 258515 download_size: 22670676655 dataset_size: 24829848720.625 --- # Dataset Card for "chunk_112" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

result-muse256-muse512-wuerst-sdv15/909d509a

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 221 num_examples: 10 download_size: 1393 dataset_size: 221 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "909d509a" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mHossain/merge_new_para_detection_data_v5

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 8837101.8 num_examples: 50400 - name: test num_bytes: 981900.2 num_examples: 5600 download_size: 4451360 dataset_size: 9819002.0 --- # Dataset Card for "merge_new_para_detection_data_v5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

xrizs/test.v83i.coco-segmentation

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* dataset_info: features: - name: image dtype: image - name: annotation dtype: image splits: - name: train num_bytes: 815324785.5 num_examples: 1814 - name: val num_bytes: 205298969.0 num_examples: 453 download_size: 1020036030 dataset_size: 1020623754.5 --- # Dataset Card for "test.v83i.coco-segmentation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Elicke/Epicdarkguy

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 793929.0 num_examples: 1 download_size: 776077 dataset_size: 793929.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

kgr123/quality_counter_3000

--- dataset_info: features: - name: context dtype: string - name: word dtype: string - name: claim dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 16636623 num_examples: 1929 - name: train num_bytes: 16474873 num_examples: 1935 - name: validation num_bytes: 16809536 num_examples: 1941 download_size: 11133065 dataset_size: 49921032 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* ---

TinyPixel/based_0

--- dataset_info: features: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 53106 num_examples: 352 download_size: 0 dataset_size: 53106 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "wizard" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_saucam__mistral-orpo-beta-NeuralBeagle14-7B-dare-ties

--- pretty_name: Evaluation run of saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties](https://huggingface.co/saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties)\ \ 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_saucam__mistral-orpo-beta-NeuralBeagle14-7B-dare-ties\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T11:57:13.922311](https://huggingface.co/datasets/open-llm-leaderboard/details_saucam__mistral-orpo-beta-NeuralBeagle14-7B-dare-ties/blob/main/results_2024-03-21T11-57-13.922311.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\": 0.64885588006924,\n\ \ \"acc_stderr\": 0.03206109245911583,\n \"acc_norm\": 0.6502812247567149,\n\ \ \"acc_norm_stderr\": 0.03270979476504535,\n \"mc1\": 0.3659730722154223,\n\ \ \"mc1_stderr\": 0.016862941684088376,\n \"mc2\": 0.5386604944982498,\n\ \ \"mc2_stderr\": 0.014994373950114138\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6262798634812287,\n \"acc_stderr\": 0.014137708601759088,\n\ \ \"acc_norm\": 0.6672354948805461,\n \"acc_norm_stderr\": 0.013769863046192307\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6683927504481179,\n\ \ \"acc_stderr\": 0.004698285350019214,\n \"acc_norm\": 0.859788886675961,\n\ \ \"acc_norm_stderr\": 0.003464963379379926\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621503\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n\ \ \"acc_stderr\": 0.04115324610336953,\n \"acc_norm\": 0.6518518518518519,\n\ \ \"acc_norm_stderr\": 0.04115324610336953\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.038234289699266046,\n\ \ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.038234289699266046\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n\ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6936416184971098,\n\ \ \"acc_stderr\": 0.03514942551267438,\n \"acc_norm\": 0.6936416184971098,\n\ \ \"acc_norm_stderr\": 0.03514942551267438\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.04897104952726366,\n\ \ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.04897104952726366\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\ \ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.025424835086924006,\n \"\ acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.025424835086924006\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n\ \ \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.4126984126984127,\n\ \ \"acc_norm_stderr\": 0.04403438954768176\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n\ \ \"acc_stderr\": 0.02328766512726855,\n \"acc_norm\": 0.7870967741935484,\n\ \ \"acc_norm_stderr\": 0.02328766512726855\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\ \ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\ \ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586818,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586818\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.02338193534812142,\n\ \ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.02338193534812142\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n\ \ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857413,\n \ \ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857413\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\ \ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\ acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8330275229357799,\n \"acc_stderr\": 0.01599015488507338,\n \"\ acc_norm\": 0.8330275229357799,\n \"acc_norm_stderr\": 0.01599015488507338\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"\ acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676166,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676166\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7130044843049327,\n\ \ \"acc_stderr\": 0.030360379710291954,\n \"acc_norm\": 0.7130044843049327,\n\ \ \"acc_norm_stderr\": 0.030360379710291954\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\ \ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\ acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8314176245210728,\n\ \ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\ \ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.02410571260775431,\n\ \ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.02410571260775431\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.34301675977653634,\n\ \ \"acc_stderr\": 0.015876912673057745,\n \"acc_norm\": 0.34301675977653634,\n\ \ \"acc_norm_stderr\": 0.015876912673057745\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\ \ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\ \ \"acc_stderr\": 0.025494259350694912,\n \"acc_norm\": 0.7202572347266881,\n\ \ \"acc_norm_stderr\": 0.025494259350694912\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7808641975308642,\n \"acc_stderr\": 0.023016705640262196,\n\ \ \"acc_norm\": 0.7808641975308642,\n \"acc_norm_stderr\": 0.023016705640262196\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4576271186440678,\n\ \ \"acc_stderr\": 0.01272429655098019,\n \"acc_norm\": 0.4576271186440678,\n\ \ \"acc_norm_stderr\": 0.01272429655098019\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.028582709753898452,\n\ \ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.028582709753898452\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.673202614379085,\n \"acc_stderr\": 0.01897542792050721,\n \ \ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.01897542792050721\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142773,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142773\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n\ \ \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n\ \ \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\ \ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\ \ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3659730722154223,\n\ \ \"mc1_stderr\": 0.016862941684088376,\n \"mc2\": 0.5386604944982498,\n\ \ \"mc2_stderr\": 0.014994373950114138\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8121546961325967,\n \"acc_stderr\": 0.010977481103435091\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6338134950720242,\n \ \ \"acc_stderr\": 0.013270100238748835\n }\n}\n```" repo_url: https://huggingface.co/saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties 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_21T11_57_13.922311 path: - '**/details_harness|arc:challenge|25_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-21T11-57-13.922311.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|gsm8k|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hellaswag|10_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T11-57-13.922311.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T11-57-13.922311.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T11-57-13.922311.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T11_57_13.922311 path: - '**/details_harness|winogrande|5_2024-03-21T11-57-13.922311.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-21T11-57-13.922311.parquet' - config_name: results data_files: - split: 2024_03_21T11_57_13.922311 path: - results_2024-03-21T11-57-13.922311.parquet - split: latest path: - results_2024-03-21T11-57-13.922311.parquet --- # Dataset Card for Evaluation run of saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties  Dataset automatically created during the evaluation run of model [saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties](https://huggingface.co/saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties) 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_saucam__mistral-orpo-beta-NeuralBeagle14-7B-dare-ties", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T11:57:13.922311](https://huggingface.co/datasets/open-llm-leaderboard/details_saucam__mistral-orpo-beta-NeuralBeagle14-7B-dare-ties/blob/main/results_2024-03-21T11-57-13.922311.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": 0.64885588006924, "acc_stderr": 0.03206109245911583, "acc_norm": 0.6502812247567149, "acc_norm_stderr": 0.03270979476504535, "mc1": 0.3659730722154223, "mc1_stderr": 0.016862941684088376, "mc2": 0.5386604944982498, "mc2_stderr": 0.014994373950114138 }, "harness|arc:challenge|25": { "acc": 0.6262798634812287, "acc_stderr": 0.014137708601759088, "acc_norm": 0.6672354948805461, "acc_norm_stderr": 0.013769863046192307 }, "harness|hellaswag|10": { "acc": 0.6683927504481179, "acc_stderr": 0.004698285350019214, "acc_norm": 0.859788886675961, "acc_norm_stderr": 0.003464963379379926 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.04688261722621503, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621503 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6518518518518519, "acc_stderr": 0.04115324610336953, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.04115324610336953 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6710526315789473, "acc_stderr": 0.038234289699266046, "acc_norm": 0.6710526315789473, "acc_norm_stderr": 0.038234289699266046 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03476590104304134, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03476590104304134 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6936416184971098, "acc_stderr": 0.03514942551267438, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.03514942551267438 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.04897104952726366, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.04897104952726366 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.78, "acc_stderr": 0.04163331998932263, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932263 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5087719298245614, "acc_stderr": 0.04702880432049615, "acc_norm": 0.5087719298245614, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878152, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878152 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42063492063492064, "acc_stderr": 0.025424835086924006, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.025424835086924006 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4126984126984127, "acc_stderr": 0.04403438954768176, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.04403438954768176 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7870967741935484, "acc_stderr": 0.02328766512726855, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.02328766512726855 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.67, "acc_stderr": 0.04725815626252607, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252607 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586818, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586818 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8808290155440415, "acc_stderr": 0.02338193534812142, "acc_norm": 0.8808290155440415, "acc_norm_stderr": 0.02338193534812142 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657266, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657266 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.35555555555555557, "acc_stderr": 0.029185714949857413, "acc_norm": 0.35555555555555557, "acc_norm_stderr": 0.029185714949857413 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6848739495798319, "acc_stderr": 0.030176808288974337, "acc_norm": 0.6848739495798319, "acc_norm_stderr": 0.030176808288974337 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3576158940397351, "acc_stderr": 0.03913453431177258, "acc_norm": 0.3576158940397351, "acc_norm_stderr": 0.03913453431177258 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8330275229357799, "acc_stderr": 0.01599015488507338, "acc_norm": 0.8330275229357799, "acc_norm_stderr": 0.01599015488507338 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5185185185185185, "acc_stderr": 0.03407632093854051, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.03407632093854051 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8137254901960784, "acc_stderr": 0.027325470966716312, "acc_norm": 0.8137254901960784, "acc_norm_stderr": 0.027325470966716312 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676166, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676166 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7130044843049327, "acc_stderr": 0.030360379710291954, "acc_norm": 0.7130044843049327, "acc_norm_stderr": 0.030360379710291954 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.816793893129771, "acc_stderr": 0.03392770926494733, "acc_norm": 0.816793893129771, "acc_norm_stderr": 0.03392770926494733 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7407407407407407, "acc_stderr": 0.04236511258094633, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.04236511258094633 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness|hendrycksTest-management|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7225433526011561, "acc_stderr": 0.02410571260775431, "acc_norm": 0.7225433526011561, "acc_norm_stderr": 0.02410571260775431 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.34301675977653634, "acc_stderr": 0.015876912673057745, "acc_norm": 0.34301675977653634, "acc_norm_stderr": 0.015876912673057745 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292456, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292456 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7202572347266881, "acc_stderr": 0.025494259350694912, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.025494259350694912 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7808641975308642, "acc_stderr": 0.023016705640262196, "acc_norm": 0.7808641975308642, "acc_norm_stderr": 0.023016705640262196 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4576271186440678, "acc_stderr": 0.01272429655098019, "acc_norm": 0.4576271186440678, "acc_norm_stderr": 0.01272429655098019 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6691176470588235, "acc_stderr": 0.028582709753898452, "acc_norm": 0.6691176470588235, "acc_norm_stderr": 0.028582709753898452 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.673202614379085, "acc_stderr": 0.01897542792050721, "acc_norm": 0.673202614379085, "acc_norm_stderr": 0.01897542792050721 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7387755102040816, "acc_stderr": 0.028123429335142773, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142773 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8606965174129353, "acc_stderr": 0.024484487162913973, "acc_norm": 0.8606965174129353, "acc_norm_stderr": 0.024484487162913973 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-virology|5": { "acc": 0.5301204819277109, "acc_stderr": 0.03885425420866767, "acc_norm": 0.5301204819277109, "acc_norm_stderr": 0.03885425420866767 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640038, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640038 }, "harness|truthfulqa:mc|0": { "mc1": 0.3659730722154223, "mc1_stderr": 0.016862941684088376, "mc2": 0.5386604944982498, "mc2_stderr": 0.014994373950114138 }, "harness|winogrande|5": { "acc": 0.8121546961325967, "acc_stderr": 0.010977481103435091 }, "harness|gsm8k|5": { "acc": 0.6338134950720242, "acc_stderr": 0.013270100238748835 } } ``` ## 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]

tyzhu/find_last_sent_train_500_eval_20

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: title dtype: string - name: context dtype: string splits: - name: train num_bytes: 1144818 num_examples: 904 - name: validation num_bytes: 20896 num_examples: 20 download_size: 499234 dataset_size: 1165714 --- # Dataset Card for "find_last_sent_train_500_eval_20" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

veezbo/akkadian_english_corpus

--- license: mit task_categories: - text-generation language: - en pretty_name: English-translated Akkadian Corpus size_categories: - 1K<n<10K --- # Akkadian English Corpus This dataset is a cleaned English-translated Akkadian language dataset. This dataset can and has been used for text generation tasks, for example to fine-tune LLMs. ## How it was generated Please visit my [repo](https://github.com/veezbo/akkadian_english_corpus) on Github which explains the steps that were taken to prepare this dataset for a text generation task. At a high level, these are steps that were taken: - Sourced a high-quality dataset of English-translated Akkadian by experts - Enforced a minimum line length - Removed duplicate lines - Removed textual notes and other generic notes within parantheses - Inserted translation notes and literal notes in place (preserving grammar and adding clarity to the corpus) ## Credit Credit for the aggregation of the raw data belongs to the [Akkademia](https://github.com/gaigutherz/Akkademia/tree/master) project. Specifically, the exact data file used as the starting dataset is linked [here](https://github.com/gaigutherz/Akkademia/blob/master/NMT_input/train.en) and was also used to train their SOTA neural machine translation Akkadian->English model as described in their recent [paper](https://academic.oup.com/pnasnexus/article/2/5/pgad096/7147349) Gutherz et al. 2023 [1]. Credit for the original source of the raw data belongs to the incredible Open Richly Annotated Cuneiform Corpus ([ORACC](http://oracc.org)) project [2]. Specifically, as noted by the Akkademia project above, the RINAP 1, 3, 4, and 5 datasets are the source of the original raw data. ## Citations [1] Gai Gutherz, Shai Gordin, Luis Sáenz, Omer Levy, Jonathan Berant, Translating Akkadian to English with neural machine translation, PNAS Nexus, Volume 2, Issue 5, May 2023, pgad096, https://doi.org/10.1093/pnasnexus/pgad096 [2] Jamie Novotny, Eleanor Robson, Steve Tinney, Niek Veldhuis, et al. Open Richly Annotated Cuneiform Corpus, http://oracc.org

Penguin-N/github-issues

--- dataset_info: features: - name: id dtype: int64 - name: labels_url dtype: string - name: body dtype: string - name: updated_at dtype: string - name: number dtype: int64 - name: milestone struct: - name: closed_at dtype: 'null' - name: closed_issues dtype: int64 - name: created_at dtype: string - name: creator struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: description dtype: string - name: due_on dtype: 'null' - name: html_url dtype: string - name: id dtype: int64 - name: labels_url dtype: string - name: node_id dtype: string - name: number dtype: int64 - name: open_issues dtype: int64 - name: state dtype: string - name: title dtype: string - name: updated_at dtype: string - name: url dtype: string - name: repository_url dtype: string - name: draft dtype: bool - name: labels list: - name: color dtype: string - name: default dtype: bool - name: description dtype: string - name: id dtype: int64 - name: name dtype: string - name: node_id dtype: string - name: url dtype: string - name: created_at dtype: string - name: comments_url dtype: string - name: assignee struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: timeline_url dtype: string - name: title dtype: string - name: events_url dtype: string - name: active_lock_reason dtype: 'null' - name: user struct: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: assignees list: - name: avatar_url dtype: string - name: events_url dtype: string - name: followers_url dtype: string - name: following_url dtype: string - name: gists_url dtype: string - name: gravatar_id dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: login dtype: string - name: node_id dtype: string - name: organizations_url dtype: string - name: received_events_url dtype: string - name: repos_url dtype: string - name: site_admin dtype: bool - name: starred_url dtype: string - name: subscriptions_url dtype: string - name: type dtype: string - name: url dtype: string - name: performed_via_github_app dtype: 'null' - name: state_reason dtype: string - name: author_association dtype: string - name: closed_at dtype: string - name: pull_request struct: - name: diff_url dtype: string - name: html_url dtype: string - name: merged_at dtype: string - name: patch_url dtype: string - name: url dtype: string - name: node_id dtype: string - name: comments sequence: string - name: reactions struct: - name: '+1' dtype: int64 - name: '-1' dtype: int64 - name: confused dtype: int64 - name: eyes dtype: int64 - name: heart dtype: int64 - name: hooray dtype: int64 - name: laugh dtype: int64 - name: rocket dtype: int64 - name: total_count dtype: int64 - name: url dtype: string - name: state dtype: string - name: locked dtype: bool - name: url dtype: string - name: html_url dtype: string - name: is_pull_request dtype: bool splits: - name: train num_bytes: 23420904 num_examples: 3000 download_size: 6966686 dataset_size: 23420904 configs: - config_name: default data_files: - split: train path: data/train-* ---

Nexdata/47811_Sentences_Intention_Annotation_Data_in_Interactive_Scenes

--- license: cc-by-nc-nd-4.0 --- ## Description Intent-like single-sentence annotated textual data, the data size is 47811 sentences, annotated with intent classes, including slot and slot value information; the intent field includes music, weather, date, schedule, home equipment, etc.; it is applied to intent recognition research and related fields. For more details, please refer to the link: https://www.nexdata.ai/dataset/1085?source=Huggingface # Specifications ## Storage format Json ## Data content 47,811 sentences of 16 domains, and each sentence is marked with intention ## Annotation Manually write sentences for each intent type, and then mark the slot; ## Language Chinese ## Accuracy Rate 99% ## Application scenario intention understanding in speech interaction # Licensing Information Commercial License

juancopi81/test-audio

--- dataset_info: features: - name: audio dtype: audio - name: playlist_title dtype: string - name: url dtype: string - name: title dtype: string - name: description dtype: string splits: - name: train num_bytes: 1052419.0 num_examples: 3 download_size: 0 dataset_size: 1052419.0 --- # Dataset Card for "test-audio" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

liuyanchen1015/MULTI_VALUE_sst2_drop_aux_wh

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: score dtype: int64 splits: - name: dev num_bytes: 393 num_examples: 4 - name: test num_bytes: 600 num_examples: 6 - name: train num_bytes: 7762 num_examples: 62 download_size: 10707 dataset_size: 8755 --- # Dataset Card for "MULTI_VALUE_sst2_drop_aux_wh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/sakakibara_satomi_idolmastercinderellagirls

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sakakibara_satomi/榊原里美 (THE iDOLM@STER: Cinderella Girls) This is the dataset of sakakibara_satomi/榊原里美 (THE iDOLM@STER: Cinderella Girls), containing 71 images and their tags. The core tags of this character are `grey_hair, breasts, long_hair, large_breasts, purple_eyes, drill_hair, braid, twintails`, 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 | 71 | 46.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakakibara_satomi_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 71 | 38.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakakibara_satomi_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 134 | 67.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakakibara_satomi_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 71 | 44.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakakibara_satomi_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 134 | 76.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakakibara_satomi_idolmastercinderellagirls/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/sakakibara_satomi_idolmastercinderellagirls', 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 | 14 | ![](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, cleavage, open_mouth, necklace, hairband, looking_at_viewer, :d, blush, microphone | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, dress, necklace, blush, simple_background, twin_braids, looking_at_viewer, open_mouth, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | cleavage | open_mouth | necklace | hairband | looking_at_viewer | :d | blush | microphone | dress | simple_background | twin_braids | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:-------------|:-----------|:-----------|:--------------------|:-----|:--------|:-------------|:--------|:--------------------|:--------------|:-------------------| | 0 | 14 | ![](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 | | | | | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | X | X | | X | | X | | X | X | X | X |

manu/french_librispeech_text_only

--- dataset_info: features: - name: text dtype: string - name: id dtype: string splits: - name: train num_bytes: 62120933 num_examples: 258213 download_size: 37959942 dataset_size: 62120933 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "french_librispeech_text_only" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tyzhu/squad_qa_context_v5_full_recite_ans_sent_random_permute_rerun_4

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 4850217.0 num_examples: 2385 - name: validation num_bytes: 631113 num_examples: 300 download_size: 1204825 dataset_size: 5481330.0 --- # Dataset Card for "squad_qa_context_v5_full_recite_ans_sent_random_permute_rerun_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DopeorNope/new_instruct6

--- dataset_info: features: - name: input dtype: string - name: output dtype: string - name: instruction dtype: string - name: tag dtype: string splits: - name: train num_bytes: 525389527 num_examples: 127811 download_size: 259744648 dataset_size: 525389527 configs: - config_name: default data_files: - split: train path: data/train-* ---

Elfsong/Mercury

--- dataset_info: features: - name: slug_name dtype: string - name: meta_info struct: - name: data struct: - name: question struct: - name: categoryTitle dtype: string - name: content dtype: string - name: difficulty dtype: string - name: questionFrontendId dtype: string - name: questionId dtype: string - name: questionTitle dtype: string - name: questionTitleSlug dtype: string - name: similarQuestions dtype: string - name: stats dtype: string - name: topicTags list: - name: name dtype: string - name: slug dtype: string - name: id dtype: string - name: difficulty dtype: string - name: pretty_content sequence: string - name: solutions list: - name: hash dtype: int64 - name: runtime dtype: string - name: solution dtype: string - name: prompt dtype: string - name: generator_code dtype: string - name: convert_online dtype: string - name: convert_offline dtype: string - name: evaluate_offline dtype: string - name: entry_point dtype: string - name: test_cases dtype: string splits: - name: train num_bytes: 24879611 num_examples: 1633 - name: eval num_bytes: 7028101 num_examples: 256 download_size: 10526574 dataset_size: 31907712 configs: - config_name: default data_files: - split: train path: data/train-* - split: eval path: data/eval-* language: - en size_categories: - 1K<n<10K --- # Welcome to Mercury 🪐! ## It is a code efficiency benchmark. ## Please consider citing our paper: https://arxiv.org/abs/2402.07844

dchatca/vn-economic-articles-summary-remove-1

--- dataset_info: features: - name: Title dtype: string - name: Content dtype: string - name: Sum-Content dtype: string - name: text dtype: string splits: - name: train num_bytes: 12177542.253918495 num_examples: 1148 - name: test num_bytes: 1357774.7460815047 num_examples: 128 download_size: 6349365 dataset_size: 13535317.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

kamilakesbi/cv_for_spd_fr_augmented

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 48000 - name: speakers sequence: string - name: timestamps_start sequence: float64 - name: timestamps_end sequence: float64 splits: - name: train num_bytes: 174115378.0 num_examples: 120 - name: validation num_bytes: 39951832.0 num_examples: 24 - name: test num_bytes: 40056748.0 num_examples: 24 download_size: 236615435 dataset_size: 254123958.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

ademax/ocr_scan_vi

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 628707658.0172987 num_examples: 10000 - name: test num_bytes: 32755668.982701264 num_examples: 521 download_size: 660712028 dataset_size: 661463327.0 --- # Dataset Card for "ocr_scan_vi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/rudewell_emilia_maougakuinnofutekigousha

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Rudewell Emilia/エミリア・ルードウェル (Maou Gakuin no Futekigousha) This is the dataset of Rudewell Emilia/エミリア・ルードウェル (Maou Gakuin no Futekigousha), containing 36 images and their tags. The core tags of this character are `long_hair, purple_hair, purple_eyes, hair_between_eyes, ponytail, pink_eyes, asymmetrical_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 | 36 | 25.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rudewell_emilia_maougakuinnofutekigousha/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 36 | 25.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rudewell_emilia_maougakuinnofutekigousha/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 81 | 48.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rudewell_emilia_maougakuinnofutekigousha/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/rudewell_emilia_maougakuinnofutekigousha', 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 | 5 | ![](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, closed_mouth, solo, frown, looking_at_viewer, necklace, asymmetrical_bangs, portrait, upper_body | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | closed_mouth | solo | frown | looking_at_viewer | necklace | asymmetrical_bangs | portrait | upper_body | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:--------|:--------------------|:-----------|:---------------------|:-----------|:-------------| | 0 | 5 | ![](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 |

LexiconShiftInnovations/sinhala_facebook_stories

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 15882438 num_examples: 776 download_size: 6524881 dataset_size: 15882438 configs: - config_name: default data_files: - split: train path: data/train-* ---

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/56d4a1b5

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 184 num_examples: 10 download_size: 1335 dataset_size: 184 --- # Dataset Card for "56d4a1b5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

zeroman1318/daegu-ai-06

--- configs: - config_name: default data_files: - split: train path: data.csv --- # Dataset Card for Dataset Name  ## 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]

tyzhu/squad_qa_context_v5_full_recite_ans_sent_random_permute_rerun_8

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 4850217.0 num_examples: 2385 - name: validation num_bytes: 631113 num_examples: 300 download_size: 1204825 dataset_size: 5481330.0 --- # Dataset Card for "squad_qa_context_v5_full_recite_ans_sent_random_permute_rerun_8" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

chloecchng/biomedical_cpgQA

--- license: apache-2.0 task_categories: - question-answering language: - en tags: - biology - medical size_categories: - 1K<n<10K --- # Dataset Card for the Biomedical Domain ### Dataset Summary This dataset was obtain through github (https://github.com/mmahbub/cpgQA/blob/main/dataset/cpgQA-v1.0.csv?plain=1) to Huggin Face for easier access while fine tuning. ### Languages English (en) ## Dataset Structure The dataset is in a CSV format, with each row representing a single review. The following columns are included: * **Title:** Categorises the QA. * **Context:** Gives a context of the QA. * **Question:** The question asked. * **Answer:** The expected and appropriate answer to the question asked.

kiennguyen1703/DiroxCompany

--- license: apache-2.0 language: - en ---

magixxixx/shopee-product-reviews-on-computer-category

--- task_categories: - text-classification language: - tl - en tags: - shopee - reviews pretty_name: Shopee reviews on computer category, 20k positives and 20k negatives size_categories: - 10K<n<100K ---

CAiRE/prosocial-dialog-eng_Latn-bloomz-rlhf

--- dataset_info: features: - name: context dtype: string - name: response dtype: string - name: rots sequence: string - name: safety_label dtype: string - name: safety_annotations sequence: string - name: safety_annotation_reasons sequence: string - name: source dtype: string - name: etc dtype: string - name: dialogue_id dtype: int64 - name: response_id dtype: int64 - name: episode_done dtype: bool - name: agent_response dtype: string splits: - name: train num_bytes: 144916538 num_examples: 120236 - name: validation num_bytes: 29794902 num_examples: 20416 - name: test num_bytes: 36311173 num_examples: 25029 download_size: 107425838 dataset_size: 211022613 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

kaleemWaheed/twitter_dataset_1712994766

--- 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: 23267 num_examples: 51 download_size: 11096 dataset_size: 23267 configs: - config_name: default data_files: - split: train path: data/train-* ---

TinyPixel/lima-u

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1804087 num_examples: 780 download_size: 1044055 dataset_size: 1804087 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "lima-u" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

haoranxu/WMT22-Test

--- dataset_info: - config_name: cs-en features: - name: cs-en struct: - name: cs dtype: string - name: en dtype: string splits: - name: test num_bytes: 325040 num_examples: 1448 download_size: 224193 dataset_size: 325040 - config_name: de-en features: - name: de-en struct: - name: de dtype: string - name: en dtype: string splits: - name: test num_bytes: 403424 num_examples: 1984 download_size: 267107 dataset_size: 403424 - config_name: en-cs features: - name: en-cs struct: - name: cs dtype: string - name: en dtype: string splits: - name: test num_bytes: 422875 num_examples: 2037 download_size: 281086 dataset_size: 422875 - config_name: en-de features: - name: en-de struct: - name: de dtype: string - name: en dtype: string splits: - name: test num_bytes: 442576 num_examples: 2037 download_size: 280415 dataset_size: 442576 - config_name: en-is features: - name: en-is struct: - name: en dtype: string - name: is dtype: string splits: - name: test num_bytes: 310807 num_examples: 1000 download_size: 197437 dataset_size: 310807 - config_name: en-ru features: - name: en-ru struct: - name: en dtype: string - name: ru dtype: string splits: - name: test num_bytes: 598414 num_examples: 2037 download_size: 333784 dataset_size: 598414 - config_name: en-zh features: - name: en-zh struct: - name: en dtype: string - name: zh dtype: string splits: - name: test num_bytes: 383751 num_examples: 2037 download_size: 257805 dataset_size: 383751 - config_name: is-en features: - name: is-en struct: - name: en dtype: string - name: is dtype: string splits: - name: test num_bytes: 248029 num_examples: 1000 download_size: 152885 dataset_size: 248029 - config_name: ru-en features: - name: ru-en struct: - name: en dtype: string - name: ru dtype: string splits: - name: test num_bytes: 579656 num_examples: 2016 download_size: 340830 dataset_size: 579656 - config_name: zh-en features: - name: zh-en struct: - name: en dtype: string - name: zh dtype: string splits: - name: test num_bytes: 526074 num_examples: 1875 download_size: 333078 dataset_size: 526074 configs: - config_name: cs-en data_files: - split: test path: cs-en/test-* - config_name: de-en data_files: - split: test path: de-en/test-* - config_name: en-cs data_files: - split: test path: en-cs/test-* - config_name: en-de data_files: - split: test path: en-de/test-* - config_name: en-is data_files: - split: test path: en-is/test-* - config_name: en-ru data_files: - split: test path: en-ru/test-* - config_name: en-zh data_files: - split: test path: en-zh/test-* - config_name: is-en data_files: - split: test path: is-en/test-* - config_name: ru-en data_files: - split: test path: ru-en/test-* - config_name: zh-en data_files: - split: test path: zh-en/test-* --- # Dataset Card for "WMT22-Test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mb7419/legal-advice-reddit

--- dataset_info: features: - name: ques_title dtype: string - name: ques_text dtype: string - name: ques_created dtype: string - name: ques_score dtype: int64 - name: ans_text dtype: string - name: ans_created dtype: string - name: ans_score dtype: float64 - name: dominant_topic_name dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 228451850 num_examples: 115359 - name: validation num_bytes: 49046245 num_examples: 24720 - name: test num_bytes: 49058903 num_examples: 24720 download_size: 197856704 dataset_size: 326556998 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

shrinath-suresh/so_5k_with_short_answer_cleaned

--- license: apache-2.0 ---

Rami/Diabetic_Retinopathy_Preprocessed_Dataset_256x256

--- language: - en size_categories: - 1K<n<10K task_categories: - image-classification tags: - medical configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 354568127.0 num_examples: 2750 download_size: 0 dataset_size: 354568127.0 --- This is dataset comes from this [Kaggle Dataset](https://www.kaggle.com/datasets/sachinkumar413/diabetic-retinopathy-dataset/) from the user [Sachin Kumar](https://www.kaggle.com/sachinkumar413). - The goal of the dataset is for the Varun AIM Projects to easily start running and download the dataset on their local computer in the HF libraries as the directory I strongly recommedn to use.

DBQ/Mr.Porter.Product.prices.Hungary

--- annotations_creators: - other language_creators: - other language: - en license: - unknown multilinguality: - monolingual source_datasets: - original task_categories: - text-classification - image-classification - feature-extraction - image-segmentation - image-to-image - image-to-text - object-detection - summarization - zero-shot-image-classification pretty_name: Hungary - Mr Porter - Product-level price list tags: - webscraping - ecommerce - Mr Porter - fashion - fashion product - image - fashion image configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: website_name dtype: string - name: competence_date dtype: string - name: country_code dtype: string - name: currency_code dtype: string - name: brand dtype: string - name: category1_code dtype: string - name: category2_code dtype: string - name: category3_code dtype: string - name: product_code dtype: int64 - name: title dtype: string - name: itemurl dtype: string - name: imageurl dtype: string - name: full_price dtype: float64 - name: price dtype: float64 - name: full_price_eur dtype: float64 - name: price_eur dtype: float64 - name: flg_discount dtype: int64 splits: - name: train num_bytes: 9097423 num_examples: 27686 download_size: 2070861 dataset_size: 9097423 --- # Mr Porter web scraped data ## About the website Mr Porter operates within the **online luxury fashion retail industry** in the **EMEA** region, particularly focusing on the arena of mens fashion in **Hungary**. This industry has been seeing notable growth owing to increasing internet penetration and the rising consumer interest in high-end fashion. **Ecommerce** has now become a significant component of the luxury fashion business model. The dataset studied particularly consists of **product-list page (PLP) data** for **Mr Porter in Hungary**, which is a valuable source of insight into consumer preferences for luxury mens fashion and the overall performance of the industry in this market. ## Link to **dataset** [Hungary - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Hungary/r/recevRWtytAuVgwUz)