davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
bot-yaya/undl_ar2en_aligned	--- dataset_info: features: - name: record dtype: string - name: clean_para_index_set_pair dtype: string - name: src dtype: string - name: dst dtype: string - name: src_text dtype: string - name: dst_text dtype: string - name: src_rate dtype: float64 - name: dst_rate dtype: float64 splits: - name: train num_bytes: 12012712129 num_examples: 15217906 download_size: 0 dataset_size: 12012712129 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "undl_ar2en_aligned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/martha_santa_fgo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of martha_santa/マルタ〔サンタ〕/玛尔达〔圣诞〕 (Fate/Grand Order) This is the dataset of martha_santa/マルタ〔サンタ〕/玛尔达〔圣诞〕 (Fate/Grand Order), containing 64 images and their tags. The core tags of this character are `purple_hair, long_hair, blue_eyes, hat, red_headwear, santa_hat, breasts`, 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 \| 64 \| 61.38 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/martha_santa_fgo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 64 \| 59.12 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/martha_santa_fgo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 143 \| 109.03 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/martha_santa_fgo/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/martha_santa_fgo', 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 \| 64 \| ![](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, long_sleeves, smile, solo, brown_shirt, blush, christmas, looking_at_viewer, mittens, white_apron, red_skirt, fur_trim, brooch, open_mouth, off_shoulder, belt \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| long_sleeves \| smile \| solo \| brown_shirt \| blush \| christmas \| looking_at_viewer \| mittens \| white_apron \| red_skirt \| fur_trim \| brooch \| open_mouth \| off_shoulder \| belt \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:--------\|:-------\|:--------------\|:--------\|:------------\|:--------------------\|:----------\|:--------------\|:------------\|:-----------\|:---------\|:-------------\|:---------------\|:-------\| \| 0 \| 64 \| ![](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 \|
ZHLiu627/ultrafeedback_binarized_with_response_full_part1	--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: reference_response dtype: string splits: - name: train_prefs num_bytes: 167825271 num_examples: 20000 download_size: 93223431 dataset_size: 167825271 configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* --- # Dataset Card for "ultrafeedback_binarized_with_response_full_part1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sekarmulyani/ulasan-ecommerce-classification	--- license: apache-2.0 task_categories: - text-classification language: - id size_categories: - 100K<n<1M ---
johannes-garstenauer/PerformanceTest	--- license: apache-2.0 ---
rajistics/electricity_demand	--- task_categories: - time-series-forecasting --- The Victoria electricity demand dataset from the [MAPIE github repository](https://github.com/scikit-learn-contrib/MAPIE/tree/master/examples/data). It consists of hourly electricity demand (in GW) of the Victoria state in Australia together with the temperature (in Celsius degrees).
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_125m_Attributes_ns_5647	--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_0_bs_16 num_bytes: 84088557.125 num_examples: 5647 - name: fewshot_1_bs_16 num_bytes: 85276022.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 87656291.125 num_examples: 5647 - name: fewshot_5_bs_16 num_bytes: 90034037.125 num_examples: 5647 - name: fewshot_8_bs_16 num_bytes: 93580093.125 num_examples: 5647 download_size: 415553691 dataset_size: 440635000.625 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_125m_Attributes_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_jingyeom__SOLAR_KO_1.3_deup	--- pretty_name: Evaluation run of jingyeom/SOLAR_KO_1.3_deup dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jingyeom/SOLAR_KO_1.3_deup](https://huggingface.co/jingyeom/SOLAR_KO_1.3_deup)\ \ 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_jingyeom__SOLAR_KO_1.3_deup\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-17T00:23:55.496430](https://huggingface.co/datasets/open-llm-leaderboard/details_jingyeom__SOLAR_KO_1.3_deup/blob/main/results_2024-01-17T00-23-55.496430.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\": 0.5568308436610663,\n\ \ \"acc_stderr\": 0.03382759863491837,\n \"acc_norm\": 0.562882955720715,\n\ \ \"acc_norm_stderr\": 0.03456146092708182,\n \"mc1\": 0.3182374541003672,\n\ \ \"mc1_stderr\": 0.016305988648920623,\n \"mc2\": 0.4754562707057089,\n\ \ \"mc2_stderr\": 0.01501819768286651\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5238907849829352,\n \"acc_stderr\": 0.014594701798071654,\n\ \ \"acc_norm\": 0.5597269624573379,\n \"acc_norm_stderr\": 0.014506769524804234\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5974905397331209,\n\ \ \"acc_stderr\": 0.004894012555642646,\n \"acc_norm\": 0.7997410874327823,\n\ \ \"acc_norm_stderr\": 0.003993761698847879\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4962962962962963,\n\ \ \"acc_stderr\": 0.04319223625811331,\n \"acc_norm\": 0.4962962962962963,\n\ \ \"acc_norm_stderr\": 0.04319223625811331\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.5723684210526315,\n \"acc_stderr\": 0.04026097083296564,\n\ \ \"acc_norm\": 0.5723684210526315,\n \"acc_norm_stderr\": 0.04026097083296564\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.5924528301886792,\n \"acc_stderr\": 0.030242233800854494,\n\ \ \"acc_norm\": 0.5924528301886792,\n \"acc_norm_stderr\": 0.030242233800854494\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.039420826399272135,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.039420826399272135\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n\ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.5606936416184971,\n\ \ \"acc_stderr\": 0.03784271932887468,\n \"acc_norm\": 0.5606936416184971,\n\ \ \"acc_norm_stderr\": 0.03784271932887468\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n\ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467382,\n\ \ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467382\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.37719298245614036,\n\ \ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n\ \ \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n\ \ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.36243386243386244,\n \"acc_stderr\": 0.02475747390275206,\n \"\ acc_norm\": 0.36243386243386244,\n \"acc_norm_stderr\": 0.02475747390275206\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.40476190476190477,\n\ \ \"acc_stderr\": 0.043902592653775614,\n \"acc_norm\": 0.40476190476190477,\n\ \ \"acc_norm_stderr\": 0.043902592653775614\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.632258064516129,\n \"acc_stderr\": 0.02743086657997347,\n \"acc_norm\"\ : 0.632258064516129,\n \"acc_norm_stderr\": 0.02743086657997347\n },\n\ \ \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.3793103448275862,\n\ \ \"acc_stderr\": 0.03413963805906235,\n \"acc_norm\": 0.3793103448275862,\n\ \ \"acc_norm_stderr\": 0.03413963805906235\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"\ acc\": 0.6727272727272727,\n \"acc_stderr\": 0.036639749943912434,\n \ \ \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.036639749943912434\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7070707070707071,\n \"acc_stderr\": 0.03242497958178816,\n \"\ acc_norm\": 0.7070707070707071,\n \"acc_norm_stderr\": 0.03242497958178816\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7772020725388601,\n \"acc_stderr\": 0.030031147977641538,\n\ \ \"acc_norm\": 0.7772020725388601,\n \"acc_norm_stderr\": 0.030031147977641538\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5051282051282051,\n \"acc_stderr\": 0.025349672906838653,\n\ \ \"acc_norm\": 0.5051282051282051,\n \"acc_norm_stderr\": 0.025349672906838653\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253252,\n \ \ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253252\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.5672268907563025,\n \"acc_stderr\": 0.03218358107742613,\n \ \ \"acc_norm\": 0.5672268907563025,\n \"acc_norm_stderr\": 0.03218358107742613\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.037101857261199946,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.037101857261199946\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7504587155963303,\n \"acc_stderr\": 0.018553897629501624,\n \"\ acc_norm\": 0.7504587155963303,\n \"acc_norm_stderr\": 0.018553897629501624\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4537037037037037,\n \"acc_stderr\": 0.03395322726375797,\n \"\ acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.03395322726375797\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\": 0.6962025316455697,\n \"acc_stderr\": 0.02993669638713861,\n \ \ \"acc_norm\": 0.6962025316455697,\n \"acc_norm_stderr\": 0.02993669638713861\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.5877862595419847,\n \"acc_stderr\": 0.04317171194870255,\n\ \ \"acc_norm\": 0.5877862595419847,\n \"acc_norm_stderr\": 0.04317171194870255\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.6942148760330579,\n \"acc_stderr\": 0.04205953933884122,\n \"\ acc_norm\": 0.6942148760330579,\n \"acc_norm_stderr\": 0.04205953933884122\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.04668408033024931,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.04668408033024931\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6503067484662577,\n \"acc_stderr\": 0.03746668325470021,\n\ \ \"acc_norm\": 0.6503067484662577,\n \"acc_norm_stderr\": 0.03746668325470021\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8333333333333334,\n\ \ \"acc_stderr\": 0.024414947304543678,\n \"acc_norm\": 0.8333333333333334,\n\ \ \"acc_norm_stderr\": 0.024414947304543678\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7611749680715197,\n\ \ \"acc_stderr\": 0.015246803197398687,\n \"acc_norm\": 0.7611749680715197,\n\ \ \"acc_norm_stderr\": 0.015246803197398687\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5867052023121387,\n \"acc_stderr\": 0.026511261369409247,\n\ \ \"acc_norm\": 0.5867052023121387,\n \"acc_norm_stderr\": 0.026511261369409247\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2759776536312849,\n\ \ \"acc_stderr\": 0.014950103002475365,\n \"acc_norm\": 0.2759776536312849,\n\ \ \"acc_norm_stderr\": 0.014950103002475365\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6274509803921569,\n \"acc_stderr\": 0.027684181883302877,\n\ \ \"acc_norm\": 0.6274509803921569,\n \"acc_norm_stderr\": 0.027684181883302877\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6655948553054662,\n\ \ \"acc_stderr\": 0.02679542232789393,\n \"acc_norm\": 0.6655948553054662,\n\ \ \"acc_norm_stderr\": 0.02679542232789393\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.654320987654321,\n \"acc_stderr\": 0.02646248777700187,\n\ \ \"acc_norm\": 0.654320987654321,\n \"acc_norm_stderr\": 0.02646248777700187\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.3829787234042553,\n \"acc_stderr\": 0.02899908090480618,\n \ \ \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.02899908090480618\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4211212516297262,\n\ \ \"acc_stderr\": 0.012610325733489905,\n \"acc_norm\": 0.4211212516297262,\n\ \ \"acc_norm_stderr\": 0.012610325733489905\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5330882352941176,\n \"acc_stderr\": 0.030306257722468317,\n\ \ \"acc_norm\": 0.5330882352941176,\n \"acc_norm_stderr\": 0.030306257722468317\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5441176470588235,\n \"acc_stderr\": 0.020148939420415745,\n \ \ \"acc_norm\": 0.5441176470588235,\n \"acc_norm_stderr\": 0.020148939420415745\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5909090909090909,\n\ \ \"acc_stderr\": 0.04709306978661896,\n \"acc_norm\": 0.5909090909090909,\n\ \ \"acc_norm_stderr\": 0.04709306978661896\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6530612244897959,\n \"acc_stderr\": 0.030472526026726492,\n\ \ \"acc_norm\": 0.6530612244897959,\n \"acc_norm_stderr\": 0.030472526026726492\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.7313432835820896,\n\ \ \"acc_stderr\": 0.03134328358208955,\n \"acc_norm\": 0.7313432835820896,\n\ \ \"acc_norm_stderr\": 0.03134328358208955\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.463855421686747,\n\ \ \"acc_stderr\": 0.03882310850890593,\n \"acc_norm\": 0.463855421686747,\n\ \ \"acc_norm_stderr\": 0.03882310850890593\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7426900584795322,\n \"acc_stderr\": 0.03352799844161865,\n\ \ \"acc_norm\": 0.7426900584795322,\n \"acc_norm_stderr\": 0.03352799844161865\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3182374541003672,\n\ \ \"mc1_stderr\": 0.016305988648920623,\n \"mc2\": 0.4754562707057089,\n\ \ \"mc2_stderr\": 0.01501819768286651\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.01185004012485051\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.2259287338893101,\n \ \ \"acc_stderr\": 0.01151909877727995\n }\n}\n```" repo_url: https://huggingface.co/jingyeom/SOLAR_KO_1.3_deup 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_01_17T00_23_55.496430 path: - '/details_harness\|arc:challenge\|25_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-17T00-23-55.496430.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|gsm8k\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hellaswag\|10_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-17T00-23-55.496430.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-17T00-23-55.496430.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '/details_harness\|winogrande\|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-17T00-23-55.496430.parquet' - config_name: results data_files: - split: 2024_01_17T00_23_55.496430 path: - results_2024-01-17T00-23-55.496430.parquet - split: latest path: - results_2024-01-17T00-23-55.496430.parquet --- # Dataset Card for Evaluation run of jingyeom/SOLAR_KO_1.3_deup <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [jingyeom/SOLAR_KO_1.3_deup](https://huggingface.co/jingyeom/SOLAR_KO_1.3_deup) 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_jingyeom__SOLAR_KO_1.3_deup", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-17T00:23:55.496430](https://huggingface.co/datasets/open-llm-leaderboard/details_jingyeom__SOLAR_KO_1.3_deup/blob/main/results_2024-01-17T00-23-55.496430.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": 0.5568308436610663, "acc_stderr": 0.03382759863491837, "acc_norm": 0.562882955720715, "acc_norm_stderr": 0.03456146092708182, "mc1": 0.3182374541003672, "mc1_stderr": 0.016305988648920623, "mc2": 0.4754562707057089, "mc2_stderr": 0.01501819768286651 }, "harness\|arc:challenge\|25": { "acc": 0.5238907849829352, "acc_stderr": 0.014594701798071654, "acc_norm": 0.5597269624573379, "acc_norm_stderr": 0.014506769524804234 }, "harness\|hellaswag\|10": { "acc": 0.5974905397331209, "acc_stderr": 0.004894012555642646, "acc_norm": 0.7997410874327823, "acc_norm_stderr": 0.003993761698847879 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4962962962962963, "acc_stderr": 0.04319223625811331, "acc_norm": 0.4962962962962963, "acc_norm_stderr": 0.04319223625811331 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5723684210526315, "acc_stderr": 0.04026097083296564, "acc_norm": 0.5723684210526315, "acc_norm_stderr": 0.04026097083296564 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5924528301886792, "acc_stderr": 0.030242233800854494, "acc_norm": 0.5924528301886792, "acc_norm_stderr": 0.030242233800854494 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.039420826399272135, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.039420826399272135 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5606936416184971, "acc_stderr": 0.03784271932887468, "acc_norm": 0.5606936416184971, "acc_norm_stderr": 0.03784271932887468 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2647058823529412, "acc_stderr": 0.04389869956808778, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.04389869956808778 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467382, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467382 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.37719298245614036, "acc_stderr": 0.04559522141958216, "acc_norm": 0.37719298245614036, "acc_norm_stderr": 0.04559522141958216 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5241379310344828, "acc_stderr": 0.0416180850350153, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.0416180850350153 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.36243386243386244, "acc_stderr": 0.02475747390275206, "acc_norm": 0.36243386243386244, "acc_norm_stderr": 0.02475747390275206 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.043902592653775614, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.043902592653775614 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.632258064516129, "acc_stderr": 0.02743086657997347, "acc_norm": 0.632258064516129, "acc_norm_stderr": 0.02743086657997347 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3793103448275862, "acc_stderr": 0.03413963805906235, "acc_norm": 0.3793103448275862, "acc_norm_stderr": 0.03413963805906235 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.036639749943912434, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.036639749943912434 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7070707070707071, "acc_stderr": 0.03242497958178816, "acc_norm": 0.7070707070707071, "acc_norm_stderr": 0.03242497958178816 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7772020725388601, "acc_stderr": 0.030031147977641538, "acc_norm": 0.7772020725388601, "acc_norm_stderr": 0.030031147977641538 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5051282051282051, "acc_stderr": 0.025349672906838653, "acc_norm": 0.5051282051282051, "acc_norm_stderr": 0.025349672906838653 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.337037037037037, "acc_stderr": 0.028820884666253252, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.028820884666253252 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5672268907563025, "acc_stderr": 0.03218358107742613, "acc_norm": 0.5672268907563025, "acc_norm_stderr": 0.03218358107742613 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2913907284768212, "acc_stderr": 0.037101857261199946, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.037101857261199946 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7504587155963303, "acc_stderr": 0.018553897629501624, "acc_norm": 0.7504587155963303, "acc_norm_stderr": 0.018553897629501624 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4537037037037037, "acc_stderr": 0.03395322726375797, "acc_norm": 0.4537037037037037, "acc_norm_stderr": 0.03395322726375797 }, "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": 0.6962025316455697, "acc_stderr": 0.02993669638713861, "acc_norm": 0.6962025316455697, "acc_norm_stderr": 0.02993669638713861 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6771300448430493, "acc_stderr": 0.03138147637575499, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.03138147637575499 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5877862595419847, "acc_stderr": 0.04317171194870255, "acc_norm": 0.5877862595419847, "acc_norm_stderr": 0.04317171194870255 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6942148760330579, "acc_stderr": 0.04205953933884122, "acc_norm": 0.6942148760330579, "acc_norm_stderr": 0.04205953933884122 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6296296296296297, "acc_stderr": 0.04668408033024931, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.04668408033024931 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6503067484662577, "acc_stderr": 0.03746668325470021, "acc_norm": 0.6503067484662577, "acc_norm_stderr": 0.03746668325470021 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.024414947304543678, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.024414947304543678 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7611749680715197, "acc_stderr": 0.015246803197398687, "acc_norm": 0.7611749680715197, "acc_norm_stderr": 0.015246803197398687 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5867052023121387, "acc_stderr": 0.026511261369409247, "acc_norm": 0.5867052023121387, "acc_norm_stderr": 0.026511261369409247 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2759776536312849, "acc_stderr": 0.014950103002475365, "acc_norm": 0.2759776536312849, "acc_norm_stderr": 0.014950103002475365 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6274509803921569, "acc_stderr": 0.027684181883302877, "acc_norm": 0.6274509803921569, "acc_norm_stderr": 0.027684181883302877 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6655948553054662, "acc_stderr": 0.02679542232789393, "acc_norm": 0.6655948553054662, "acc_norm_stderr": 0.02679542232789393 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.654320987654321, "acc_stderr": 0.02646248777700187, "acc_norm": 0.654320987654321, "acc_norm_stderr": 0.02646248777700187 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3829787234042553, "acc_stderr": 0.02899908090480618, "acc_norm": 0.3829787234042553, "acc_norm_stderr": 0.02899908090480618 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4211212516297262, "acc_stderr": 0.012610325733489905, "acc_norm": 0.4211212516297262, "acc_norm_stderr": 0.012610325733489905 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5330882352941176, "acc_stderr": 0.030306257722468317, "acc_norm": 0.5330882352941176, "acc_norm_stderr": 0.030306257722468317 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5441176470588235, "acc_stderr": 0.020148939420415745, "acc_norm": 0.5441176470588235, "acc_norm_stderr": 0.020148939420415745 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5909090909090909, "acc_stderr": 0.04709306978661896, "acc_norm": 0.5909090909090909, "acc_norm_stderr": 0.04709306978661896 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6530612244897959, "acc_stderr": 0.030472526026726492, "acc_norm": 0.6530612244897959, "acc_norm_stderr": 0.030472526026726492 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.7313432835820896, "acc_stderr": 0.03134328358208955, "acc_norm": 0.7313432835820896, "acc_norm_stderr": 0.03134328358208955 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.8, "acc_stderr": 0.04020151261036846, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036846 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.463855421686747, "acc_stderr": 0.03882310850890593, "acc_norm": 0.463855421686747, "acc_norm_stderr": 0.03882310850890593 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7426900584795322, "acc_stderr": 0.03352799844161865, "acc_norm": 0.7426900584795322, "acc_norm_stderr": 0.03352799844161865 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3182374541003672, "mc1_stderr": 0.016305988648920623, "mc2": 0.4754562707057089, "mc2_stderr": 0.01501819768286651 }, "harness\|winogrande\|5": { "acc": 0.7687450670876085, "acc_stderr": 0.01185004012485051 }, "harness\|gsm8k\|5": { "acc": 0.2259287338893101, "acc_stderr": 0.01151909877727995 } } ``` ## 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]
DJBanzin/Vozesvukvuk	--- license: openrail ---
dar-tau/lm-extraction-benchmark	--- dataset_info: features: - name: preprefix sequence: uint16 - name: prefix sequence: uint16 - name: suffix sequence: uint16 splits: - name: train num_bytes: 6180000 num_examples: 15000 download_size: 5792506 dataset_size: 6180000 configs: - config_name: default data_files: - split: train path: data/train-* ---
erikliu18/us-congress-hearing	--- task_categories: - text-classification language: - en tags: - finance - legal --- # U.S. Congressional Hearings Dataset This dataset currently contains cleaned sentences from all House Committee on Energy and Commerce hearings from 2002. A total of 1K+ hearing transcripts in txt formats from govinfo.gov were collected and cleaned.
feliipert/Reportes-radiologicos	--- license: apache-2.0 task_categories: - text-classification language: - es tags: - medical pretty_name: Radiologist size_categories: - n<1K ---
mnazari/nena_speech_1_0_test	--- pretty_name: NENA Speech Dataset 1.0 (test) annotations_creators: - crowdsourced - Geoffrey Khan language_creators: - crowdsourced language: - aii - cld - huy - lsd - trg - aij - bhn - hrt - kqd - syn license: - cc0-1.0 multilinguality: - multilingual task_categories: - automatic-speech-recognition - text-to-speech - translation size_categories: - 10K<n<100K - 1K<n<10K - n<1K --- # Dataset Card for NENA Speech Dataset 1.0 (test) ## Table of Contents - [Dataset Summary](#dataset-summary) - [Dataset Description](#dataset-description) - [Languages](#languages) - [How to Use](#how-to-use) - [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) --> - [Building the Dataset](#building-the-dataset) - [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) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## ⚠️ This is a temperary repository that will be replaced by end of 2023 ## Dataset Summary NENA Speech is a multimodal dataset to help teach machines how real people speak the Northeastern Neo-Aramaic (NENA) dialects. The NENA dialects form a very diverse group of Aramaic dialects spoken by Christian and Jewish communities indigenous to northwestern Iran, northern Iraq, and southeastern Türkiye. NENA Speech consists of multimodal examples of speech of the NENA dialects. While all documented NENA dialects are included, not all have data yet, and some will never due to recent loss of their final speakers. ## Dataset Description - Homepage: https://crowdsource.nenadb.dev/ - Point of Contact: [Matthew Nazari](mailto:matthewnazari@college.harvard.edu) ## Languages The NENA dialects form a very diverse group of Aramaic dialects spoken by Christian and Jewish communities indigenous to northwestern Iran, northern Iraq, and southeastern Türkiye. Speakers of the Christian dialects call their language Assyrian and Chaldean in English. In their language these speakers use multiple different terms (e.g. suráy, sureth, ḥadiṯan, senaya). Speakers of the Jewish dialects call their language lišana deni, lišanət noshan, lišana nosha, lišana didan, all meaning "our language". Some names reflect the consciousness of it being a specifically Jewish language (e.g. lišan hozaye, hulaula). NENA Speech has a subset for all of the over 150 NENA dialects. Not all dialects have examples available yet. Some dialects will never have examples available due to the loss of their final speakers in recent years. ## How to Use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, simply specify the corresponding language config name (e.g., "urmi (christian)" for the dialect of the Assyrian Christians of Urmi): ```python from datasets import load_dataset nena_speech = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train") ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ## Dataset Structure ### Data Instances The NENA Speech dataset is a multimodal dataset that consists of three different kinds of examples: 1. Unlabeled speech examples: these contain audio of speech (`audio`) but no accompanying transcription (`transcription`) or translation (`translation`). This is useful for representation learning. 2. Transcribed speech examples: these contain both audio and transcription of speech. These are useful for machine learning tasks like automatic speech recognition and speech synthesis. 3. Transcribed and translated speech examples: these kinds of examples contain audio, transcription, and translation of speech. These are useful for tasks like multimodal translation. Make sure to filter for the kinds of examples you need for your task before before using it. ```json { "transcription": "gu-mdìta.ˈ", "translation": "in the town.", "audio": { "path": "et/train/nena_speech_0uk14ofpom196aj.mp3", "array": array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), "sampling_rate": 48000 }, "locale": "IRN", "proficiency": "proficient as mom", "age": "70's", "crowdsourced": true, "unlabeled": true, "interrupted": true, "client_id": "gwurt1g1ln" , "path": "et/train/nena_speech_0uk14ofpom196aj.mp3", } ``` ### Data Fields - `transcription (string)`: The transcription of what was spoken (e.g. `"beta"`) - `translation (string)`: The translation of what was spoken in English (e.g. `"house"`) - `audio (dict)`: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. - `locale (string)`: The locale of the speaker - `proficiency (string)`: The proficiency of the speaker - `age (string)`: The age of the speaker (e.g. `"20's"`, `"50's"`, `"100+"`) - `crowdsourced (bool)`: Indicates whether the example was crowdsourced as opposed to collected from existing language documentation resources - `interrupted (bool)`: Indicates whether the example was interrupted with the speaker making sound effects or switching into another language - `client_id (string)`: An id for which client (voice) made the recording - `path (string)`: The path to the audio file ### Data Splits The examples have been subdivided into three portions: 1. dev: the validation split (10%) 3. test: the test split (10%) 2. train: the train split (80%) The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Dataset Creation <!-- ### Curation Rationale [Needs More Information] ### Source Data #### Language Documentation Resources [Needs More Information] #### Webscraping Facebook [Needs More Information] #### Crowdsourcing [Needs More Information] ### Annotations [Needs More Information] --> ### Building the Dataset The NENA Speech dataset itself is built using `build.py`. First, install the necessary requirements. ``` pip install -r requirements.txt ``` Next, build the dataset. ``` python build.py --build ``` Finally, push to the HuggingFace dataset repository. ## Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Data Preprocessing The dataset consists of three different kinds of examples (see [Data Instances](#data-instances)). Make sure to filter for the kinds of examples you need for your task before before using it. For example, for automatic speech recognition you will want to filter for examples with transcriptions. In most tasks, you will want to filter out examples that are interrupted (e.g. by the speaker making sound effects, by the speaker switching into a another language). ```python from datasets import load_dataset ds = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train") def filter_for_asr(example): return example['transcription'] and not example['interrupted'] ds = ds.filter(filter_for_asr, desc="filter dataset") ``` Transcriptions include markers of linguistic and acoustic features which may be removed in certain tasks (e.g. word stress, nuclear stress, intonation group markers, vowel length). ```python from datasets import load_dataset ds = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train") def prepare_dataset(batch): chars_to_remove = ['ˈ', '̀', '́', '̄', '̆', '.', ',', '?', '!'] for char in chars_to_remove: batch["transcription"] = batch["transcription"].replace(char, "") return batch ds = ds.map(prepare_dataset, desc="preprocess dataset") ``` <!-- ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] --> ## Additional Information ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/). ### Citation Information This work has not yet been published.
andersonbcdefg/doc_nli_pos_pairs	--- dataset_info: features: - name: query dtype: string - name: pos dtype: string splits: - name: train num_bytes: 888275454 num_examples: 528671 download_size: 467347853 dataset_size: 888275454 configs: - config_name: default data_files: - split: train path: data/train-* ---
happycute123/DL_dataset	--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 679382539.464 num_examples: 1057 - name: test num_bytes: 167054773.0 num_examples: 264 download_size: 0 dataset_size: 846437312.464 --- # Dataset Card for "DL_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
xorsuyash/raft_datasetp1	--- license: mit ---
FDeRubeis/araft	--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: label dtype: string - name: prediction dtype: string - name: trajectory dtype: string splits: - name: train num_bytes: 961824 num_examples: 413 download_size: 499573 dataset_size: 961824 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_uukuguy__GDC-Tiny-L1-1.8B	--- pretty_name: Evaluation run of uukuguy/GDC-Tiny-L1-1.8B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [uukuguy/GDC-Tiny-L1-1.8B](https://huggingface.co/uukuguy/GDC-Tiny-L1-1.8B) 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 4 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_uukuguy__GDC-Tiny-L1-1.8B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-07T19:08:55.303427](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__GDC-Tiny-L1-1.8B/blob/main/results_2024-04-07T19-08-55.303427.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\": 0.43818164678489546,\n\ \ \"acc_stderr\": 0.03446376592395749,\n \"acc_norm\": 0.44061901287311,\n\ \ \"acc_norm_stderr\": 0.03519031730786029,\n \"mc1\": 0.2594859241126071,\n\ \ \"mc1_stderr\": 0.015345409485557989,\n \"mc2\": 0.40282806404013544,\n\ \ \"mc2_stderr\": 0.014486104033756\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.3438566552901024,\n \"acc_stderr\": 0.01388064457015621,\n\ \ \"acc_norm\": 0.3651877133105802,\n \"acc_norm_stderr\": 0.014070265519268804\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.4400517825134435,\n\ \ \"acc_stderr\": 0.004953787146510927,\n \"acc_norm\": 0.5866361282613025,\n\ \ \"acc_norm_stderr\": 0.004914305798575695\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720685,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720685\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.362962962962963,\n\ \ \"acc_stderr\": 0.04153948404742399,\n \"acc_norm\": 0.362962962962963,\n\ \ \"acc_norm_stderr\": 0.04153948404742399\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.46710526315789475,\n \"acc_stderr\": 0.040601270352363966,\n\ \ \"acc_norm\": 0.46710526315789475,\n \"acc_norm_stderr\": 0.040601270352363966\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.52,\n\ \ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.4528301886792453,\n \"acc_stderr\": 0.030635627957961816,\n\ \ \"acc_norm\": 0.4528301886792453,\n \"acc_norm_stderr\": 0.030635627957961816\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4305555555555556,\n\ \ \"acc_stderr\": 0.04140685639111503,\n \"acc_norm\": 0.4305555555555556,\n\ \ \"acc_norm_stderr\": 0.04140685639111503\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\"\ : 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.3930635838150289,\n\ \ \"acc_stderr\": 0.0372424959581773,\n \"acc_norm\": 0.3930635838150289,\n\ \ \"acc_norm_stderr\": 0.0372424959581773\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179964,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179964\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n\ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.4297872340425532,\n \"acc_stderr\": 0.03236214467715564,\n\ \ \"acc_norm\": 0.4297872340425532,\n \"acc_norm_stderr\": 0.03236214467715564\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2982456140350877,\n\ \ \"acc_stderr\": 0.04303684033537314,\n \"acc_norm\": 0.2982456140350877,\n\ \ \"acc_norm_stderr\": 0.04303684033537314\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.4206896551724138,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.4206896551724138,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3544973544973545,\n \"acc_stderr\": 0.024636830602842,\n \"acc_norm\"\ : 0.3544973544973545,\n \"acc_norm_stderr\": 0.024636830602842\n },\n\ \ \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2619047619047619,\n\ \ \"acc_stderr\": 0.0393253768039287,\n \"acc_norm\": 0.2619047619047619,\n\ \ \"acc_norm_stderr\": 0.0393253768039287\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.49032258064516127,\n \"acc_stderr\": 0.028438677998909558,\n \"\ acc_norm\": 0.49032258064516127,\n \"acc_norm_stderr\": 0.028438677998909558\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.3103448275862069,\n \"acc_stderr\": 0.03255086769970103,\n \"\ acc_norm\": 0.3103448275862069,\n \"acc_norm_stderr\": 0.03255086769970103\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.44,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\"\ : 0.44,\n \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.5818181818181818,\n \"acc_stderr\": 0.03851716319398395,\n\ \ \"acc_norm\": 0.5818181818181818,\n \"acc_norm_stderr\": 0.03851716319398395\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.5959595959595959,\n \"acc_stderr\": 0.03496130972056127,\n \"\ acc_norm\": 0.5959595959595959,\n \"acc_norm_stderr\": 0.03496130972056127\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.5440414507772021,\n \"acc_stderr\": 0.03594413711272437,\n\ \ \"acc_norm\": 0.5440414507772021,\n \"acc_norm_stderr\": 0.03594413711272437\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.37948717948717947,\n \"acc_stderr\": 0.024603626924097417,\n\ \ \"acc_norm\": 0.37948717948717947,\n \"acc_norm_stderr\": 0.024603626924097417\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3,\n \"acc_stderr\": 0.027940457136228412,\n \"acc_norm\"\ : 0.3,\n \"acc_norm_stderr\": 0.027940457136228412\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.0322529423239964,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.0322529423239964\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.25165562913907286,\n \"acc_stderr\": 0.035433042343899844,\n \"\ acc_norm\": 0.25165562913907286,\n \"acc_norm_stderr\": 0.035433042343899844\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.5596330275229358,\n \"acc_stderr\": 0.02128431062376155,\n \"\ acc_norm\": 0.5596330275229358,\n \"acc_norm_stderr\": 0.02128431062376155\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.25462962962962965,\n \"acc_stderr\": 0.029711275860005354,\n \"\ acc_norm\": 0.25462962962962965,\n \"acc_norm_stderr\": 0.029711275860005354\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.4852941176470588,\n \"acc_stderr\": 0.035077938347913236,\n \"\ acc_norm\": 0.4852941176470588,\n \"acc_norm_stderr\": 0.035077938347913236\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.5527426160337553,\n \"acc_stderr\": 0.03236564251614192,\n \ \ \"acc_norm\": 0.5527426160337553,\n \"acc_norm_stderr\": 0.03236564251614192\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.5291479820627802,\n\ \ \"acc_stderr\": 0.03350073248773404,\n \"acc_norm\": 0.5291479820627802,\n\ \ \"acc_norm_stderr\": 0.03350073248773404\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.4961832061068702,\n \"acc_stderr\": 0.04385162325601553,\n\ \ \"acc_norm\": 0.4961832061068702,\n \"acc_norm_stderr\": 0.04385162325601553\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.6198347107438017,\n \"acc_stderr\": 0.04431324501968431,\n \"\ acc_norm\": 0.6198347107438017,\n \"acc_norm_stderr\": 0.04431324501968431\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.48148148148148145,\n\ \ \"acc_stderr\": 0.04830366024635331,\n \"acc_norm\": 0.48148148148148145,\n\ \ \"acc_norm_stderr\": 0.04830366024635331\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.32515337423312884,\n \"acc_stderr\": 0.036803503712864595,\n\ \ \"acc_norm\": 0.32515337423312884,\n \"acc_norm_stderr\": 0.036803503712864595\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4107142857142857,\n\ \ \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.4107142857142857,\n\ \ \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.5825242718446602,\n \"acc_stderr\": 0.048828405482122375,\n\ \ \"acc_norm\": 0.5825242718446602,\n \"acc_norm_stderr\": 0.048828405482122375\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7606837606837606,\n\ \ \"acc_stderr\": 0.027951826808924336,\n \"acc_norm\": 0.7606837606837606,\n\ \ \"acc_norm_stderr\": 0.027951826808924336\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.5644955300127714,\n\ \ \"acc_stderr\": 0.017730589927926595,\n \"acc_norm\": 0.5644955300127714,\n\ \ \"acc_norm_stderr\": 0.017730589927926595\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.49710982658959535,\n \"acc_stderr\": 0.02691864538323901,\n\ \ \"acc_norm\": 0.49710982658959535,\n \"acc_norm_stderr\": 0.02691864538323901\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.23128491620111732,\n\ \ \"acc_stderr\": 0.014102223623152586,\n \"acc_norm\": 0.23128491620111732,\n\ \ \"acc_norm_stderr\": 0.014102223623152586\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.028607893699576063,\n\ \ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.028607893699576063\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.4340836012861736,\n\ \ \"acc_stderr\": 0.0281502322445356,\n \"acc_norm\": 0.4340836012861736,\n\ \ \"acc_norm_stderr\": 0.0281502322445356\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.45987654320987653,\n \"acc_stderr\": 0.027731022753539274,\n\ \ \"acc_norm\": 0.45987654320987653,\n \"acc_norm_stderr\": 0.027731022753539274\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.34397163120567376,\n \"acc_stderr\": 0.028338017428611317,\n \ \ \"acc_norm\": 0.34397163120567376,\n \"acc_norm_stderr\": 0.028338017428611317\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.34810951760104303,\n\ \ \"acc_stderr\": 0.012166738993698195,\n \"acc_norm\": 0.34810951760104303,\n\ \ \"acc_norm_stderr\": 0.012166738993698195\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.34191176470588236,\n \"acc_stderr\": 0.028814722422254187,\n\ \ \"acc_norm\": 0.34191176470588236,\n \"acc_norm_stderr\": 0.028814722422254187\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.4199346405228758,\n \"acc_stderr\": 0.019966811178256483,\n \ \ \"acc_norm\": 0.4199346405228758,\n \"acc_norm_stderr\": 0.019966811178256483\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5272727272727272,\n\ \ \"acc_stderr\": 0.04782001791380061,\n \"acc_norm\": 0.5272727272727272,\n\ \ \"acc_norm_stderr\": 0.04782001791380061\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.45714285714285713,\n \"acc_stderr\": 0.03189141832421397,\n\ \ \"acc_norm\": 0.45714285714285713,\n \"acc_norm_stderr\": 0.03189141832421397\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.5373134328358209,\n\ \ \"acc_stderr\": 0.03525675167467974,\n \"acc_norm\": 0.5373134328358209,\n\ \ \"acc_norm_stderr\": 0.03525675167467974\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695238,\n \ \ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695238\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.39156626506024095,\n\ \ \"acc_stderr\": 0.03799857454479636,\n \"acc_norm\": 0.39156626506024095,\n\ \ \"acc_norm_stderr\": 0.03799857454479636\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.52046783625731,\n \"acc_stderr\": 0.0383161053282193,\n\ \ \"acc_norm\": 0.52046783625731,\n \"acc_norm_stderr\": 0.0383161053282193\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2594859241126071,\n\ \ \"mc1_stderr\": 0.015345409485557989,\n \"mc2\": 0.40282806404013544,\n\ \ \"mc2_stderr\": 0.014486104033756\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.6101026045777427,\n \"acc_stderr\": 0.013707547317008465\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.29037149355572406,\n \ \ \"acc_stderr\": 0.01250359248181895\n }\n}\n```" repo_url: https://huggingface.co/uukuguy/GDC-Tiny-L1-1.8B 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_06T00_21_15.731330 path: - '/details_harness\|arc:challenge\|25_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|arc:challenge\|25_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|arc:challenge\|25_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|arc:challenge\|25_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-07T19-08-55.303427.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|gsm8k\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|gsm8k\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|gsm8k\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|gsm8k\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hellaswag\|10_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hellaswag\|10_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hellaswag\|10_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hellaswag\|10_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-06T00-21-15.731330.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-06T15-47-54.200639.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T05-02-03.235353.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T19-08-55.303427.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-07T19-08-55.303427.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '/details_harness\|winogrande\|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '/details_harness\|winogrande\|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '/details_harness\|winogrande\|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '/details_harness\|winogrande\|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-07T19-08-55.303427.parquet' - config_name: results data_files: - split: 2024_04_06T00_21_15.731330 path: - results_2024-04-06T00-21-15.731330.parquet - split: 2024_04_06T15_47_54.200639 path: - results_2024-04-06T15-47-54.200639.parquet - split: 2024_04_07T05_02_03.235353 path: - results_2024-04-07T05-02-03.235353.parquet - split: 2024_04_07T19_08_55.303427 path: - results_2024-04-07T19-08-55.303427.parquet - split: latest path: - results_2024-04-07T19-08-55.303427.parquet --- # Dataset Card for Evaluation run of uukuguy/GDC-Tiny-L1-1.8B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [uukuguy/GDC-Tiny-L1-1.8B](https://huggingface.co/uukuguy/GDC-Tiny-L1-1.8B) 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 4 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_uukuguy__GDC-Tiny-L1-1.8B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-07T19:08:55.303427](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__GDC-Tiny-L1-1.8B/blob/main/results_2024-04-07T19-08-55.303427.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": 0.43818164678489546, "acc_stderr": 0.03446376592395749, "acc_norm": 0.44061901287311, "acc_norm_stderr": 0.03519031730786029, "mc1": 0.2594859241126071, "mc1_stderr": 0.015345409485557989, "mc2": 0.40282806404013544, "mc2_stderr": 0.014486104033756 }, "harness\|arc:challenge\|25": { "acc": 0.3438566552901024, "acc_stderr": 0.01388064457015621, "acc_norm": 0.3651877133105802, "acc_norm_stderr": 0.014070265519268804 }, "harness\|hellaswag\|10": { "acc": 0.4400517825134435, "acc_stderr": 0.004953787146510927, "acc_norm": 0.5866361282613025, "acc_norm_stderr": 0.004914305798575695 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720685, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720685 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.362962962962963, "acc_stderr": 0.04153948404742399, "acc_norm": 0.362962962962963, "acc_norm_stderr": 0.04153948404742399 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.46710526315789475, "acc_stderr": 0.040601270352363966, "acc_norm": 0.46710526315789475, "acc_norm_stderr": 0.040601270352363966 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.4528301886792453, "acc_stderr": 0.030635627957961816, "acc_norm": 0.4528301886792453, "acc_norm_stderr": 0.030635627957961816 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4305555555555556, "acc_stderr": 0.04140685639111503, "acc_norm": 0.4305555555555556, "acc_norm_stderr": 0.04140685639111503 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.3930635838150289, "acc_stderr": 0.0372424959581773, "acc_norm": 0.3930635838150289, "acc_norm_stderr": 0.0372424959581773 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179964, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179964 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4297872340425532, "acc_stderr": 0.03236214467715564, "acc_norm": 0.4297872340425532, "acc_norm_stderr": 0.03236214467715564 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2982456140350877, "acc_stderr": 0.04303684033537314, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.04303684033537314 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.4206896551724138, "acc_stderr": 0.0411391498118926, "acc_norm": 0.4206896551724138, "acc_norm_stderr": 0.0411391498118926 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3544973544973545, "acc_stderr": 0.024636830602842, "acc_norm": 0.3544973544973545, "acc_norm_stderr": 0.024636830602842 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2619047619047619, "acc_stderr": 0.0393253768039287, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.0393253768039287 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.49032258064516127, "acc_stderr": 0.028438677998909558, "acc_norm": 0.49032258064516127, "acc_norm_stderr": 0.028438677998909558 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3103448275862069, "acc_stderr": 0.03255086769970103, "acc_norm": 0.3103448275862069, "acc_norm_stderr": 0.03255086769970103 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.44, "acc_stderr": 0.049888765156985884, "acc_norm": 0.44, "acc_norm_stderr": 0.049888765156985884 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.5818181818181818, "acc_stderr": 0.03851716319398395, "acc_norm": 0.5818181818181818, "acc_norm_stderr": 0.03851716319398395 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.5959595959595959, "acc_stderr": 0.03496130972056127, "acc_norm": 0.5959595959595959, "acc_norm_stderr": 0.03496130972056127 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.5440414507772021, "acc_stderr": 0.03594413711272437, "acc_norm": 0.5440414507772021, "acc_norm_stderr": 0.03594413711272437 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.37948717948717947, "acc_stderr": 0.024603626924097417, "acc_norm": 0.37948717948717947, "acc_norm_stderr": 0.024603626924097417 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3, "acc_stderr": 0.027940457136228412, "acc_norm": 0.3, "acc_norm_stderr": 0.027940457136228412 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.0322529423239964, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.0322529423239964 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.25165562913907286, "acc_stderr": 0.035433042343899844, "acc_norm": 0.25165562913907286, "acc_norm_stderr": 0.035433042343899844 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.5596330275229358, "acc_stderr": 0.02128431062376155, "acc_norm": 0.5596330275229358, "acc_norm_stderr": 0.02128431062376155 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.25462962962962965, "acc_stderr": 0.029711275860005354, "acc_norm": 0.25462962962962965, "acc_norm_stderr": 0.029711275860005354 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.4852941176470588, "acc_stderr": 0.035077938347913236, "acc_norm": 0.4852941176470588, "acc_norm_stderr": 0.035077938347913236 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.5527426160337553, "acc_stderr": 0.03236564251614192, "acc_norm": 0.5527426160337553, "acc_norm_stderr": 0.03236564251614192 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.5291479820627802, "acc_stderr": 0.03350073248773404, "acc_norm": 0.5291479820627802, "acc_norm_stderr": 0.03350073248773404 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.4961832061068702, "acc_stderr": 0.04385162325601553, "acc_norm": 0.4961832061068702, "acc_norm_stderr": 0.04385162325601553 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6198347107438017, "acc_stderr": 0.04431324501968431, "acc_norm": 0.6198347107438017, "acc_norm_stderr": 0.04431324501968431 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.48148148148148145, "acc_stderr": 0.04830366024635331, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.04830366024635331 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.32515337423312884, "acc_stderr": 0.036803503712864595, "acc_norm": 0.32515337423312884, "acc_norm_stderr": 0.036803503712864595 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4107142857142857, "acc_stderr": 0.04669510663875191, "acc_norm": 0.4107142857142857, "acc_norm_stderr": 0.04669510663875191 }, "harness\|hendrycksTest-management\|5": { "acc": 0.5825242718446602, "acc_stderr": 0.048828405482122375, "acc_norm": 0.5825242718446602, "acc_norm_stderr": 0.048828405482122375 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7606837606837606, "acc_stderr": 0.027951826808924336, "acc_norm": 0.7606837606837606, "acc_norm_stderr": 0.027951826808924336 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.5644955300127714, "acc_stderr": 0.017730589927926595, "acc_norm": 0.5644955300127714, "acc_norm_stderr": 0.017730589927926595 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.49710982658959535, "acc_stderr": 0.02691864538323901, "acc_norm": 0.49710982658959535, "acc_norm_stderr": 0.02691864538323901 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23128491620111732, "acc_stderr": 0.014102223623152586, "acc_norm": 0.23128491620111732, "acc_norm_stderr": 0.014102223623152586 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5196078431372549, "acc_stderr": 0.028607893699576063, "acc_norm": 0.5196078431372549, "acc_norm_stderr": 0.028607893699576063 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.4340836012861736, "acc_stderr": 0.0281502322445356, "acc_norm": 0.4340836012861736, "acc_norm_stderr": 0.0281502322445356 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.45987654320987653, "acc_stderr": 0.027731022753539274, "acc_norm": 0.45987654320987653, "acc_norm_stderr": 0.027731022753539274 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.34397163120567376, "acc_stderr": 0.028338017428611317, "acc_norm": 0.34397163120567376, "acc_norm_stderr": 0.028338017428611317 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.34810951760104303, "acc_stderr": 0.012166738993698195, "acc_norm": 0.34810951760104303, "acc_norm_stderr": 0.012166738993698195 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.34191176470588236, "acc_stderr": 0.028814722422254187, "acc_norm": 0.34191176470588236, "acc_norm_stderr": 0.028814722422254187 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4199346405228758, "acc_stderr": 0.019966811178256483, "acc_norm": 0.4199346405228758, "acc_norm_stderr": 0.019966811178256483 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5272727272727272, "acc_stderr": 0.04782001791380061, "acc_norm": 0.5272727272727272, "acc_norm_stderr": 0.04782001791380061 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.45714285714285713, "acc_stderr": 0.03189141832421397, "acc_norm": 0.45714285714285713, "acc_norm_stderr": 0.03189141832421397 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.5373134328358209, "acc_stderr": 0.03525675167467974, "acc_norm": 0.5373134328358209, "acc_norm_stderr": 0.03525675167467974 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695238, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695238 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.39156626506024095, "acc_stderr": 0.03799857454479636, "acc_norm": 0.39156626506024095, "acc_norm_stderr": 0.03799857454479636 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.52046783625731, "acc_stderr": 0.0383161053282193, "acc_norm": 0.52046783625731, "acc_norm_stderr": 0.0383161053282193 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2594859241126071, "mc1_stderr": 0.015345409485557989, "mc2": 0.40282806404013544, "mc2_stderr": 0.014486104033756 }, "harness\|winogrande\|5": { "acc": 0.6101026045777427, "acc_stderr": 0.013707547317008465 }, "harness\|gsm8k\|5": { "acc": 0.29037149355572406, "acc_stderr": 0.01250359248181895 } } ``` ## 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]
liuyanchen1015/MULTI_VALUE_rte_completive_done	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 481976 num_examples: 1156 - name: train num_bytes: 421696 num_examples: 962 download_size: 578766 dataset_size: 903672 --- # Dataset Card for "MULTI_VALUE_rte_completive_done" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
toilaluan/t2i_reward_v3	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: model_type dtype: string - name: request_id dtype: int64 - name: topic dtype: string - name: reward dtype: float64 - name: individual_rewards struct: - name: image_rewarder dtype: float64 - name: hps_v2_rewarder dtype: float64 splits: - name: train num_bytes: 205400 num_examples: 2400 download_size: 49480 dataset_size: 205400 --- # Dataset Card for "t2i_reward_v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Slichi/universe	--- license: openrail ---
IndonesiaAI/dpo-dataset	--- dataset_info: features: - name: qid dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 14256438583.53102 num_examples: 3798835 - name: test num_bytes: 1584049565.4689815 num_examples: 422093 download_size: 8864292488 dataset_size: 15840488149.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
DynamicSuperb/StressDetection_MIRSD	--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: label dtype: string - name: word dtype: string - name: instruction dtype: string splits: - name: test num_bytes: 19241423.221727517 num_examples: 200 download_size: 17768718 dataset_size: 19241423.221727517 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "stress_dection_MIR_SD" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/tsubaki_bluearchive	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tsubaki/春日ツバキ/椿 (Blue Archive) This is the dataset of tsubaki/春日ツバキ/椿 (Blue Archive), containing 500 images and their tags. The core tags of this character are `black_hair, short_hair, animal_ears, breasts, large_breasts, hair_between_eyes, red_halo, halo, black_eyes, tassel, raccoon_ears`, 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 \| 500 \| 952.33 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsubaki_bluearchive/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 500 \| 784.27 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsubaki_bluearchive/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1379 \| 1.60 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/tsubaki_bluearchive/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/tsubaki_bluearchive', 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 \| 15 \| ![](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, blush, elbow_gloves, red_gloves, rope, solo, looking_at_viewer, red_sailor_collar, red_skirt, simple_background, white_background, armpits, arms_up, open_mouth, underboob, arms_behind_head, breast_curtain, sweat, sideboob, sideless_outfit \| \| 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, elbow_gloves, red_gloves, red_sailor_collar, red_skirt, revealing_clothes, sideboob, simple_background, solo, thighs, white_background, blush, looking_at_viewer, pleated_skirt, bare_shoulders, sideless_outfit, breast_curtain, underboob, shimenawa, two-tone_shirt \| \| 2 \| 14 \| ![](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) \| 1boy, 1girl, blush, hetero, penis, pussy, red_sailor_collar, vaginal, open_mouth, solo_focus, elbow_gloves, mosaic_censoring, red_gloves, spread_legs, huge_breasts, nipples, sweat, red_skirt, breast_curtain, clothed_sex, on_back, rope_belt, cowgirl_position, cum, girl_on_top, looking_at_viewer, on_bed, thighs, two-tone_shirt \| \| 3 \| 6 \| ![](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, bare_shoulders, cleavage, navel, open_mouth, stomach, blush, solo, alternate_costume, cowboy_shot, looking_at_viewer, wet, white_bikini, choker, collarbone, halterneck, red_flower, sarong, simple_background, smile, white_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| elbow_gloves \| red_gloves \| rope \| solo \| looking_at_viewer \| red_sailor_collar \| red_skirt \| simple_background \| white_background \| armpits \| arms_up \| open_mouth \| underboob \| arms_behind_head \| breast_curtain \| sweat \| sideboob \| sideless_outfit \| revealing_clothes \| thighs \| pleated_skirt \| bare_shoulders \| shimenawa \| two-tone_shirt \| 1boy \| hetero \| penis \| pussy \| vaginal \| solo_focus \| mosaic_censoring \| spread_legs \| huge_breasts \| nipples \| clothed_sex \| on_back \| rope_belt \| cowgirl_position \| cum \| girl_on_top \| on_bed \| cleavage \| navel \| stomach \| alternate_costume \| cowboy_shot \| wet \| white_bikini \| choker \| collarbone \| halterneck \| red_flower \| sarong \| smile \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:---------------\|:-------------\|:-------\|:-------\|:--------------------\|:--------------------\|:------------\|:--------------------\|:-------------------\|:----------\|:----------\|:-------------\|:------------\|:-------------------\|:-----------------\|:--------\|:-----------\|:------------------\|:--------------------\|:---------\|:----------------\|:-----------------\|:------------\|:-----------------\|:-------\|:---------\|:--------\|:--------\|:----------\|:-------------\|:-------------------\|:--------------\|:---------------\|:----------\|:--------------\|:----------\|:------------\|:-------------------\|:------\|:--------------\|:---------\|:-----------\|:--------\|:----------\|:--------------------\|:--------------\|:------\|:---------------\|:---------\|:-------------\|:-------------\|:-------------\|:---------\|:--------\| \| 0 \| 15 \| ![](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 \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 14 \| ![](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 \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](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 \| X \| X \| X \| X \| X \| X \|
Dogge/bluemoon-fandom-1-1-rp-cleaned-korean-tranlated	--- license: wtfpl ---
satendra4u2022/dpo	--- license: mit ---
tashkeela	--- annotations_creators: - no-annotation language_creators: - found language: - ar license: - gpl-2.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Tashkeela tags: - diacritics-prediction dataset_info: features: - name: text dtype: string - name: book dtype: string config_name: plain_text splits: - name: train num_bytes: 1081110249 num_examples: 97 download_size: 183393530 dataset_size: 1081110249 --- # Dataset Card for Tashkeela ## 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: [Tashkeela](https://sourceforge.net/projects/tashkeela/) - Repository: [Tashkeela](https://sourceforge.net/projects/tashkeela/) - Paper: [Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems](https://www.sciencedirect.com/science/article/pii/S2352340917300112) - Point of Contact: [Taha Zerrouki](mailto:t_zerrouki@esi.dz) ### Dataset Summary It contains 75 million of fully vocalized words mainly 97 books from classical and modern Arabic language. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is based on Arabic. ## Dataset Structure ### Data Instances ``` {'book': 'zip://Tashkeela-arabic-diacritized-text-utf8-0.3/texts.txt/msa/al-kalema.org/أشكال-التجارب-في-مَثَل-الزارع.htm.txt::https://sourceforge.net/projects/tashkeela/files/latest/download', 'text': 'الكلمة\n\n\nصفحه اصلی\nاشترك\nالكتاب المقدس\nجميع المقالات\nالترتيب بالموضوع\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nهذا المقال على نسخة PDF\n\n\nأشكال التجارب في مَثَل الزارع\n\n\tقد رأينا في مقال " \nوسائل واشكال التجارب" الأشكال التي من الممكن أن تتخذها التجارب (وخاصة الاختبارات التي تأتي من خلال الآلام والاضطهاد وأشراك إطاعة شهوات الإنسان العتيق، الجسد)، نستطيع أيضاً أن نرى هذه الأقسام عاملة في مثال الزارع. هناك مجموعتين في مثال الزارع أنه برغم من سماعهم واستقبالهم للكلمة، إلا أنهم لم يجلبوا ثماراً. والسؤال هو لماذا؟\n\n1. التجارب في القسم الثاني من مثال الزارع\n\nفيما يخص القسم الثاني من مثال الزارع، تخبرنا عنها متى 13: 20- 21 ولوقا 8: 13 \nمتى 13: 20- 21\n" وَالْمَزْرُوعُ عَلَى الأَمَاكِنِ الْمُحْجِرَةِ هُوَ الَّذِي يَسْمَعُ الْكَلِمَةَ، وَحَالاً يَقْبَلُهَا بِفَرَحٍ، وَلكِنْ لَيْسَ لَهُ أَصْلٌ فِي ذَاتِهِ، بَلْ هُوَ إِلَى حِينٍ. فَإِذَا حَدَثَ ضِيقٌ أَوِ اضْطِهَادٌ مِنْ أَجْلِ الْكَلِمَةِ فَحَالاً يَعْثُرُ."\nلوقا 8: 13\n" وَالَّذِينَ عَلَى الصَّخْرِ هُمُ الَّذِينَ مَتَى سَمِعُوا يَقْبَلُونَ الْكَلِمَةَ بِفَرَحٍ، وَهؤُلاَءِ لَيْسَ لَهُمْ أَصْلٌ، فَيُؤْمِنُونَ إِلَى حِينٍ، وَفِي وَقْتِ التَّجْرِبَةِ يَرْتَدُّونَ."\n\nكما نرى، الناس في هذا القسم سمعوا الكلمة وحالاً قبلوها بفرح! بمعنى آخر، لقد كانوا متحمسين جداً تجاه الكلمة. ثم جاءت التجارب والاختبارات في شكل ضيق واضطهاد من أجل الكلمة، أي أنه بسبب الكلمة، اضطهد هؤلاء الناس. وعندئذ توقفوا. عوضاً عن أن يحفظوا ويتمسكوا بالكلمة التي قد حدث واستقبلوها بفرح، تراجعوا وسقطوا بعيداً، إن كنت مؤمناً صغيراً مليء بالحماسة تجاه الله، وبالرغم من أنه قد يبدو أنه لا يوجد شيطان من حولك، فهذا لن يستمر إلى الأبد. فالتجارب والاختبارات آتية. ستحتاج إلى أن تحفظ وتتمسك بالإيمان وبالكلمة التي قد حدث واستقبلتها بفرح. كما تقول لنا الكلمة:\nعبرانيين 10: 35- 39\n" فَلاَ تَطْرَحُوا ثِقَتَكُمُ الَّتِي لَهَا مُجَازَاةٌ عَظِيمَةٌ. لأَنَّكُمْ تَحْتَاجُونَ إِلَى الصَّبْرِ، حَتَّى إِذَا صَنَعْتُمْ مَشِيئَةَ اللهِ تَنَالُونَ الْمَوْعِدَ. لأَنَّهُ بَعْدَ قَلِيل جِدًّا «سَيَأْتِي الآتِي وَلاَ يُبْطِئُ. أَمَّا الْبَارُّ فَبِالإِيمَانِ يَحْيَا، وَإِنِ ارْتَدَّ لاَ تُسَرُّ بِهِ نَفْسِي». وَأَمَّا نَحْنُ فَلَسْنَا مِنَ الارْتِدَادِ لِلْهَلاَكِ، بَلْ مِنَ الإِيمَانِ لاقْتِنَاءِ النَّفْسِ."\n\nوالضيق قد يأخذ أشكالاً عديدة. رأيت أناساً يسقطون، تاركين الإيمان لأن آبائهم أو أقاربهم وأصدقائهم قد عارضوهم ورفضوهم بسبب إيمانهم. بالطبع قد يأخذ الاضطهاد أشكالاً أكثر من ذلك أيضاً، مثل أن تلقى في سجن أو أن تعذب لأجل إيمانك. قد يسبب الموت كذلك، كما حدث مع اسطفانوس ويعقوب أخو يوحنا. وتقول الكلمة من أجلك ومن أجل كل الذين حوكموا:\nرومية 16: 19- 20\n" لأَنَّ طَاعَتَكُمْ ذَاعَتْ إِلَى الْجَمِيعِ، فَأَفْرَحُ أَنَا بِكُمْ، وَأُرِيدُ أَنْ تَكُونُوا حُكَمَاءَ لِلْخَيْرِ وَبُسَطَاءَ لِلشَّرِّ. وَإِلهُ السَّلاَمِ سَيَسْحَقُ الشَّيْطَانَ تَحْتَ أَرْجُلِكُمْ سَرِيعًا."\nو بطرس الأولى 5: 8- 10\n" اُصْحُوا وَاسْهَرُوا. لأَنَّ إِبْلِيسَ خَصْمَكُمْ كَأَسَدٍ زَائِرٍ، يَجُولُ مُلْتَمِسًا مَنْ يَبْتَلِعُهُ هُوَ. فَقَاوِمُوهُ، رَاسِخِينَ فِي الإِيمَانِ، عَالِمِينَ أَنَّ نَفْسَ هذِهِ الآلاَمِ تُجْرَى عَلَى إِخْوَتِكُمُ الَّذِينَ فِي الْعَالَمِ. وَإِلهُ كُلِّ نِعْمَةٍ الَّذِي دَعَانَا إِلَى مَجْدِهِ الأَبَدِيِّ فِي الْمَسِيحِ يَسُوعَ، بَعْدَمَا تَأَلَّمْتُمْ يَسِيرًا، هُوَ يُكَمِّلُكُمْ، وَيُثَبِّتُكُمْ، وَيُقَوِّيكُمْ، وَيُمَكِّنُكُمْ."\n\nتمسك بالإيمان حتى النهاية. ضع حياتك ووضعك بين يدي الله وكن مستعداً لمواجهة أي شيء قد يحدث، أجل وحتى السخرية والعذاب. الله معك، سيقويك وسيعينك تماماً مثلما فعل مع يسوع في بستان جسثيماني. وتماماً مثلما فعل مع بولس في السجن عندما اضطهد من قِبَل اليهود (أعمال الرسل 23: 11). وكما قال بولس في كورنثوس الثانية 1: 7:" عَالِمِينَ أَنَّكُمْ كَمَا أَنْتُمْ شُرَكَاءُ فِي الآلاَمِ، كَذلِكَ فِي التَّعْزِيَةِ أَيْضًا." فالعزاء الآتي من الله يوازن أي سخرية أو أي عذاب قد يأتي إلينا من أي إنسان.\n\n2. التجارب في القسم الثالث من مثال الزارع\n\nبخصوص القسم الثالث من مثال الزارع، فنقرأ عنه في مرقس 4: 18- 19\n\n" وَهؤُلاَءِ هُمُ الَّذِينَ زُرِعُوا بَيْنَ الشَّوْكِ: هؤُلاَءِ هُمُ الَّذِينَ يَسْمَعُونَ الْكَلِمَةَ، وَهُمُومُ هذَا الْعَالَمِ وَغُرُورُ الْغِنَى وَشَهَوَاتُ سَائِرِ الأَشْيَاءِ تَدْخُلُ وَتَخْنُقُ الْكَلِمَةَ فَتَصِيرُ بِلاَ ثَمَرٍ."\nو لوقا 8: 14\n" وَالَّذِي سَقَطَ بَيْنَ الشَّوْكِ هُمُ الَّذِينَ يَسْمَعُونَ، ثُمَّ يَذْهَبُونَ فَيَخْتَنِقُونَ مِنْ هُمُومِ الْحَيَاةِ وَغِنَاهَا وَلَذَّاتِهَا، وَلاَ يُنْضِجُونَ ثَمَرًا."\n\nهؤلاء قد سمعوا الكلمة وفهموها ولكنهم صاروا بلا ثمر، وما هو السبب؟ السبب هو لأنهم تركوا أبواب قلوبهم مفتوحة لأشواك " وَهُمُومُ هذَا الْعَالَمِ وَغُرُورُ الْغِنَى وَشَهَوَاتُ سَائِرِ الأَشْيَاءِ" (مرقس 4: 19)، والتي تدخل فتخنق الكلمة، كما رأينا يعقوب دائماً ما يقول:\nيعقوب 1: 13- 15\n" لاَ يَقُلْ أَحَدٌ إِذَا جُرِّبَ: «إِنِّي أُجَرَّبُ مِنْ قِبَلِ اللهِ»، لأَنَّ اللهَ غَيْرُ مُجَرَّبٍ بِالشُّرُورِ، وَهُوَ لاَ يُجَرِّبُ أَحَدًا. وَلكِنَّ كُلَّ وَاحِدٍ يُجَرَّبُ إِذَا انْجَذَبَ وَانْخَدَعَ مِنْ شَهْوَتِهِ. ثُمَّ الشَّهْوَةُ إِذَا حَبِلَتْ تَلِدُ خَطِيَّةً، وَالْخَطِيَّةُ إِذَا كَمَلَتْ تُنْتِجُ مَوْتًا."\nوتيموثاوس الأولى 6: 9 تقول لنا\n" وَأَمَّا الَّذِينَ يُرِيدُونَ أَنْ يَكُونُوا أَغْنِيَاءَ، فَيَسْقُطُونَ فِي تَجْرِبَةٍ وَفَخٍّ وَشَهَوَاتٍ كَثِيرَةٍ غَبِيَّةٍ وَمُضِرَّةٍ، تُغَرِّقُ النَّاسَ فِي الْعَطَبِ وَالْهَلاَكِ."\n\nيجب أن نلاحظ شيئاً هنا: أن تأثير هموم الحياة هو نفس التأثير الذي لتجارب الغنى وشهوات الأشياء الأخرى. فهموم الحياة أيضاً لا تجلب الثمار، إذاً فإن اردت أن تكون مسيحياً مثمراً، أي مسيحي حقيقي وليس فقط مسيحي اسمي، فيجب عليك أن تزيل أشواك الهموم والغنى وملذات الحياة وأن تمنعهم من العودة مرة أخرى. تحتاج إلى أن تفعل شيئاً، تحتاج إلى أن تتغير والله سيعينك في هذا إن كنت حقاً تريده. التجارب في القسم الثالث من مثال الزارع لا تأتي من خلال الاضطهاد والآلام عن طريق الشيطان. ولكن هنا تأخذ التجارب صوراً أكثر مكراً والتي مع هذا تتطلب مقاومتنا. الاهتمام بما يهتم به هذا العالم ("هموم هذا العالم")، الرغبة في الغنى أو اشتهاء الأشياء الأخرى هي أمور خطيرة جداً. إنها أشواك يجب إزالتها. كما رأينا بولس يقول:\nرومية 13: 14\n" بَلِ الْبَسُوا الرَّبَّ يَسُوعَ الْمَسِيحَ، وَلاَ تَصْنَعُوا تَدْبِيرًا لِلْجَسَدِ لأَجْلِ الشَّهَوَاتِ."\n\n" لاَ تَصْنَعُوا تَدْبِيرًا لِلْجَسَدِ" والتي تعني أنه يجب علينا أن لا نهتم بالجسد وشهواته. ولكن عوضاً عن ذلك ينبغي لنا أن نطعم أنفسنا بلبن الكلمة الصافي الذي ننمو بواستطه (بطرس الأولى 2: 2).\n\n\nتاسوس كيولاشوجلو'} ``` ### Data Fields - `book` (str): Book filename. - `text` (str): Text of the book. ### Data Splits The dataset is not split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization The Modern Standard Arabic texts crawled from the Internet. #### Who are the source language producers? Websites. ### Annotations The dataset does not contain any additional 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 [GNU General Public License, version 2 (GPLv2)](https://opensource.org/licenses/GPL-2.0). ### Citation Information The dataset was published on this [paper](https://www.sciencedirect.com/science/article/pii/S2352340917300112#!): ``` @article{zerrouki2017tashkeela, title={Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems}, author={Zerrouki, Taha and Balla, Amar}, journal={Data in brief}, volume={11}, pages={147}, year={2017}, publisher={Elsevier} } ``` ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset.
CyberHarem/nearl_arknights	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of nearl/ニアール/临光 (Arknights) This is the dataset of nearl/ニアール/临光 (Arknights), containing 353 images and their tags. The core tags of this character are `animal_ears, blonde_hair, long_hair, horse_ears, animal_ear_fluff, horse_girl, yellow_eyes, tail, ponytail, horse_tail, hair_between_eyes, breasts, large_breasts`, 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 \| 353 \| 649.88 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nearl_arknights/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 353 \| 537.44 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nearl_arknights/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 917 \| 1.03 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/nearl_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/nearl_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 \| 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, bare_shoulders, cowboy_shot, crop_top, looking_at_viewer, midriff, navel, solo, stomach, black_pants, black_sports_bra, leggings, simple_background, standing, sweat, alternate_costume, blush, thighs, white_background, armpits, arms_behind_head, arms_up, bare_arms, cropped_legs, parted_lips, smile, stretching, wristband \| \| 1 \| 5 \| ![](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_arms, bare_shoulders, cleavage, looking_at_viewer, midriff, navel, solo, stomach, black_shorts, short_shorts, simple_background, smile, standing, thighs, alternate_costume, black_sports_bra, cowboy_shot, crop_top, hand_up, medium_breasts, abs, bike_shorts, dolphin_shorts, parted_lips, very_long_hair, white_background \| \| 2 \| 7 \| ![](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, blush, looking_at_viewer, nipples, solo, collarbone, completely_nude, navel, pussy, cowboy_shot, standing, stomach, sidelocks, smile \| \| 3 \| 5 \| ![](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, blush, horse_penis, huge_penis, looking_at_viewer, solo, anus, ass, simple_background, from_behind, full-package_futanari, huge_breasts, huge_testicles, looking_back, nipples, pussy, uncensored, centaur, completely_nude, cum, erection, grey_background, monster_girl, multiple_legs, parted_lips, torn_clothes \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, bare_shoulders, looking_at_viewer, official_alternate_costume, outdoors, solo, swimsuit_cover-up, white_one-piece_swimsuit, cloud, covered_navel, cowboy_shot, off_shoulder, parted_lips, smile, blue_sky, competition_swimsuit, day, groin, medium_breasts, open_jacket, see-through, standing, thigh_strap, ass_visible_through_thighs, bird, blue_jacket, collarbone, eyewear_on_head, long_sleeves, sunglasses \| \| 5 \| 7 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, bare_shoulders, cowboy_shot, solo, thighs, white_one-piece_swimsuit, collarbone, competition_swimsuit, covered_navel, eyewear_on_head, looking_at_viewer, official_alternate_costume, standing, sunglasses, outdoors, water, arm_up, bare_arms, day, smile, blue_sky, blush, cloud, hand_up, swimsuit_cover-up, thigh_strap, wading, wet \| \| 6 \| 9 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, closed_mouth, headset, implied_extra_ears, looking_at_viewer, simple_background, solo, white_background, portrait, upper_body, smile, black_scarf, shoulder_armor \| \| 7 \| 15 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, breastplate, headset, solo, upper_body, pauldrons, scarf, sidelocks, simple_background, closed_mouth, white_background, looking_at_viewer, parted_lips \| \| 8 \| 6 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, headset, solo, weapon, breastplate, holding, looking_at_viewer, orange_eyes, pauldrons, scarf, shield, sidelocks, upper_body, v-shaped_eyebrows, open_mouth \| \| 9 \| 19 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, solo, breastplate, headset, black_gloves, looking_at_viewer, pauldrons, sidelocks, holding_weapon, holding_shield, black_scarf, cowboy_shot, black_dress, closed_mouth, headphones \| \| 10 \| 14 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| 1girl, breastplate, full_body, pauldrons, solo, black_gloves, headset, looking_at_viewer, standing, black_dress, black_footwear, sidelocks, black_skirt, high_heel_boots, black_scarf, simple_background, holding_weapon, torn_clothes, holding_shield, torn_scarf, closed_mouth, white_background, axe, floating_hair \| \| 11 \| 5 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| 1girl, headset, looking_at_viewer, shoulder_armor, solo, black_gloves, holding_weapon, implied_extra_ears, upper_body, belt \| \| 12 \| 7 \| ![](samples/12/clu12-sample0.png) \| ![](samples/12/clu12-sample1.png) \| ![](samples/12/clu12-sample2.png) \| ![](samples/12/clu12-sample3.png) \| ![](samples/12/clu12-sample4.png) \| 1girl, headset, implied_extra_ears, looking_at_viewer, solo, white_dress, black_gloves, official_alternate_costume, cowboy_shot, holding_weapon, simple_background, belt, shoulder_armor, black_background, medium_breasts, orange_eyes, polearm \| \| 13 \| 6 \| ![](samples/13/clu13-sample0.png) \| ![](samples/13/clu13-sample1.png) \| ![](samples/13/clu13-sample2.png) \| ![](samples/13/clu13-sample3.png) \| ![](samples/13/clu13-sample4.png) \| 1girl, headset, holding_polearm, implied_extra_ears, solo, armored_boots, looking_at_viewer, shoulder_armor, standing, white_dress, black_gloves, full_body, spear, outdoors, white_coat \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| cowboy_shot \| crop_top \| looking_at_viewer \| midriff \| navel \| solo \| stomach \| black_pants \| black_sports_bra \| leggings \| simple_background \| standing \| sweat \| alternate_costume \| blush \| thighs \| white_background \| armpits \| arms_behind_head \| arms_up \| bare_arms \| cropped_legs \| parted_lips \| smile \| stretching \| wristband \| cleavage \| black_shorts \| short_shorts \| hand_up \| medium_breasts \| abs \| bike_shorts \| dolphin_shorts \| very_long_hair \| nipples \| collarbone \| completely_nude \| pussy \| sidelocks \| horse_penis \| huge_penis \| anus \| ass \| from_behind \| full-package_futanari \| huge_breasts \| huge_testicles \| looking_back \| uncensored \| centaur \| cum \| erection \| grey_background \| monster_girl \| multiple_legs \| torn_clothes \| official_alternate_costume \| outdoors \| swimsuit_cover-up \| white_one-piece_swimsuit \| cloud \| covered_navel \| off_shoulder \| blue_sky \| competition_swimsuit \| day \| groin \| open_jacket \| see-through \| thigh_strap \| ass_visible_through_thighs \| bird \| blue_jacket \| eyewear_on_head \| long_sleeves \| sunglasses \| water \| arm_up \| wading \| wet \| closed_mouth \| headset \| implied_extra_ears \| portrait \| upper_body \| black_scarf \| shoulder_armor \| breastplate \| pauldrons \| scarf \| weapon \| holding \| orange_eyes \| shield \| v-shaped_eyebrows \| open_mouth \| black_gloves \| holding_weapon \| holding_shield \| black_dress \| headphones \| full_body \| black_footwear \| black_skirt \| high_heel_boots \| torn_scarf \| axe \| floating_hair \| belt \| white_dress \| black_background \| polearm \| holding_polearm \| armored_boots \| spear \| white_coat \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:-----------------\|:--------------\|:-----------\|:--------------------\|:----------\|:--------\|:-------\|:----------\|:--------------\|:-------------------\|:-----------\|:--------------------\|:-----------\|:--------\|:--------------------\|:--------\|:---------\|:-------------------\|:----------\|:-------------------\|:----------\|:------------\|:---------------\|:--------------\|:--------\|:-------------\|:------------\|:-----------\|:---------------\|:---------------\|:----------\|:-----------------\|:------\|:--------------\|:-----------------\|:-----------------\|:----------\|:-------------\|:------------------\|:--------\|:------------\|:--------------\|:-------------\|:-------\|:------\|:--------------\|:------------------------\|:---------------\|:-----------------\|:---------------\|:-------------\|:----------\|:------\|:-----------\|:------------------\|:---------------\|:----------------\|:---------------\|:-----------------------------\|:-----------\|:--------------------\|:---------------------------\|:--------\|:----------------\|:---------------\|:-----------\|:-----------------------\|:------\|:--------\|:--------------\|:--------------\|:--------------\|:-----------------------------\|:-------\|:--------------\|:------------------\|:---------------\|:-------------\|:--------\|:---------\|:---------\|:------\|:---------------\|:----------\|:---------------------\|:-----------\|:-------------\|:--------------\|:-----------------\|:--------------\|:------------\|:--------\|:---------\|:----------\|:--------------\|:---------\|:--------------------\|:-------------\|:---------------\|:-----------------\|:-----------------\|:--------------\|:-------------\|:------------\|:-----------------\|:--------------\|:------------------\|:-------------\|:------\|:----------------\|:-------\|:--------------\|:-------------------\|:----------\|:------------------\|:----------------\|:--------\|:-------------\| \| 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 \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](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 \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 7 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 5 \| ![](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 \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| X \| \| X \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 7 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| \| X \| \| \| X \| \| \| \| \| \| X \| \| \| X \| X \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| \| \| \| X \| \| \| \| X \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 9 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| \| \| X \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 15 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| \| \| X \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| X \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 6 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 19 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| X \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| X \| \| X \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 14 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| X \| \| \| \| X \| \| \| X \| \| \| \| \| X \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| X \| \| X \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| 11 \| 5 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| X \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| 12 \| 7 \| ![](samples/12/clu12-sample0.png) \| ![](samples/12/clu12-sample1.png) \| ![](samples/12/clu12-sample2.png) \| ![](samples/12/clu12-sample3.png) \| ![](samples/12/clu12-sample4.png) \| X \| \| X \| \| X \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| 13 \| 6 \| ![](samples/13/clu13-sample0.png) \| ![](samples/13/clu13-sample1.png) \| ![](samples/13/clu13-sample2.png) \| ![](samples/13/clu13-sample3.png) \| ![](samples/13/clu13-sample4.png) \| X \| \| \| \| X \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \|
simonveitner/samsum_rewritten_0_to_1200	--- dataset_info: features: - name: orginal_text dtype: string - name: rewrite_prompt dtype: string - name: rewritten_text dtype: string splits: - name: train num_bytes: 421524 num_examples: 1200 download_size: 263197 dataset_size: 421524 configs: - config_name: default data_files: - split: train path: data/train-* ---
ZhangShenao/0.00045_idpo_decalpha_ref_response	--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: reference_response dtype: string splits: - name: train_prefs_1 num_bytes: 164111773 num_examples: 20378 - name: test_prefs_1 num_bytes: 16019213 num_examples: 2000 download_size: 99390696 dataset_size: 180130986 configs: - config_name: default data_files: - split: train_prefs_1 path: data/train_prefs_1-* - split: test_prefs_1 path: data/test_prefs_1-* --- # Dataset Card for "0.00045_idpo_decalpha_ref_response" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
fathyshalab/reklambox-balanced-no-stopwords	--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: label_name dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 401120 num_examples: 1102 - name: test num_bytes: 140041 num_examples: 276 download_size: 335528 dataset_size: 541161 --- # Dataset Card for "reklambox-balanced-no-stopwords" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/matsuda_arisa_theidolmstermillionlive	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of matsuda_arisa/松田亜利沙 (THE iDOLM@STER: Million Live!) This is the dataset of matsuda_arisa/松田亜利沙 (THE iDOLM@STER: Million Live!), containing 134 images and their tags. The core tags of this character are `brown_hair, twintails, long_hair, brown_eyes, bangs`, 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 \| 134 \| 132.08 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/matsuda_arisa_theidolmstermillionlive/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 134 \| 91.51 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/matsuda_arisa_theidolmstermillionlive/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 280 \| 175.71 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/matsuda_arisa_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 134 \| 120.99 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/matsuda_arisa_theidolmstermillionlive/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 280 \| 221.66 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/matsuda_arisa_theidolmstermillionlive/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/matsuda_arisa_theidolmstermillionlive', 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 \| 6 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, looking_at_viewer, open_mouth, skirt, solo, :d, blush, boots, hair_bow, jewelry \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| open_mouth \| skirt \| solo \| :d \| blush \| boots \| hair_bow \| jewelry \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------------\|:--------\|:-------\|:-----\|:--------\|:--------\|:-----------\|:----------\| \| 0 \| 6 \| ![](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 \|
mangostin2010/Korean-Wise-Saying	--- license: unknown ---
hirxn/custom_data	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 16378 num_examples: 7 download_size: 15015 dataset_size: 16378 configs: - config_name: default data_files: - split: train path: data/train-* ---
BangumiBase/mawarupenguindrum	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Mawaru Penguindrum This is the image base of bangumi Mawaru Penguindrum, we detected 23 characters, 1725 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 19 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 177 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 81 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 18 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 76 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 206 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 19 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 14 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 64 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 11 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 313 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 24 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 11 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 306 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 19 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 19 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 13 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 16 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 37 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 17 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 17 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 8 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| noise \| 240 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|
memray/kp20k	--- license: cc-by-nc-sa-4.0 ---
crylake/facesyntheticsspigacaptioned_30percent	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: spiga_seg dtype: image - name: image_caption dtype: string splits: - name: train num_bytes: 9080640177.0 num_examples: 30000 download_size: 9066954510 dataset_size: 9080640177.0 --- # Dataset Card for "facesyntheticsspigacaptioned_30percent" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
indiejoseph/wikipedia-zh-filtered	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 258992903 num_examples: 44344 download_size: 164712496 dataset_size: 258992903 --- # Dataset Card for "wikipedia-zh-filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rpadilla/ft-capstone2	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 36223 num_examples: 12 - name: test num_bytes: 23728 num_examples: 7 download_size: 57751 dataset_size: 59951 --- # Dataset Card for "ft-capstone2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 581821016 num_examples: 11666570 - name: validation num_bytes: 56120230 num_examples: 1026747 download_size: 421803322 dataset_size: 637941246 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---
nakayama/hh-rlhf-helpful-base-ja	--- license: mit language: - ja --- https://github.com/anthropics/hh-rlhf　の内容のうち、helpful-base内のchosenに記載されている英文をfuguMTで翻訳、うまく翻訳できていないものを除外、修正したものです。
gsh3729/sw2	--- dataset_info: features: - name: filename dtype: string - name: tif dtype: binary - name: tfw dtype: binary splits: - name: train num_bytes: 22663 num_examples: 2 download_size: 23838 dataset_size: 22663 configs: - config_name: default data_files: - split: train path: data/train-* ---
kyusque/github-issues	--- dataset_info: features: - name: url dtype: string - name: repository_url dtype: string - name: labels_url dtype: string - name: comments_url dtype: string - name: events_url dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - 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: 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: state dtype: string - name: locked dtype: bool - 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: 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: milestone struct: - name: closed_at dtype: string - 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: string - 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: comments sequence: string - name: created_at dtype: timestamp[ns, tz=UTC] - name: updated_at dtype: timestamp[ns, tz=UTC] - name: closed_at dtype: timestamp[ns, tz=UTC] - name: author_association dtype: string - name: active_lock_reason dtype: float64 - name: draft dtype: float64 - 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: body dtype: 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: timeline_url dtype: string - name: performed_via_github_app dtype: float64 - name: state_reason dtype: string - name: is_pull_request dtype: bool splits: - name: train num_bytes: 30854747 num_examples: 6106 download_size: 8722074 dataset_size: 30854747 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dinaaaaaa/lima_rand_sel_50_preference_self_reward	--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: chosen-rating dtype: int64 - name: rejected dtype: string - name: rejected-rating dtype: int64 splits: - name: train num_bytes: 177447 num_examples: 223 download_size: 44667 dataset_size: 177447 configs: - config_name: default data_files: - split: train path: data/train-* ---
automated-research-group/llama2_7b_chat-commonsense_qa-results	--- dataset_info: config_name: '{''do_sample''=False, ''beams''=1}' features: - name: id dtype: string - name: prediction dtype: string - name: commonsense_qa_accuracy dtype: bool splits: - name: train num_bytes: 159243 num_examples: 1221 download_size: 83126 dataset_size: 159243 configs: - config_name: '{''do_sample''=False, ''beams''=1}' data_files: - split: train path: '{''do_sample''=False, ''beams''=1}/train-*' ---
avankumar/new_data_model_methanol_lca_500	--- dataset_info: features: - name: Train dtype: string splits: - name: train num_bytes: 378305 num_examples: 502 download_size: 145305 dataset_size: 378305 configs: - config_name: default data_files: - split: train path: data/train-* ---
ImperialIndians23/nlp_cw_data_processed	--- dataset_info: features: - name: par_id dtype: string - name: community dtype: string - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1775055 num_examples: 8375 - name: valid num_bytes: 435628 num_examples: 2094 download_size: 1354140 dataset_size: 2210683 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* ---
sudeepag/sampled-t0_fsnoopt_data	--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: _template_idx dtype: int64 - name: _task_source dtype: string - name: _task_name dtype: string - name: _template_type dtype: string splits: - name: train num_bytes: 7365274691.188863 num_examples: 3219106 download_size: 4167856318 dataset_size: 7365274691.188863 configs: - config_name: default data_files: - split: train path: data/train-* ---
NebulaeWis/gelbooru_images	--- task_categories: - text-to-image language: - en pretty_name: gelbooru size_categories: - 1M<n<10M --- Collect images from https://gelbooru.com/ id range:0~9393795 encoding: UTF-8 search tags:"-animated -3d_(artwork) -webm -gif -video -real_life -comic -photo_(medium)" max shortest edge size ==1536 ,save using .webp with 90%quality The total number search iamges is 8364374, filtered out 18832. image not in it: gif/video truncated(more than 10+ repeat download) too large(over pillow default limit pixels) In the metainfo last 5 columns,[artist,character,copyright,metadata,tags],"None" means lack of anything, rather than string "None". *.txt from the crawler results,it's' not captions. please build captions from metainfo and tagger Disclaimer Disclaimer: By downloading or using this dataset, you agree to the following terms and conditions: Purpose of Crawling: The dataset is obtained by crawling a publicly available website. The purpose of this crawling behavior is to upload the dataset to Hugging Face in order to alleviate the load on the original booru site. Data Accuracy: We make efforts to ensure the accuracy of the dataset, but we cannot guarantee the completeness and accuracy of the data. Users are responsible for evaluating the quality and accuracy of the dataset and bear any consequences arising from inaccurate or incomplete data. Full Responsibility: The uploader of this dataset shall not be liable for any losses or damages (including but not limited to any direct, indirect, incidental damages) arising from the use, misuse, or inability to use the dataset in any way. Please read and understand the above terms and conditions carefully before using this dataset. If you do not agree to these terms and conditions, you are not allowed to use this dataset.
breno30/PhPacote	--- license: openrail ---
AdapterOcean/med_alpaca_standardized_cluster_19_std	--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 18706021 num_examples: 34698 download_size: 9286969 dataset_size: 18706021 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_19_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
StevenLe456/viet-tones	--- dataset_info: features: - name: audio dtype: audio - name: label dtype: int64 splits: - name: train num_bytes: 177262390.44 num_examples: 1080 download_size: 0 dataset_size: 177262390.44 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "viet-tones" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_seyf1elislam__KuTrix-7b	--- pretty_name: Evaluation run of seyf1elislam/KuTrix-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [seyf1elislam/KuTrix-7b](https://huggingface.co/seyf1elislam/KuTrix-7b) 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_seyf1elislam__KuTrix-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-16T06:30:17.941313](https://huggingface.co/datasets/open-llm-leaderboard/details_seyf1elislam__KuTrix-7b/blob/main/results_2024-03-16T06-30-17.941313.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\": 0.6576587418096124,\n\ \ \"acc_stderr\": 0.03201598935752146,\n \"acc_norm\": 0.6575488737220112,\n\ \ \"acc_norm_stderr\": 0.03267746108371606,\n \"mc1\": 0.5410036719706243,\n\ \ \"mc1_stderr\": 0.017444544447661203,\n \"mc2\": 0.7084705277313671,\n\ \ \"mc2_stderr\": 0.014694482049743158\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6834470989761092,\n \"acc_stderr\": 0.013592431519068077,\n\ \ \"acc_norm\": 0.7047781569965871,\n \"acc_norm_stderr\": 0.013329750293382318\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7027484564827724,\n\ \ \"acc_stderr\": 0.004561141293448457,\n \"acc_norm\": 0.8794064927305317,\n\ \ \"acc_norm_stderr\": 0.0032498873947065044\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6444444444444445,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569526,\n\ \ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569526\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\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6763005780346821,\n\ \ \"acc_stderr\": 0.035676037996391706,\n \"acc_norm\": 0.6763005780346821,\n\ \ \"acc_norm_stderr\": 0.035676037996391706\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\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\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\ \ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\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\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440678,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440678\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.46825396825396826,\n\ \ \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n\ \ \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7838709677419354,\n\ \ \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n\ \ \"acc_norm_stderr\": 0.02341529343356853\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\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\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\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n\ \ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.337037037037037,\n \"acc_stderr\": 0.02882088466625326,\n \ \ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.02882088466625326\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977934,\n\ \ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977934\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8532110091743119,\n \"acc_stderr\": 0.015173141845126243,\n \"\ acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.015173141845126243\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.02450980392156861,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.02450980392156861\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \ \ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037181\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\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.03989139859531771,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.03989139859531771\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8675213675213675,\n\ \ \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n\ \ \"acc_norm_stderr\": 0.022209309073165616\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8301404853128991,\n\ \ \"acc_stderr\": 0.013428186370608303,\n \"acc_norm\": 0.8301404853128991,\n\ \ \"acc_norm_stderr\": 0.013428186370608303\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.02402774515526502,\n\ \ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.02402774515526502\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.44581005586592176,\n\ \ \"acc_stderr\": 0.016623998513333103,\n \"acc_norm\": 0.44581005586592176,\n\ \ \"acc_norm_stderr\": 0.016623998513333103\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7091503267973857,\n \"acc_stderr\": 0.02600480036395213,\n\ \ \"acc_norm\": 0.7091503267973857,\n \"acc_norm_stderr\": 0.02600480036395213\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.02378858355165854,\n\ \ \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 0.02378858355165854\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4804432855280313,\n\ \ \"acc_stderr\": 0.012760464028289299,\n \"acc_norm\": 0.4804432855280313,\n\ \ \"acc_norm_stderr\": 0.012760464028289299\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \ \ \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.02815637344037142\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.684640522875817,\n \"acc_stderr\": 0.018798086284886887,\n \ \ \"acc_norm\": 0.684640522875817,\n \"acc_norm_stderr\": 0.018798086284886887\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399677,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399677\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061456,\n\ \ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061456\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5410036719706243,\n\ \ \"mc1_stderr\": 0.017444544447661203,\n \"mc2\": 0.7084705277313671,\n\ \ \"mc2_stderr\": 0.014694482049743158\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.819258089976322,\n \"acc_stderr\": 0.010814911009613981\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7005307050796058,\n \ \ \"acc_stderr\": 0.012616300735519665\n }\n}\n```" repo_url: https://huggingface.co/seyf1elislam/KuTrix-7b 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_16T06_30_17.941313 path: - '/details_harness\|arc:challenge\|25_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-16T06-30-17.941313.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|gsm8k\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hellaswag\|10_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-16T06-30-17.941313.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-16T06-30-17.941313.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '/details_harness\|winogrande\|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-16T06-30-17.941313.parquet' - config_name: results data_files: - split: 2024_03_16T06_30_17.941313 path: - results_2024-03-16T06-30-17.941313.parquet - split: latest path: - results_2024-03-16T06-30-17.941313.parquet --- # Dataset Card for Evaluation run of seyf1elislam/KuTrix-7b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [seyf1elislam/KuTrix-7b](https://huggingface.co/seyf1elislam/KuTrix-7b) 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_seyf1elislam__KuTrix-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-16T06:30:17.941313](https://huggingface.co/datasets/open-llm-leaderboard/details_seyf1elislam__KuTrix-7b/blob/main/results_2024-03-16T06-30-17.941313.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": 0.6576587418096124, "acc_stderr": 0.03201598935752146, "acc_norm": 0.6575488737220112, "acc_norm_stderr": 0.03267746108371606, "mc1": 0.5410036719706243, "mc1_stderr": 0.017444544447661203, "mc2": 0.7084705277313671, "mc2_stderr": 0.014694482049743158 }, "harness\|arc:challenge\|25": { "acc": 0.6834470989761092, "acc_stderr": 0.013592431519068077, "acc_norm": 0.7047781569965871, "acc_norm_stderr": 0.013329750293382318 }, "harness\|hellaswag\|10": { "acc": 0.7027484564827724, "acc_stderr": 0.004561141293448457, "acc_norm": 0.8794064927305317, "acc_norm_stderr": 0.0032498873947065044 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7056603773584905, "acc_stderr": 0.02804918631569526, "acc_norm": 0.7056603773584905, "acc_norm_stderr": 0.02804918631569526 }, "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": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6763005780346821, "acc_stderr": 0.035676037996391706, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.035676037996391706 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "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": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "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": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42328042328042326, "acc_stderr": 0.02544636563440678, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.02544636563440678 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.46825396825396826, "acc_stderr": 0.04463112720677172, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677172 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "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": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "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": 0.8860103626943006, "acc_stderr": 0.022935144053919443, "acc_norm": 0.8860103626943006, "acc_norm_stderr": 0.022935144053919443 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563976, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563976 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.337037037037037, "acc_stderr": 0.02882088466625326, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.02882088466625326 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6890756302521008, "acc_stderr": 0.030066761582977934, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.030066761582977934 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8532110091743119, "acc_stderr": 0.015173141845126243, "acc_norm": 0.8532110091743119, "acc_norm_stderr": 0.015173141845126243 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8578431372549019, "acc_stderr": 0.02450980392156861, "acc_norm": 0.8578431372549019, "acc_norm_stderr": 0.02450980392156861 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8143459915611815, "acc_stderr": 0.025310495376944856, "acc_norm": 0.8143459915611815, "acc_norm_stderr": 0.025310495376944856 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037181, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037181 }, "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": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7961165048543689, "acc_stderr": 0.03989139859531771, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.03989139859531771 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165616, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165616 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8301404853128991, "acc_stderr": 0.013428186370608303, "acc_norm": 0.8301404853128991, "acc_norm_stderr": 0.013428186370608303 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7254335260115607, "acc_stderr": 0.02402774515526502, "acc_norm": 0.7254335260115607, "acc_norm_stderr": 0.02402774515526502 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.44581005586592176, "acc_stderr": 0.016623998513333103, "acc_norm": 0.44581005586592176, "acc_norm_stderr": 0.016623998513333103 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7091503267973857, "acc_stderr": 0.02600480036395213, "acc_norm": 0.7091503267973857, "acc_norm_stderr": 0.02600480036395213 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7592592592592593, "acc_stderr": 0.02378858355165854, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.02378858355165854 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5070921985815603, "acc_stderr": 0.02982449855912901, "acc_norm": 0.5070921985815603, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4804432855280313, "acc_stderr": 0.012760464028289299, "acc_norm": 0.4804432855280313, "acc_norm_stderr": 0.012760464028289299 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6875, "acc_stderr": 0.02815637344037142, "acc_norm": 0.6875, "acc_norm_stderr": 0.02815637344037142 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.684640522875817, "acc_stderr": 0.018798086284886887, "acc_norm": 0.684640522875817, "acc_norm_stderr": 0.018798086284886887 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.746938775510204, "acc_stderr": 0.027833023871399677, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.027833023871399677 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.847953216374269, "acc_stderr": 0.027539122889061456, "acc_norm": 0.847953216374269, "acc_norm_stderr": 0.027539122889061456 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5410036719706243, "mc1_stderr": 0.017444544447661203, "mc2": 0.7084705277313671, "mc2_stderr": 0.014694482049743158 }, "harness\|winogrande\|5": { "acc": 0.819258089976322, "acc_stderr": 0.010814911009613981 }, "harness\|gsm8k\|5": { "acc": 0.7005307050796058, "acc_stderr": 0.012616300735519665 } } ``` ## 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]
DeepFoldProtein/openfold_msa_contrastive_cards_000	--- dataset_info: features: - name: query_accession dtype: string - name: excludes sequence: string - name: query_sequence dtype: string - name: target_accessions sequence: string - name: target_sequences sequence: string splits: - name: train num_bytes: 1368566426 num_examples: 259752 download_size: 1342381431 dataset_size: 1368566426 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "openfold_msa_contrastive_cards_000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Lancelot53/srbd1_v2_annotated	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: xml dtype: string - name: html dtype: string - name: response dtype: string - name: annotated dtype: string splits: - name: train num_bytes: 29595348.121978022 num_examples: 1077 download_size: 3598400 dataset_size: 29595348.121978022 --- # Dataset Card for "srbd1_v2_annotated" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gokuls/wiki_book_corpus_processed_bert_dataset_small	--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 5550400800.0 num_examples: 1541778 download_size: 1636779213 dataset_size: 5550400800.0 --- # Dataset Card for "wiki_book_corpus_processed_bert_dataset_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_1880	--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_dtd_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 852862 num_examples: 1880 - name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 1030510 num_examples: 1880 download_size: 477779 dataset_size: 1883372 --- # Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-eval-samsum-samsum-1bb2ba-1486554327	--- type: predictions tags: - autotrain - evaluation datasets: - samsum eval_info: task: summarization model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum metrics: [] dataset_name: samsum dataset_config: samsum dataset_split: validation col_mapping: text: dialogue target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum * Dataset: samsum * Config: samsum * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@samuelallen123](https://huggingface.co/samuelallen123) for evaluating this model.
Dahoas/unet-cifar10-32	--- dataset_info: features: - name: images sequence: sequence: sequence: uint8 splits: - name: train num_bytes: 7110656 num_examples: 2048 download_size: 6350172 dataset_size: 7110656 --- # Dataset Card for "unet-cifar10-32" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_xxyyy123__10k_v1_lora_qkvo_rank14_v3	--- pretty_name: Evaluation run of xxyyy123/10k_v1_lora_qkvo_rank14_v3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [xxyyy123/10k_v1_lora_qkvo_rank14_v3](https://huggingface.co/xxyyy123/10k_v1_lora_qkvo_rank14_v3)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 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 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_xxyyy123__10k_v1_lora_qkvo_rank14_v3\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-09-03T13:17:02.987872](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__10k_v1_lora_qkvo_rank14_v3/blob/main/results_2023-09-03T13%3A17%3A02.987872.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\": 0.5091352266849982,\n\ \ \"acc_stderr\": 0.03495474191892426,\n \"acc_norm\": 0.5128128131582483,\n\ \ \"acc_norm_stderr\": 0.03493935725866389,\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5344202990692574,\n\ \ \"mc2_stderr\": 0.015729161957393895\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5298634812286689,\n \"acc_stderr\": 0.014585305840007105,\n\ \ \"acc_norm\": 0.5597269624573379,\n \"acc_norm_stderr\": 0.01450676952480424\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6050587532364071,\n\ \ \"acc_stderr\": 0.004878390226591715,\n \"acc_norm\": 0.7921728739294961,\n\ \ \"acc_norm_stderr\": 0.00404923158643323\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.48148148148148145,\n\ \ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.48148148148148145,\n\ \ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04068942293855797,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04068942293855797\n },\n\ \ \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.49,\n \ \ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.5584905660377358,\n \"acc_stderr\": 0.030561590426731833,\n\ \ \"acc_norm\": 0.5584905660377358,\n \"acc_norm_stderr\": 0.030561590426731833\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.5486111111111112,\n\ \ \"acc_stderr\": 0.041614023984032786,\n \"acc_norm\": 0.5486111111111112,\n\ \ \"acc_norm_stderr\": 0.041614023984032786\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.39,\n\ \ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.45664739884393063,\n\ \ \"acc_stderr\": 0.03798106566014498,\n \"acc_norm\": 0.45664739884393063,\n\ \ \"acc_norm_stderr\": 0.03798106566014498\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n\ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n\ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.46808510638297873,\n \"acc_stderr\": 0.03261936918467382,\n\ \ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.03261936918467382\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.34210526315789475,\n\ \ \"acc_stderr\": 0.04462917535336936,\n \"acc_norm\": 0.34210526315789475,\n\ \ \"acc_norm_stderr\": 0.04462917535336936\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\ \ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.29365079365079366,\n \"acc_stderr\": 0.023456037383982022,\n \"\ acc_norm\": 0.29365079365079366,\n \"acc_norm_stderr\": 0.023456037383982022\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2698412698412698,\n\ \ \"acc_stderr\": 0.03970158273235173,\n \"acc_norm\": 0.2698412698412698,\n\ \ \"acc_norm_stderr\": 0.03970158273235173\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5451612903225806,\n\ \ \"acc_stderr\": 0.028327743091561077,\n \"acc_norm\": 0.5451612903225806,\n\ \ \"acc_norm_stderr\": 0.028327743091561077\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.3793103448275862,\n \"acc_stderr\": 0.034139638059062345,\n\ \ \"acc_norm\": 0.3793103448275862,\n \"acc_norm_stderr\": 0.034139638059062345\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\"\ : 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885415,\n\ \ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885415\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.6363636363636364,\n \"acc_stderr\": 0.03427308652999934,\n \"\ acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.03427308652999934\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.7253886010362695,\n \"acc_stderr\": 0.03221024508041153,\n\ \ \"acc_norm\": 0.7253886010362695,\n \"acc_norm_stderr\": 0.03221024508041153\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.4948717948717949,\n \"acc_stderr\": 0.02534967290683866,\n \ \ \"acc_norm\": 0.4948717948717949,\n \"acc_norm_stderr\": 0.02534967290683866\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275805,\n \ \ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275805\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.5126050420168067,\n \"acc_stderr\": 0.03246816765752174,\n \ \ \"acc_norm\": 0.5126050420168067,\n \"acc_norm_stderr\": 0.03246816765752174\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"\ acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7211009174311926,\n \"acc_stderr\": 0.0192274688764635,\n \"acc_norm\"\ : 0.7211009174311926,\n \"acc_norm_stderr\": 0.0192274688764635\n },\n\ \ \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.4166666666666667,\n\ \ \"acc_stderr\": 0.03362277436608043,\n \"acc_norm\": 0.4166666666666667,\n\ \ \"acc_norm_stderr\": 0.03362277436608043\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\"\ : {\n \"acc\": 0.696078431372549,\n \"acc_stderr\": 0.03228210387037892,\n\ \ \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.03228210387037892\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7341772151898734,\n \"acc_stderr\": 0.02875679962965834,\n \ \ \"acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.02875679962965834\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.5515695067264574,\n\ \ \"acc_stderr\": 0.03337883736255098,\n \"acc_norm\": 0.5515695067264574,\n\ \ \"acc_norm_stderr\": 0.03337883736255098\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6030534351145038,\n \"acc_stderr\": 0.04291135671009224,\n\ \ \"acc_norm\": 0.6030534351145038,\n \"acc_norm_stderr\": 0.04291135671009224\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7024793388429752,\n \"acc_stderr\": 0.04173349148083499,\n \"\ acc_norm\": 0.7024793388429752,\n \"acc_norm_stderr\": 0.04173349148083499\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6388888888888888,\n\ \ \"acc_stderr\": 0.04643454608906275,\n \"acc_norm\": 0.6388888888888888,\n\ \ \"acc_norm_stderr\": 0.04643454608906275\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.5398773006134969,\n \"acc_stderr\": 0.03915857291436971,\n\ \ \"acc_norm\": 0.5398773006134969,\n \"acc_norm_stderr\": 0.03915857291436971\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.3392857142857143,\n\ \ \"acc_stderr\": 0.04493949068613539,\n \"acc_norm\": 0.3392857142857143,\n\ \ \"acc_norm_stderr\": 0.04493949068613539\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7649572649572649,\n\ \ \"acc_stderr\": 0.027778835904935434,\n \"acc_norm\": 0.7649572649572649,\n\ \ \"acc_norm_stderr\": 0.027778835904935434\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7037037037037037,\n\ \ \"acc_stderr\": 0.016328814422102052,\n \"acc_norm\": 0.7037037037037037,\n\ \ \"acc_norm_stderr\": 0.016328814422102052\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5664739884393064,\n \"acc_stderr\": 0.026680134761679214,\n\ \ \"acc_norm\": 0.5664739884393064,\n \"acc_norm_stderr\": 0.026680134761679214\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2547486033519553,\n\ \ \"acc_stderr\": 0.014572650383409155,\n \"acc_norm\": 0.2547486033519553,\n\ \ \"acc_norm_stderr\": 0.014572650383409155\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.5359477124183006,\n \"acc_stderr\": 0.02855582751652878,\n\ \ \"acc_norm\": 0.5359477124183006,\n \"acc_norm_stderr\": 0.02855582751652878\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.5819935691318328,\n\ \ \"acc_stderr\": 0.028013651891995072,\n \"acc_norm\": 0.5819935691318328,\n\ \ \"acc_norm_stderr\": 0.028013651891995072\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.5524691358024691,\n \"acc_stderr\": 0.027667138569422704,\n\ \ \"acc_norm\": 0.5524691358024691,\n \"acc_norm_stderr\": 0.027667138569422704\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.36879432624113473,\n \"acc_stderr\": 0.028782227561347243,\n \ \ \"acc_norm\": 0.36879432624113473,\n \"acc_norm_stderr\": 0.028782227561347243\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3820078226857888,\n\ \ \"acc_stderr\": 0.012409564470235567,\n \"acc_norm\": 0.3820078226857888,\n\ \ \"acc_norm_stderr\": 0.012409564470235567\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.48161764705882354,\n \"acc_stderr\": 0.030352303395351964,\n\ \ \"acc_norm\": 0.48161764705882354,\n \"acc_norm_stderr\": 0.030352303395351964\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.4820261437908497,\n \"acc_stderr\": 0.020214761037872404,\n \ \ \"acc_norm\": 0.4820261437908497,\n \"acc_norm_stderr\": 0.020214761037872404\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5818181818181818,\n\ \ \"acc_stderr\": 0.04724577405731572,\n \"acc_norm\": 0.5818181818181818,\n\ \ \"acc_norm_stderr\": 0.04724577405731572\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6040816326530613,\n \"acc_stderr\": 0.03130802899065686,\n\ \ \"acc_norm\": 0.6040816326530613,\n \"acc_norm_stderr\": 0.03130802899065686\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.5373134328358209,\n\ \ \"acc_stderr\": 0.035256751674679745,\n \"acc_norm\": 0.5373134328358209,\n\ \ \"acc_norm_stderr\": 0.035256751674679745\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.42168674698795183,\n\ \ \"acc_stderr\": 0.03844453181770917,\n \"acc_norm\": 0.42168674698795183,\n\ \ \"acc_norm_stderr\": 0.03844453181770917\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.6783625730994152,\n \"acc_stderr\": 0.03582529442573122,\n\ \ \"acc_norm\": 0.6783625730994152,\n \"acc_norm_stderr\": 0.03582529442573122\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5344202990692574,\n\ \ \"mc2_stderr\": 0.015729161957393895\n }\n}\n```" repo_url: https://huggingface.co/xxyyy123/10k_v1_lora_qkvo_rank14_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_03T13_17_02.987872 path: - '/details_harness\|arc:challenge\|25_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hellaswag\|10_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-09-03T13:17:02.987872.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-management\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_03T13_17_02.987872 path: - '/details_harness\|truthfulqa:mc\|0_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-09-03T13:17:02.987872.parquet' - config_name: results data_files: - split: 2023_09_03T13_17_02.987872 path: - results_2023-09-03T13:17:02.987872.parquet - split: latest path: - results_2023-09-03T13:17:02.987872.parquet --- # Dataset Card for Evaluation run of xxyyy123/10k_v1_lora_qkvo_rank14_v3 ## Dataset Description - Homepage: - Repository: https://huggingface.co/xxyyy123/10k_v1_lora_qkvo_rank14_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 [xxyyy123/10k_v1_lora_qkvo_rank14_v3](https://huggingface.co/xxyyy123/10k_v1_lora_qkvo_rank14_v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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 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_xxyyy123__10k_v1_lora_qkvo_rank14_v3", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-03T13:17:02.987872](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__10k_v1_lora_qkvo_rank14_v3/blob/main/results_2023-09-03T13%3A17%3A02.987872.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": 0.5091352266849982, "acc_stderr": 0.03495474191892426, "acc_norm": 0.5128128131582483, "acc_norm_stderr": 0.03493935725866389, "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5344202990692574, "mc2_stderr": 0.015729161957393895 }, "harness\|arc:challenge\|25": { "acc": 0.5298634812286689, "acc_stderr": 0.014585305840007105, "acc_norm": 0.5597269624573379, "acc_norm_stderr": 0.01450676952480424 }, "harness\|hellaswag\|10": { "acc": 0.6050587532364071, "acc_stderr": 0.004878390226591715, "acc_norm": 0.7921728739294961, "acc_norm_stderr": 0.00404923158643323 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.48148148148148145, "acc_stderr": 0.043163785995113245, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.043163785995113245 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.5, "acc_stderr": 0.04068942293855797, "acc_norm": 0.5, "acc_norm_stderr": 0.04068942293855797 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5584905660377358, "acc_stderr": 0.030561590426731833, "acc_norm": 0.5584905660377358, "acc_norm_stderr": 0.030561590426731833 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.5486111111111112, "acc_stderr": 0.041614023984032786, "acc_norm": 0.5486111111111112, "acc_norm_stderr": 0.041614023984032786 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.45664739884393063, "acc_stderr": 0.03798106566014498, "acc_norm": 0.45664739884393063, "acc_norm_stderr": 0.03798106566014498 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2647058823529412, "acc_stderr": 0.04389869956808778, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.04389869956808778 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.46808510638297873, "acc_stderr": 0.03261936918467382, "acc_norm": 0.46808510638297873, "acc_norm_stderr": 0.03261936918467382 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.34210526315789475, "acc_stderr": 0.04462917535336936, "acc_norm": 0.34210526315789475, "acc_norm_stderr": 0.04462917535336936 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.46206896551724136, "acc_stderr": 0.041546596717075474, "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.29365079365079366, "acc_stderr": 0.023456037383982022, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.023456037383982022 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2698412698412698, "acc_stderr": 0.03970158273235173, "acc_norm": 0.2698412698412698, "acc_norm_stderr": 0.03970158273235173 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5451612903225806, "acc_stderr": 0.028327743091561077, "acc_norm": 0.5451612903225806, "acc_norm_stderr": 0.028327743091561077 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.3793103448275862, "acc_stderr": 0.034139638059062345, "acc_norm": 0.3793103448275862, "acc_norm_stderr": 0.034139638059062345 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7333333333333333, "acc_stderr": 0.03453131801885415, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.03453131801885415 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.6363636363636364, "acc_stderr": 0.03427308652999934, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.03427308652999934 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.7253886010362695, "acc_stderr": 0.03221024508041153, "acc_norm": 0.7253886010362695, "acc_norm_stderr": 0.03221024508041153 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.4948717948717949, "acc_stderr": 0.02534967290683866, "acc_norm": 0.4948717948717949, "acc_norm_stderr": 0.02534967290683866 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.24074074074074073, "acc_stderr": 0.026067159222275805, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.026067159222275805 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5126050420168067, "acc_stderr": 0.03246816765752174, "acc_norm": 0.5126050420168067, "acc_norm_stderr": 0.03246816765752174 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.31125827814569534, "acc_stderr": 0.03780445850526733, "acc_norm": 0.31125827814569534, "acc_norm_stderr": 0.03780445850526733 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7211009174311926, "acc_stderr": 0.0192274688764635, "acc_norm": 0.7211009174311926, "acc_norm_stderr": 0.0192274688764635 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4166666666666667, "acc_stderr": 0.03362277436608043, "acc_norm": 0.4166666666666667, "acc_norm_stderr": 0.03362277436608043 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.696078431372549, "acc_stderr": 0.03228210387037892, "acc_norm": 0.696078431372549, "acc_norm_stderr": 0.03228210387037892 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7341772151898734, "acc_stderr": 0.02875679962965834, "acc_norm": 0.7341772151898734, "acc_norm_stderr": 0.02875679962965834 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.5515695067264574, "acc_stderr": 0.03337883736255098, "acc_norm": 0.5515695067264574, "acc_norm_stderr": 0.03337883736255098 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6030534351145038, "acc_stderr": 0.04291135671009224, "acc_norm": 0.6030534351145038, "acc_norm_stderr": 0.04291135671009224 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7024793388429752, "acc_stderr": 0.04173349148083499, "acc_norm": 0.7024793388429752, "acc_norm_stderr": 0.04173349148083499 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6388888888888888, "acc_stderr": 0.04643454608906275, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.04643454608906275 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.5398773006134969, "acc_stderr": 0.03915857291436971, "acc_norm": 0.5398773006134969, "acc_norm_stderr": 0.03915857291436971 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.3392857142857143, "acc_stderr": 0.04493949068613539, "acc_norm": 0.3392857142857143, "acc_norm_stderr": 0.04493949068613539 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7649572649572649, "acc_stderr": 0.027778835904935434, "acc_norm": 0.7649572649572649, "acc_norm_stderr": 0.027778835904935434 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7037037037037037, "acc_stderr": 0.016328814422102052, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.016328814422102052 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5664739884393064, "acc_stderr": 0.026680134761679214, "acc_norm": 0.5664739884393064, "acc_norm_stderr": 0.026680134761679214 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2547486033519553, "acc_stderr": 0.014572650383409155, "acc_norm": 0.2547486033519553, "acc_norm_stderr": 0.014572650383409155 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5359477124183006, "acc_stderr": 0.02855582751652878, "acc_norm": 0.5359477124183006, "acc_norm_stderr": 0.02855582751652878 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5819935691318328, "acc_stderr": 0.028013651891995072, "acc_norm": 0.5819935691318328, "acc_norm_stderr": 0.028013651891995072 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5524691358024691, "acc_stderr": 0.027667138569422704, "acc_norm": 0.5524691358024691, "acc_norm_stderr": 0.027667138569422704 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.36879432624113473, "acc_stderr": 0.028782227561347243, "acc_norm": 0.36879432624113473, "acc_norm_stderr": 0.028782227561347243 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3820078226857888, "acc_stderr": 0.012409564470235567, "acc_norm": 0.3820078226857888, "acc_norm_stderr": 0.012409564470235567 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.48161764705882354, "acc_stderr": 0.030352303395351964, "acc_norm": 0.48161764705882354, "acc_norm_stderr": 0.030352303395351964 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4820261437908497, "acc_stderr": 0.020214761037872404, "acc_norm": 0.4820261437908497, "acc_norm_stderr": 0.020214761037872404 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5818181818181818, "acc_stderr": 0.04724577405731572, "acc_norm": 0.5818181818181818, "acc_norm_stderr": 0.04724577405731572 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6040816326530613, "acc_stderr": 0.03130802899065686, "acc_norm": 0.6040816326530613, "acc_norm_stderr": 0.03130802899065686 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.5373134328358209, "acc_stderr": 0.035256751674679745, "acc_norm": 0.5373134328358209, "acc_norm_stderr": 0.035256751674679745 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.42168674698795183, "acc_stderr": 0.03844453181770917, "acc_norm": 0.42168674698795183, "acc_norm_stderr": 0.03844453181770917 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6783625730994152, "acc_stderr": 0.03582529442573122, "acc_norm": 0.6783625730994152, "acc_norm_stderr": 0.03582529442573122 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5344202990692574, "mc2_stderr": 0.015729161957393895 } } ``` ### 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]
passionMan/usda_tokenized_target	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 541970 num_examples: 2527 - name: test num_bytes: 180736 num_examples: 843 download_size: 136249 dataset_size: 722706 --- # Dataset Card for "usda_tokenized_target" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
RUCAIBox/Erya-dataset	--- license: apache-2.0 task_categories: - translation - text-generation --- monolingual.tgz contains a vast collection of Ancient Chinese sentences. trans.tgz combines vairous types of parallel corpora, with Ancient Chinese sentences aligned with Modern Chinese sentences. finetune.tgz selects certain classical books as benchmarks, with Ancient Chinese sentences aligned with Modern Chinese sentences. The data in trans.tgz and finetune.tgz does not overlap. More information can be found here [RUCAIBox/Erya (github.com)](https://github.com/RUCAIBox/Erya).
semiotic/spider_dataset_tuning	--- dataset_info: features: - name: type dtype: string - name: question dtype: string - name: query dtype: string - name: db_id dtype: string - name: schema dtype: string splits: - name: train num_bytes: 125169641 num_examples: 97317 - name: val num_bytes: 10757137 num_examples: 7909 - name: test num_bytes: 1384246 num_examples: 1292 download_size: 7245840 dataset_size: 137311024 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---
liuyanchen1015/MULTI_VALUE_wnli_you_ye	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 1605 num_examples: 7 - name: train num_bytes: 849 num_examples: 6 download_size: 6423 dataset_size: 2454 --- # Dataset Card for "MULTI_VALUE_wnli_you_ye" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kanishka/counterfactual_training_test	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 79390 num_examples: 1000 - name: validation num_bytes: 56120230 num_examples: 1026747 download_size: 0 dataset_size: 56199620 --- # Dataset Card for "counterfactual_training_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dahwinsingularity/dahyun-v-l-0	--- license: apache-2.0 ---
intfloat/query2doc_msmarco	--- license: cc-by-4.0 language: - en size_categories: - 100K<n<1M --- ### Dataset Summary This dataset contains GPT-3.5 (`text-davinci-003`) generations from MS-MARCO queries. [Query2doc: Query Expansion with Large Language Models](https://arxiv.org/pdf/2303.07678.pdf) Liang Wang, Nan Yang and Furu Wei ### Data Instances An example looks as follows. ``` { "query_id": "1030303", "query": "who is aziz hashim", "pseudo_doc": "Aziz Hashim is a renowned entrepreneur, business leader, and one of the most successful restaurant franchise operators in the US. He is the founder of NRD Capital, a private equity firm focused on investments in multi-unit restaurant franchised businesses. Hashim has built a formidable track record of success in the franchise industry, with brands such as Outback Steakhouse and Jamba Juice. His accomplishments and philanthropic initiatives have earned him numerous awards, including the prestigious Ernst and Young Entrepreneur of the Year award." } ``` ### Data Fields - `query_id`: a `string` feature. - `query`: a `string` feature. - `pseudo_doc`: a `string` feature. ### Data Splits \| train \| dev \| test \| trec_dl2019 \| trec_dl2020 \| \|--------\|------:\|------:\|------:\|------:\| \| 502939 \| 6980 \| 6837 \| 43 \| 54 \| ### How to use this dataset ```python from datasets import load_dataset dataset = load_dataset('intfloat/query2doc_msmarco') print(dataset['trec_dl2019'][0]) ``` ### Reproducing our results We provide a python script [repro_bm25.py](https://huggingface.co/datasets/intfloat/query2doc_msmarco/blob/main/repro_bm25.py) to reproduce our results with BM25 retrieval. First install some python dependency packages: ``` pip install pyserini==0.15.0 pytrec_eval datasets tqdm ``` Then download and run the python code: ``` python repro_bm25.py ``` This script utilizes the pre-built Lucene index from [Pyserini](https://github.com/castorini/pyserini/blob/pyserini-0.15.0/docs/prebuilt-indexes.md) and might yield slightly different results compared to the paper. ### Citation Information ``` @article{wang2023query2doc, title={Query2doc: Query Expansion with Large Language Models}, author={Wang, Liang and Yang, Nan and Wei, Furu}, journal={arXiv preprint arXiv:2303.07678}, year={2023} } ```
mespinosami/map2sat-central-belt-clarity-old-map20-samples	--- dataset_info: features: - name: input_image dtype: image - name: edit_prompt dtype: string - name: edited_image dtype: image splits: - name: train num_bytes: 857306.8 num_examples: 16 - name: test num_bytes: 201058.2 num_examples: 4 download_size: 1061836 dataset_size: 1058365.0 --- # Dataset Card for "map2sat-central-belt-clarity-old-map20-samples" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ricahrd/McKevin	--- license: openrail ---
harpreetsahota/Instruction-Following-Evaluation-for-Large-Language-Models	--- dataset_info: features: - name: key dtype: int64 - name: prompt dtype: string - name: instruction_id_list sequence: string - name: kwargs dtype: string splits: - name: train num_bytes: 181824 num_examples: 541 download_size: 80840 dataset_size: 181824 configs: - config_name: default data_files: - split: train path: data/train-* --- # Instruction-Following Evaluation Dataset ## 📜 Overview This dataset, specifically designed for the evaluation of large language models in instruction-following tasks, is directly inspired by the methodologies and experiments described in the paper titled _"Instruction-Following Evaluation for Large Language Models"_. The dataset's creation and availability on HuggingFace are aimed at enhancing research and application in the field of natural language understanding, particularly in the context of instruction interpretation and execution by AI models. ## 🌐 Source The dataset draws its structure and content from the insights provided in: - Original Research Paper: [_"Instruction-Following Evaluation for Large Language Models"_](https://arxiv.org/abs/2311.07911) - Original Data Repository: [Google Research on GitHub](https://github.com/google-research/google-research/tree/master/instruction_following_eval) ## 📊 Dataset Structure Comprising primarily of 'prompts', this dataset is tailored to challenge and assess language models on various facets of understanding and executing instructions. Each prompt represents a unique scenario or task, simulating real-world applications where accurate interpretation of instructions is crucial. ## 💡 Usage Targeted for use within the HuggingFace ecosystem, this dataset serves as a pivotal tool for researchers and developers focusing on the advancement of language models. It stands as a benchmark for: - 📈 Evaluating model performance in instruction-following tasks. - 🔍 Identifying model capabilities and areas of improvement. - 🤖 Enhancing AI's understanding of complex, human-like commands. ## 🙏 Acknowledgements This dataset is a tribute to the foundational work presented in the original paper and is intended for academic and research purposes. It reflects a commitment to furthering the understanding of AI's interaction with human language, particularly in processing and responding to diverse and complex instructions.
open-llm-leaderboard/details_TFLai__gpt2-turkish-uncased	--- pretty_name: Evaluation run of TFLai/gpt2-turkish-uncased dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TFLai/gpt2-turkish-uncased](https://huggingface.co/TFLai/gpt2-turkish-uncased)\ \ 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 3 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_TFLai__gpt2-turkish-uncased\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-02T15:29:40.186292](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__gpt2-turkish-uncased/blob/main/results_2023-12-02T15-29-40.186292.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\": 0.0,\n \"\ acc_stderr\": 0.0\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \ \ \"acc_stderr\": 0.0\n }\n}\n```" repo_url: https://huggingface.co/TFLai/gpt2-turkish-uncased 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_07_24T09_48_46.264649 path: - '/details_harness\|arc:challenge\|25_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-24T09:48:46.264649.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_22T01_34_05.823968 path: - '/details_harness\|drop\|3_2023-10-22T01-34-05.823968.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-22T01-34-05.823968.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_22T01_34_05.823968 path: - '/details_harness\|gsm8k\|5_2023-10-22T01-34-05.823968.parquet' - split: 2023_12_02T15_29_40.186292 path: - '/details_harness\|gsm8k\|5_2023-12-02T15-29-40.186292.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-02T15-29-40.186292.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hellaswag\|10_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-24T09:48:46.264649.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_24T09_48_46.264649 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-24T09:48:46.264649.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_22T01_34_05.823968 path: - '/details_harness\|winogrande\|5_2023-10-22T01-34-05.823968.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-22T01-34-05.823968.parquet' - config_name: results data_files: - split: 2023_07_24T09_48_46.264649 path: - results_2023-07-24T09:48:46.264649.parquet - split: 2023_10_22T01_34_05.823968 path: - results_2023-10-22T01-34-05.823968.parquet - split: 2023_12_02T15_29_40.186292 path: - results_2023-12-02T15-29-40.186292.parquet - split: latest path: - results_2023-12-02T15-29-40.186292.parquet --- # Dataset Card for Evaluation run of TFLai/gpt2-turkish-uncased ## Dataset Description - Homepage: - Repository: https://huggingface.co/TFLai/gpt2-turkish-uncased - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [TFLai/gpt2-turkish-uncased](https://huggingface.co/TFLai/gpt2-turkish-uncased) 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 3 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_TFLai__gpt2-turkish-uncased", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-02T15:29:40.186292](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__gpt2-turkish-uncased/blob/main/results_2023-12-02T15-29-40.186292.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": 0.0, "acc_stderr": 0.0 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ### 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]
AdapterOcean/python3-standardized_cluster_16_std	--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 3711623 num_examples: 3834 download_size: 762202 dataset_size: 3711623 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "python3-standardized_cluster_16_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ibupari/mini-learnilatypus	--- dataset_info: features: - name: title dtype: string - name: subtitle dtype: string - name: paragraph dtype: string - name: sentences dtype: string splits: - name: train num_bytes: 1324745 num_examples: 1000 download_size: 264663 dataset_size: 1324745 configs: - config_name: default data_files: - split: train path: data/train-* ---
humane-lab/K-HATERS	--- license: cc-by-4.0 language: - ko pretty_name: K-Haters tags: - hate speech detection --- <!-- # ℹ️ Dataset card for K-HATERS ### Dataset summary We introduces K-HATERS, a new corpus for hate speech detection in Korean, comprising approximately 192K news comments with target-specific offensiveness ratings. The corpus consists of 192,158 news comments consisting of 184,117 news comments collected by ourselves and 8,041 comments collected from a [previous study](https://aclanthology.org/2020.socialnlp-1.4/). We collected news comments published through the politics, society and world news sections in Naver News over two months in 2021. All comments were annotated through CashMission, a crowdsourcing service run by SELECTSTAR. </br>For more information, please refer to the paper [K-HATERS](https://arxiv.org/abs/2310.15439) published at EMNLP 2023 Findings. ### Supported tasks - Hate speech detection - Multi class classification (labels: normal, offensive, L1_hate, L2_hate) - Binary classifiction (labels: normal, toxic(offensive, L1_hate, L2_hate)) - Rationale prediction (offensiveness, target rationale) ### Data describtion ``` data['train'][42] {'text': '군대도 안간 놈 이 주둥아리 는 씽씽하네..보수 놈 들..군대는 안가고 애국이냐..#@이름#,#@이름#,', 'label': 'L1_hate', 'target_label': ['political'], 'offensiveness_rationale': [[7, 8], [11, 15], [27, 28]], 'target_rationale': [[24, 26], [46, 51], [52, 57]]} ``` - Abusive language categories (label) - L2_hate: Comments with explicit forms of hate expressions toward one of the groups of protected attributes (e.g., gender, age, race, ...) - L1_hate: Comments with more implicit forms of hate expressions - Offensive: Comments that express offensiveness but not toward a protected attribute group - Normal: The rest comments - Multi-label target categories (target_label): list of offensiveness targets. A comment can have zero or multiple targets. - List of target categories: gender, age, race, religion, politics, job, disability, individuals, and others. - Annotators' rationales for the strength of ratings (offensiveness_rationale): lists providing annotators' rationales for the strength of ratings. The list includes the start and end indices of highlight spans. - Annotators' rationales for the target of offensiveness (target_rationale) ### Dataset split We provide the dataset in the form of splits as 172,158 (for train), 10,000 (for validation), and 10,000 (for test). Label ratio was preseved (stratified split). ### Labeling guidelines Labeling guidelines are available as a part of SELECTSTAR open datasets (in Korean). [link](https://open.selectstar.ai/ko/?page_id=5948) </br> # 📜 Data statement We present the data statement for responsible usage [(Bender and Friedman, 2018)](https://aclanthology.org/Q18-1041/). ### Curation Rationale We collected the raw data from the news aggregator of Naver, the largest news portal in Korea. We targeted news articles published in the society, world news, and politics sections because discussions are active in the hard news. ### Language Variety Our dataset consists of the news comments in Korean (ko-KR). ### Speaker Demographic The user demographic is not available. However, considering that the portal site has the largest share of Korean, it can be assumed that speakers are mostly Korean. ### Annotator Demographic A total of 405 workers participated in an annotation. 21 workers are 10s, 222 workers are 20s, 116 workers are 30s, 35 workers are 40s, 9 workers are 50s, and 2 workers are 60s. ### Speech Situation News article in the hard news section deals with controversial events, so there are more likely to exist hate comments or toxicity comments. The target articles were published between July 2021 and August 2021. During that period, the most controversial events were the South Korean presidential election, the Tokyo Olympics, COVID-19, and the Restoration of Taliban Control, etc. ### Text Characteristics It includes hatred words limited to Korea, such as hatred of certain political orientations and certain groups. For example, '대깨문' (a word that hates former Korean president Moon's supporter), and '꼴페미' (a word that hates feminists) </br> # 🤝 License & Contributors ### Licensing information This dataset is shared under CC-BY 4.0. </br>According to this license, you are free to use the dataset as long as you provide appropriate attribution (e.g., citing our paper). ### Citation information ``` @article{park2023haters, title={K-HATERS: A Hate Speech Detection Corpus in Korean with Target-Specific Ratings}, author={Park, Chaewon and Kim, Suhwan and Park, Kyubyong and Park, Kunwoo}, journal={Findings of the EMNLP 2023}, year={2023} } ``` ### Contributions - Chaewon Park - Suhwan Kim (TUNiB) - Kyubyong Park (TUNiB) - Kunwoo Park #-->
open-llm-leaderboard/details_Sharathhebbar24__ssh_1.8B	--- pretty_name: Evaluation run of Sharathhebbar24/ssh_1.8B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Sharathhebbar24/ssh_1.8B](https://huggingface.co/Sharathhebbar24/ssh_1.8B) 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_Sharathhebbar24__ssh_1.8B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-03T16:03:37.862164](https://huggingface.co/datasets/open-llm-leaderboard/details_Sharathhebbar24__ssh_1.8B/blob/main/results_2024-02-03T16-03-37.862164.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\": 0.4400975999737303,\n\ \ \"acc_stderr\": 0.0345967614345703,\n \"acc_norm\": 0.4431186866947614,\n\ \ \"acc_norm_stderr\": 0.03532660922667111,\n \"mc1\": 0.2668298653610771,\n\ \ \"mc1_stderr\": 0.015483691939237265,\n \"mc2\": 0.4314996062576424,\n\ \ \"mc2_stderr\": 0.015306262833109105\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.3728668941979522,\n \"acc_stderr\": 0.014131176760131165,\n\ \ \"acc_norm\": 0.39078498293515357,\n \"acc_norm_stderr\": 0.014258563880513778\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.47560246962756425,\n\ \ \"acc_stderr\": 0.004983837641502896,\n \"acc_norm\": 0.6236805417247561,\n\ \ \"acc_norm_stderr\": 0.00483471581420811\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.37777777777777777,\n\ \ \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.37777777777777777,\n\ \ \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.4605263157894737,\n \"acc_stderr\": 0.04056242252249033,\n\ \ \"acc_norm\": 0.4605263157894737,\n \"acc_norm_stderr\": 0.04056242252249033\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.55,\n\ \ \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.5056603773584906,\n \"acc_stderr\": 0.030770900763851316,\n\ \ \"acc_norm\": 0.5056603773584906,\n \"acc_norm_stderr\": 0.030770900763851316\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4166666666666667,\n\ \ \"acc_stderr\": 0.041227287076512825,\n \"acc_norm\": 0.4166666666666667,\n\ \ \"acc_norm_stderr\": 0.041227287076512825\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\"\ : 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.044084400227680794,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.044084400227680794\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.3872832369942196,\n\ \ \"acc_stderr\": 0.037143259063020656,\n \"acc_norm\": 0.3872832369942196,\n\ \ \"acc_norm_stderr\": 0.037143259063020656\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.040233822736177476,\n\ \ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.040233822736177476\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.032555253593403555,\n\ \ \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.032555253593403555\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.39473684210526316,\n\ \ \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.39473684210526316,\n\ \ \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.4206896551724138,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.4206896551724138,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.30952380952380953,\n \"acc_stderr\": 0.023809523809523867,\n \"\ acc_norm\": 0.30952380952380953,\n \"acc_norm_stderr\": 0.023809523809523867\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2857142857142857,\n\ \ \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n\ \ \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.028444006199428714,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.028444006199428714\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.33004926108374383,\n \"acc_stderr\": 0.033085304262282574,\n\ \ \"acc_norm\": 0.33004926108374383,\n \"acc_norm_stderr\": 0.033085304262282574\n\ \ },\n \"harness\|hendrycksTest-high_school_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-high_school_european_history\|5\"\ : {\n \"acc\": 0.5878787878787879,\n \"acc_stderr\": 0.03843566993588717,\n\ \ \"acc_norm\": 0.5878787878787879,\n \"acc_norm_stderr\": 0.03843566993588717\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.035623524993954825,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.035623524993954825\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\"\ : {\n \"acc\": 0.6113989637305699,\n \"acc_stderr\": 0.035177397963731316,\n\ \ \"acc_norm\": 0.6113989637305699,\n \"acc_norm_stderr\": 0.035177397963731316\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.40512820512820513,\n \"acc_stderr\": 0.024890471769938145,\n\ \ \"acc_norm\": 0.40512820512820513,\n \"acc_norm_stderr\": 0.024890471769938145\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066482,\n \ \ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066482\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.46638655462184875,\n \"acc_stderr\": 0.03240501447690071,\n\ \ \"acc_norm\": 0.46638655462184875,\n \"acc_norm_stderr\": 0.03240501447690071\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389024,\n \"\ acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389024\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.5467889908256881,\n \"acc_stderr\": 0.021343255165546037,\n \"\ acc_norm\": 0.5467889908256881,\n \"acc_norm_stderr\": 0.021343255165546037\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.35648148148148145,\n \"acc_stderr\": 0.032664783315272714,\n \"\ acc_norm\": 0.35648148148148145,\n \"acc_norm_stderr\": 0.032664783315272714\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.49019607843137253,\n \"acc_stderr\": 0.03508637358630572,\n \"\ acc_norm\": 0.49019607843137253,\n \"acc_norm_stderr\": 0.03508637358630572\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.5316455696202531,\n \"acc_stderr\": 0.032481974005110756,\n \ \ \"acc_norm\": 0.5316455696202531,\n \"acc_norm_stderr\": 0.032481974005110756\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.4977578475336323,\n\ \ \"acc_stderr\": 0.033557465352232634,\n \"acc_norm\": 0.4977578475336323,\n\ \ \"acc_norm_stderr\": 0.033557465352232634\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.4732824427480916,\n \"acc_stderr\": 0.04379024936553893,\n\ \ \"acc_norm\": 0.4732824427480916,\n \"acc_norm_stderr\": 0.04379024936553893\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.628099173553719,\n \"acc_stderr\": 0.044120158066245044,\n \"\ acc_norm\": 0.628099173553719,\n \"acc_norm_stderr\": 0.044120158066245044\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.5648148148148148,\n\ \ \"acc_stderr\": 0.04792898170907062,\n \"acc_norm\": 0.5648148148148148,\n\ \ \"acc_norm_stderr\": 0.04792898170907062\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.4723926380368098,\n \"acc_stderr\": 0.039223782906109894,\n\ \ \"acc_norm\": 0.4723926380368098,\n \"acc_norm_stderr\": 0.039223782906109894\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.32142857142857145,\n\ \ \"acc_stderr\": 0.044328040552915185,\n \"acc_norm\": 0.32142857142857145,\n\ \ \"acc_norm_stderr\": 0.044328040552915185\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.6601941747572816,\n \"acc_stderr\": 0.046897659372781335,\n\ \ \"acc_norm\": 0.6601941747572816,\n \"acc_norm_stderr\": 0.046897659372781335\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.6581196581196581,\n\ \ \"acc_stderr\": 0.03107502852650775,\n \"acc_norm\": 0.6581196581196581,\n\ \ \"acc_norm_stderr\": 0.03107502852650775\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.5644955300127714,\n\ \ \"acc_stderr\": 0.017730589927926588,\n \"acc_norm\": 0.5644955300127714,\n\ \ \"acc_norm_stderr\": 0.017730589927926588\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5115606936416185,\n \"acc_stderr\": 0.026911898686377927,\n\ \ \"acc_norm\": 0.5115606936416185,\n \"acc_norm_stderr\": 0.026911898686377927\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2435754189944134,\n\ \ \"acc_stderr\": 0.014355911964767857,\n \"acc_norm\": 0.2435754189944134,\n\ \ \"acc_norm_stderr\": 0.014355911964767857\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.5359477124183006,\n \"acc_stderr\": 0.028555827516528787,\n\ \ \"acc_norm\": 0.5359477124183006,\n \"acc_norm_stderr\": 0.028555827516528787\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.45016077170418006,\n\ \ \"acc_stderr\": 0.028256660723360184,\n \"acc_norm\": 0.45016077170418006,\n\ \ \"acc_norm_stderr\": 0.028256660723360184\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.027744313443376536,\n\ \ \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.027744313443376536\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.3404255319148936,\n \"acc_stderr\": 0.028267657482650144,\n \ \ \"acc_norm\": 0.3404255319148936,\n \"acc_norm_stderr\": 0.028267657482650144\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.34485006518904826,\n\ \ \"acc_stderr\": 0.012139881006287058,\n \"acc_norm\": 0.34485006518904826,\n\ \ \"acc_norm_stderr\": 0.012139881006287058\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.3786764705882353,\n \"acc_stderr\": 0.029465133639776132,\n\ \ \"acc_norm\": 0.3786764705882353,\n \"acc_norm_stderr\": 0.029465133639776132\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.4084967320261438,\n \"acc_stderr\": 0.01988622103750188,\n \ \ \"acc_norm\": 0.4084967320261438,\n \"acc_norm_stderr\": 0.01988622103750188\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5363636363636364,\n\ \ \"acc_stderr\": 0.04776449162396197,\n \"acc_norm\": 0.5363636363636364,\n\ \ \"acc_norm_stderr\": 0.04776449162396197\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.5183673469387755,\n \"acc_stderr\": 0.03198761546763127,\n\ \ \"acc_norm\": 0.5183673469387755,\n \"acc_norm_stderr\": 0.03198761546763127\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.3781094527363184,\n\ \ \"acc_stderr\": 0.03428867848778658,\n \"acc_norm\": 0.3781094527363184,\n\ \ \"acc_norm_stderr\": 0.03428867848778658\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.3795180722891566,\n\ \ \"acc_stderr\": 0.037777988227480165,\n \"acc_norm\": 0.3795180722891566,\n\ \ \"acc_norm_stderr\": 0.037777988227480165\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.5146198830409356,\n \"acc_stderr\": 0.038331852752130254,\n\ \ \"acc_norm\": 0.5146198830409356,\n \"acc_norm_stderr\": 0.038331852752130254\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2668298653610771,\n\ \ \"mc1_stderr\": 0.015483691939237265,\n \"mc2\": 0.4314996062576424,\n\ \ \"mc2_stderr\": 0.015306262833109105\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.5927387529597474,\n \"acc_stderr\": 0.013808654122417848\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.27520849128127367,\n \ \ \"acc_stderr\": 0.012302114305862647\n }\n}\n```" repo_url: https://huggingface.co/Sharathhebbar24/ssh_1.8B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|arc:challenge\|25_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-03T16-03-37.862164.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|gsm8k\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hellaswag\|10_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-03T16-03-37.862164.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-03T16-03-37.862164.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '/details_harness\|winogrande\|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-03T16-03-37.862164.parquet' - config_name: results data_files: - split: 2024_02_03T16_03_37.862164 path: - results_2024-02-03T16-03-37.862164.parquet - split: latest path: - results_2024-02-03T16-03-37.862164.parquet --- # Dataset Card for Evaluation run of Sharathhebbar24/ssh_1.8B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Sharathhebbar24/ssh_1.8B](https://huggingface.co/Sharathhebbar24/ssh_1.8B) 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_Sharathhebbar24__ssh_1.8B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-03T16:03:37.862164](https://huggingface.co/datasets/open-llm-leaderboard/details_Sharathhebbar24__ssh_1.8B/blob/main/results_2024-02-03T16-03-37.862164.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": 0.4400975999737303, "acc_stderr": 0.0345967614345703, "acc_norm": 0.4431186866947614, "acc_norm_stderr": 0.03532660922667111, "mc1": 0.2668298653610771, "mc1_stderr": 0.015483691939237265, "mc2": 0.4314996062576424, "mc2_stderr": 0.015306262833109105 }, "harness\|arc:challenge\|25": { "acc": 0.3728668941979522, "acc_stderr": 0.014131176760131165, "acc_norm": 0.39078498293515357, "acc_norm_stderr": 0.014258563880513778 }, "harness\|hellaswag\|10": { "acc": 0.47560246962756425, "acc_stderr": 0.004983837641502896, "acc_norm": 0.6236805417247561, "acc_norm_stderr": 0.00483471581420811 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.37777777777777777, "acc_stderr": 0.04188307537595853, "acc_norm": 0.37777777777777777, "acc_norm_stderr": 0.04188307537595853 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.4605263157894737, "acc_stderr": 0.04056242252249033, "acc_norm": 0.4605263157894737, "acc_norm_stderr": 0.04056242252249033 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5056603773584906, "acc_stderr": 0.030770900763851316, "acc_norm": 0.5056603773584906, "acc_norm_stderr": 0.030770900763851316 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4166666666666667, "acc_stderr": 0.041227287076512825, "acc_norm": 0.4166666666666667, "acc_norm_stderr": 0.041227287076512825 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.26, "acc_stderr": 0.044084400227680794, "acc_norm": 0.26, "acc_norm_stderr": 0.044084400227680794 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.3872832369942196, "acc_stderr": 0.037143259063020656, "acc_norm": 0.3872832369942196, "acc_norm_stderr": 0.037143259063020656 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.20588235294117646, "acc_stderr": 0.040233822736177476, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.040233822736177476 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4553191489361702, "acc_stderr": 0.032555253593403555, "acc_norm": 0.4553191489361702, "acc_norm_stderr": 0.032555253593403555 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.39473684210526316, "acc_stderr": 0.045981880578165414, "acc_norm": 0.39473684210526316, "acc_norm_stderr": 0.045981880578165414 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.4206896551724138, "acc_stderr": 0.0411391498118926, "acc_norm": 0.4206896551724138, "acc_norm_stderr": 0.0411391498118926 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.30952380952380953, "acc_stderr": 0.023809523809523867, "acc_norm": 0.30952380952380953, "acc_norm_stderr": 0.023809523809523867 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.04040610178208841, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.04040610178208841 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5, "acc_stderr": 0.028444006199428714, "acc_norm": 0.5, "acc_norm_stderr": 0.028444006199428714 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.33004926108374383, "acc_stderr": 0.033085304262282574, "acc_norm": 0.33004926108374383, "acc_norm_stderr": 0.033085304262282574 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.5878787878787879, "acc_stderr": 0.03843566993588717, "acc_norm": 0.5878787878787879, "acc_norm_stderr": 0.03843566993588717 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.5, "acc_stderr": 0.035623524993954825, "acc_norm": 0.5, "acc_norm_stderr": 0.035623524993954825 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.6113989637305699, "acc_stderr": 0.035177397963731316, "acc_norm": 0.6113989637305699, "acc_norm_stderr": 0.035177397963731316 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.40512820512820513, "acc_stderr": 0.024890471769938145, "acc_norm": 0.40512820512820513, "acc_norm_stderr": 0.024890471769938145 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3148148148148148, "acc_stderr": 0.028317533496066482, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.028317533496066482 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.46638655462184875, "acc_stderr": 0.03240501447690071, "acc_norm": 0.46638655462184875, "acc_norm_stderr": 0.03240501447690071 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2847682119205298, "acc_stderr": 0.03684881521389024, "acc_norm": 0.2847682119205298, "acc_norm_stderr": 0.03684881521389024 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.5467889908256881, "acc_stderr": 0.021343255165546037, "acc_norm": 0.5467889908256881, "acc_norm_stderr": 0.021343255165546037 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.35648148148148145, "acc_stderr": 0.032664783315272714, "acc_norm": 0.35648148148148145, "acc_norm_stderr": 0.032664783315272714 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.49019607843137253, "acc_stderr": 0.03508637358630572, "acc_norm": 0.49019607843137253, "acc_norm_stderr": 0.03508637358630572 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.5316455696202531, "acc_stderr": 0.032481974005110756, "acc_norm": 0.5316455696202531, "acc_norm_stderr": 0.032481974005110756 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.4977578475336323, "acc_stderr": 0.033557465352232634, "acc_norm": 0.4977578475336323, "acc_norm_stderr": 0.033557465352232634 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.4732824427480916, "acc_stderr": 0.04379024936553893, "acc_norm": 0.4732824427480916, "acc_norm_stderr": 0.04379024936553893 }, "harness\|hendrycksTest-international_law\|5": { "acc": 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"acc_norm_stderr": 0.03107502852650775 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.5644955300127714, "acc_stderr": 0.017730589927926588, "acc_norm": 0.5644955300127714, "acc_norm_stderr": 0.017730589927926588 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5115606936416185, "acc_stderr": 0.026911898686377927, "acc_norm": 0.5115606936416185, "acc_norm_stderr": 0.026911898686377927 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2435754189944134, "acc_stderr": 0.014355911964767857, "acc_norm": 0.2435754189944134, "acc_norm_stderr": 0.014355911964767857 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5359477124183006, "acc_stderr": 0.028555827516528787, "acc_norm": 0.5359477124183006, "acc_norm_stderr": 0.028555827516528787 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.45016077170418006, "acc_stderr": 0.028256660723360184, "acc_norm": 0.45016077170418006, "acc_norm_stderr": 0.028256660723360184 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.46296296296296297, "acc_stderr": 0.027744313443376536, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.027744313443376536 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3404255319148936, "acc_stderr": 0.028267657482650144, "acc_norm": 0.3404255319148936, "acc_norm_stderr": 0.028267657482650144 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.34485006518904826, "acc_stderr": 0.012139881006287058, "acc_norm": 0.34485006518904826, "acc_norm_stderr": 0.012139881006287058 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.3786764705882353, "acc_stderr": 0.029465133639776132, "acc_norm": 0.3786764705882353, "acc_norm_stderr": 0.029465133639776132 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4084967320261438, "acc_stderr": 0.01988622103750188, "acc_norm": 0.4084967320261438, "acc_norm_stderr": 0.01988622103750188 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5363636363636364, "acc_stderr": 0.04776449162396197, "acc_norm": 0.5363636363636364, "acc_norm_stderr": 0.04776449162396197 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5183673469387755, "acc_stderr": 0.03198761546763127, "acc_norm": 0.5183673469387755, "acc_norm_stderr": 0.03198761546763127 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.3781094527363184, "acc_stderr": 0.03428867848778658, "acc_norm": 0.3781094527363184, "acc_norm_stderr": 0.03428867848778658 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.3795180722891566, "acc_stderr": 0.037777988227480165, "acc_norm": 0.3795180722891566, "acc_norm_stderr": 0.037777988227480165 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.5146198830409356, "acc_stderr": 0.038331852752130254, "acc_norm": 0.5146198830409356, "acc_norm_stderr": 0.038331852752130254 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2668298653610771, "mc1_stderr": 0.015483691939237265, "mc2": 0.4314996062576424, "mc2_stderr": 0.015306262833109105 }, "harness\|winogrande\|5": { "acc": 0.5927387529597474, "acc_stderr": 0.013808654122417848 }, "harness\|gsm8k\|5": { "acc": 0.27520849128127367, "acc_stderr": 0.012302114305862647 } } ``` ## 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 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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]
FUBUKIBG/rosepronto	--- license: openrail ---
heliosprime/twitter_dataset_1713204665	--- 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: 12837 num_examples: 36 download_size: 14403 dataset_size: 12837 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713204665" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo2_100_kl_0.1_prm_160m_thr_0.3_seed_3	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: index dtype: int64 - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43586042 num_examples: 18929 - name: epoch_1 num_bytes: 44122478 num_examples: 18929 - name: epoch_2 num_bytes: 44219397 num_examples: 18929 - name: epoch_3 num_bytes: 44261340 num_examples: 18929 - name: epoch_4 num_bytes: 44282722 num_examples: 18929 - name: epoch_5 num_bytes: 44300108 num_examples: 18929 - name: epoch_6 num_bytes: 44310569 num_examples: 18929 - name: epoch_7 num_bytes: 44316296 num_examples: 18929 - name: epoch_8 num_bytes: 44323129 num_examples: 18929 - name: epoch_9 num_bytes: 44326498 num_examples: 18929 - name: epoch_10 num_bytes: 44326077 num_examples: 18929 - name: epoch_11 num_bytes: 44327725 num_examples: 18929 - name: epoch_12 num_bytes: 44328350 num_examples: 18929 - name: epoch_13 num_bytes: 44330594 num_examples: 18929 - name: epoch_14 num_bytes: 44330360 num_examples: 18929 - name: epoch_15 num_bytes: 44332404 num_examples: 18929 - name: epoch_16 num_bytes: 44331677 num_examples: 18929 - name: epoch_17 num_bytes: 44332499 num_examples: 18929 - name: epoch_18 num_bytes: 44332572 num_examples: 18929 - name: epoch_19 num_bytes: 44334032 num_examples: 18929 - name: epoch_20 num_bytes: 44334167 num_examples: 18929 - name: epoch_21 num_bytes: 44333390 num_examples: 18929 - name: epoch_22 num_bytes: 44335365 num_examples: 18929 - name: epoch_23 num_bytes: 44334419 num_examples: 18929 - name: epoch_24 num_bytes: 44334230 num_examples: 18929 - name: epoch_25 num_bytes: 44333923 num_examples: 18929 - name: epoch_26 num_bytes: 44333784 num_examples: 18929 - name: epoch_27 num_bytes: 44334765 num_examples: 18929 - name: epoch_28 num_bytes: 44334889 num_examples: 18929 - name: epoch_29 num_bytes: 44334778 num_examples: 18929 download_size: 699687675 dataset_size: 1328698579 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
liuyanchen1015/MULTI_VALUE_wnli_indefinite_for_zero	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 12594 num_examples: 65 - name: test num_bytes: 40930 num_examples: 144 - name: train num_bytes: 115381 num_examples: 604 download_size: 62465 dataset_size: 168905 --- # Dataset Card for "MULTI_VALUE_wnli_indefinite_for_zero" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MatsuoDochiai/LUISA	--- license: openrail ---
FidelOdok/SOFA_DOA	--- dataset_info: features: - name: audio dtype: audio - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' splits: - name: train num_bytes: 21491814313.0 num_examples: 22500 download_size: 21492710615 dataset_size: 21491814313.0 --- # Dataset Card for "SOFA_DOA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/tachibana_arisu_theidolmastercinderellagirlsu149	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Tachibana Arisu This is the dataset of Tachibana Arisu, containing 200 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 200 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 486 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 200 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 200 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 200 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 200 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 200 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 486 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 486 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 486 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|
liuyanchen1015/MULTI_VALUE_rte_inverted_indirect_question	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 28900 num_examples: 53 - name: train num_bytes: 19696 num_examples: 41 download_size: 43113 dataset_size: 48596 --- # Dataset Card for "MULTI_VALUE_rte_inverted_indirect_question" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pierre-pessarossi/climate-question-answers	--- dataset_info: features: - name: instruction dtype: string - name: answer dtype: string splits: - name: train num_bytes: 2467048 num_examples: 7033 - name: test num_bytes: 622679 num_examples: 1758 download_size: 1892524 dataset_size: 3089727 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* license: mit task_categories: - question-answering language: - en tags: - climate pretty_name: Climate change questions/answers size_categories: - 1K<n<10K --- Dataset Card for Climate change questions / answers dataset Dataset Description\ This is a first version of a question/answer dataset on climate change and ecology. The dataset has been created based on a curated list of wikipedia articles on climate change from https://huggingface.co/datasets/pierre-pessarossi/wikipedia-climate-data For each wikipedia article of the original dataset, a set of question/answers pairs was created. The number of question depends on the initial size of the wikipedia article. Currently, there are only question / answer pairs (i.e. no chat mode with several messages). Open-mixtral-8x7b was used to generate the question and answers. The dataset can be useful for supervised fine-tuning on the topic of climate change.\ In forthcoming releases the dataset will be expanded in length and different LLMs might be used to generate the question / answer.
bclavie/mmarco-japanese-hard-negatives	--- language: - ja task_categories: - text-retrieval dataset_info: features: - name: query dtype: string - name: positives sequence: string - name: negatives sequence: string - name: bm25_negatives sequence: string - name: original_negatives sequence: string splits: - name: train num_bytes: 24494938913 num_examples: 391061 download_size: 11664534369 dataset_size: 24494938913 configs: - config_name: default data_files: - split: train path: data/train-* --- [Under Construction] This is a repository containing all the queries from the Japanese part of the MMarco dataset, the multilingual version of the MSMarco dataset. For each query, there are matching hard negatives: - 25 of them retrieved by the multilingual e5 base model. - Up to 10 of them retrieved by the basic implementation of BM25 from Japanese in the Anserini library.
cacheop/red-right-hand	--- license: other dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 711683 num_examples: 315 download_size: 338831 dataset_size: 711683 configs: - config_name: default data_files: - split: train path: data/train-* ---
vishal323/heart	--- license: openrail ---
CyberHarem/saori_bluearchive	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of saori/錠前サオリ/纱织 (Blue Archive) This is the dataset of saori/錠前サオリ/纱织 (Blue Archive), containing 500 images and their tags. The core tags of this character are `long_hair, breasts, halo, blue_eyes, blue_hair, black_hair, large_breasts, multicolored_hair, baseball_cap, hat`, 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 \| 500 \| 1.11 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/saori_bluearchive/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 500 \| 904.46 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/saori_bluearchive/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1354 \| 1.80 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/saori_bluearchive/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/saori_bluearchive', 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 \| 19 \| ![](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, bare_shoulders, black_headwear, black_pants, black_shirt, crop_top, holding_gun, midriff, navel, sig_sauer, sleeveless_shirt, solo, stomach, white_coat, assault_rifle, black_belt, off_shoulder, open_coat, black_gloves, looking_at_viewer, long_sleeves, mouth_mask, standing, leggings, buckle, armband, cowboy_shot \| \| 1 \| 7 \| ![](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, black_headwear, black_pants, black_shirt, crop_top, looking_at_viewer, midriff, navel, off_shoulder, simple_background, sleeveless_shirt, solo, stomach, white_coat, cowboy_shot, standing, white_background, leggings, mouth_mask, open_coat, long_sleeves, black_belt \| \| 2 \| 11 \| ![](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, black_pants, black_shirt, crop_top, long_sleeves, looking_at_viewer, midriff, mouth_mask, navel, sleeveless_shirt, solo, stomach, white_coat, bare_shoulders, black_belt, off_shoulder, open_coat, black_gloves, black_headwear, black_mask, cowboy_shot, standing, jacket, chest_harness, snap-fit_buckle, groin, holding_mask, medium_breasts, underbust, unworn_mask \| \| 3 \| 7 \| ![](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, alternate_costume, black_bikini, navel, solo, stomach, cleavage, closed_mouth, collarbone, looking_at_viewer, thighs, bare_shoulders, blush, wet, black_headwear, cowboy_shot, outdoors, sideboob, string_bikini, long_sleeves, open_clothes, skindentation, standing, underboob, wading, water \| \| 4 \| 63 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| white_dress, 1girl, alternate_costume, elbow_gloves, solo, white_gloves, cleavage, bare_shoulders, white_choker, looking_at_viewer, earrings, collarbone, colored_inner_hair, simple_background, white_background, closed_mouth, blush, blue_halo, strapless_dress, covered_navel, hair_flower \| \| 5 \| 27 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, elbow_gloves, looking_at_viewer, solo, white_dress, white_gloves, alternate_costume, ass, bare_shoulders, white_background, ponytail, simple_background, from_behind, looking_back, weapon, white_thighhighs, thigh_holster, blush, closed_mouth, knife, garter_straps, thigh_strap, colored_inner_hair \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, alternate_costume, bare_shoulders, elbow_gloves, holding_gun, looking_at_viewer, ponytail, solo, white_dress, white_gloves, from_behind, looking_back, thigh_holster, simple_background, thigh_strap, white_background, assault_rifle, closed_mouth, handgun, mask, side_slit \| \| 7 \| 8 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1boy, 1girl, hetero, penis, pussy, solo_focus, vaginal, anus, looking_at_viewer, looking_back, sex_from_behind, blush, pov, sweat, black_shirt, girl_on_top, indoors, reverse_cowgirl_position, sleeveless_shirt, bare_shoulders, mosaic_censoring, shirt_lift, all_fours, ass_focus, backboob, bottomless, completely_nude, dark-skinned_male, doggystyle, uncensored \| \| 8 \| 12 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1boy, 1girl, blush, hetero, nipples, pussy, sex, solo_focus, vaginal, penis, looking_at_viewer, navel, censored, colored_inner_hair, pov, sweat, completely_nude, female_pubic_hair, choker, hair_ornament, missionary, on_back, spread_legs, elbow_gloves, open_mouth, white_gloves, white_thighhighs \| \| 9 \| 6 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, blush, gangbang, hetero, mosaic_censoring, penis, solo_focus, 3boys, erection, fellatio, nipples, sweat, completely_nude, testicles, cum, double_handjob, male_pubic_hair, navel, pussy, vaginal, veins \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| black_headwear \| black_pants \| black_shirt \| crop_top \| holding_gun \| midriff \| navel \| sig_sauer \| sleeveless_shirt \| solo \| stomach \| white_coat \| assault_rifle \| black_belt \| off_shoulder \| open_coat \| black_gloves \| looking_at_viewer \| long_sleeves \| mouth_mask \| standing \| leggings \| buckle \| armband \| cowboy_shot \| simple_background \| white_background \| black_mask \| jacket \| chest_harness \| snap-fit_buckle \| groin \| holding_mask \| medium_breasts \| underbust \| unworn_mask \| alternate_costume \| black_bikini \| cleavage \| closed_mouth \| collarbone \| thighs \| blush \| wet \| outdoors \| sideboob \| string_bikini \| open_clothes \| skindentation \| underboob \| wading \| water \| white_dress \| elbow_gloves \| white_gloves \| white_choker \| earrings \| colored_inner_hair \| blue_halo \| strapless_dress \| covered_navel \| hair_flower \| ass \| ponytail \| from_behind \| looking_back \| weapon \| white_thighhighs \| thigh_holster \| knife \| garter_straps \| thigh_strap \| handgun \| mask \| side_slit \| 1boy \| hetero \| penis \| pussy \| solo_focus \| vaginal \| anus \| sex_from_behind \| pov \| sweat \| girl_on_top \| indoors \| reverse_cowgirl_position \| mosaic_censoring \| shirt_lift \| all_fours \| ass_focus \| backboob \| bottomless \| completely_nude \| dark-skinned_male \| doggystyle \| uncensored \| nipples \| sex \| censored \| female_pubic_hair \| choker \| hair_ornament \| missionary \| on_back \| spread_legs \| open_mouth \| gangbang \| 3boys \| erection \| fellatio \| testicles \| cum \| double_handjob \| male_pubic_hair \| veins \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:-----------------\|:--------------\|:--------------\|:-----------\|:--------------\|:----------\|:--------\|:------------\|:-------------------\|:-------\|:----------\|:-------------\|:----------------\|:-------------\|:---------------\|:------------\|:---------------\|:--------------------\|:---------------\|:-------------\|:-----------\|:-----------\|:---------\|:----------\|:--------------\|:--------------------\|:-------------------\|:-------------\|:---------\|:----------------\|:------------------\|:--------\|:---------------\|:-----------------\|:------------\|:--------------\|:--------------------\|:---------------\|:-----------\|:---------------\|:-------------\|:---------\|:--------\|:------\|:-----------\|:-----------\|:----------------\|:---------------\|:----------------\|:------------\|:---------\|:--------\|:--------------\|:---------------\|:---------------\|:---------------\|:-----------\|:---------------------\|:------------\|:------------------\|:----------------\|:--------------\|:------\|:-----------\|:--------------\|:---------------\|:---------\|:-------------------\|:----------------\|:--------\|:----------------\|:--------------\|:----------\|:-------\|:------------\|:-------\|:---------\|:--------\|:--------\|:-------------\|:----------\|:-------\|:------------------\|:------\|:--------\|:--------------\|:----------\|:---------------------------\|:-------------------\|:-------------\|:------------\|:------------\|:-----------\|:-------------\|:------------------\|:--------------------\|:-------------\|:-------------\|:----------\|:------\|:-----------\|:--------------------\|:---------\|:----------------\|:-------------\|:----------\|:--------------\|:-------------\|:-----------\|:--------\|:-----------\|:-----------\|:------------\|:------\|:-----------------\|:------------------\|:--------\| \| 0 \| 19 \| ![](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 \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 7 \| ![](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 \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 11 \| ![](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 \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](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 \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 63 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 27 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| \| \| \| \| X \| \| \| \| \| X \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| X \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 8 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 12 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| 9 \| 6 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
nicholasbien/custom-txt	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 3295001.7540148846 num_examples: 2042 - name: test num_bytes: 824557.2459851155 num_examples: 511 download_size: 1904830 dataset_size: 4119559.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Dinite/vozisaac	--- license: openrail ---
society-ethics/lila_camera_traps	--- annotations_creators: - expert-generated license: - other language_creators: - expert-generated language: - en multilinguality: - monolingual size_categories: - 10M<n<100M source_datasets: - original task_categories: - image-classification tags: - biodiversity - camera trap data - wildlife monitoring pretty_name: LILA Camera Traps --- # Dataset Card for LILA ## Table of Contents - [Table of Contents](#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) - [Tutorial](#tutorial) - [Working with Taxonomies](#working-with-taxonomies) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://lila.science/ - Repository: N/A - Paper: N/A - Leaderboard: N/A - Point of Contact: [info@lila.science](info@lila.science) ### Dataset Summary LILA Camera Traps is an aggregate data set of images taken by camera traps, which are devices that automatically (e.g. via motion detection) capture images of wild animals to help ecological research. This data set is the first time when disparate camera trap data sets have been aggregated into a single training environment with a single [taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/). This data set consists of only camera trap image data sets, whereas the broader [LILA](lila.science/) website also has other data sets related to biology and conservation, intended as a resource for both machine learning (ML) researchers and those that want to harness ML for this topic. See below for information about each specific dataset that LILA contains: <details> <summary> Caltech Camera Traps </summary> This data set contains 243,100 images from 140 camera locations in the Southwestern United States, with labels for 21 animal categories (plus empty), primarily at the species level (for example, the most common labels are opossum, raccoon, and coyote), and approximately 66,000 bounding box annotations. Approximately 70% of images are labeled as empty. More information about this data set is available [here](https://beerys.github.io/CaltechCameraTraps/). This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). For questions about this data set, contact caltechcameratraps@gmail.com. If you use this data set, please cite the associated manuscript: ```bibtex @inproceedings{DBLP:conf/eccv/BeeryHP18, author = {Sara Beery and Grant Van Horn and Pietro Perona}, title = {Recognition in Terra Incognita}, booktitle = {Computer Vision - {ECCV} 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part {XVI}}, pages = {472--489}, year = {2018}, crossref = {DBLP:conf/eccv/2018-16}, url = {https://doi.org/10.1007/978-3-030-01270-0\_28}, doi = {10.1007/978-3-030-01270-0\_28}, timestamp = {Mon, 08 Oct 2018 17:08:07 +0200}, biburl = {https://dblp.org/rec/bib/conf/eccv/BeeryHP18}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` </details> <details> <summary> ENA24 </summary> This data set contains approximately 10,000 camera trap images representing 23 classes from Eastern North America, with bounding boxes on each image. The most common classes are “American Crow”, “American Black Bear”, and “Dog”. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). Please cite this manuscript if you use this data set: ```bibtex @article{yousif2019dynamic, title={Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild}, author={Yousif, Hayder and Kays, Roland and He, Zhihai}, journal={IEEE Transactions on Circuits and Systems for Video Technology}, year={2019}, publisher={IEEE} } ``` For questions about this data set, contact [Hayder Yousif](hyypp5@mail.missouri.edu). </details> <details> <summary> Missouri Camera Traps </summary> This data set contains approximately 25,000 camera trap images representing 20 species (for example, the most common labels are red deer, mouflon, and white-tailed deer). Images within each sequence share the same species label (even though the animal may not have been recorded in all the images in the sequence). Around 900 bounding boxes are included. These are very challenging sequences with highly cluttered and dynamic scenes. Spatial resolutions of the images vary from 1920 × 1080 to 2048 × 1536. Sequence lengths vary from 3 to more than 300 frames. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). If you use this data set, please cite the associated manuscript: ```bibtex @article{zhang2016animal, title={Animal detection from highly cluttered natural scenes using spatiotemporal object region proposals and patch verification}, author={Zhang, Zhi and He, Zhihai and Cao, Guitao and Cao, Wenming}, journal={IEEE Transactions on Multimedia}, volume={18}, number={10}, pages={2079--2092}, year={2016}, publisher={IEEE} } ``` For questions about this data set, contact [Hayder Yousif](hyypp5@mail.missouri.edu) and [Zhi Zhang](zzbhf@mail.missouri.edu). </details> <details> <summary> North American Camera Trap Images (NACTI) </summary> This data set contains 3.7M camera trap images from five locations across the United States, with labels for 28 animal categories, primarily at the species level (for example, the most common labels are cattle, boar, and red deer). Approximately 12% of images are labeled as empty. We have also added bounding box annotations to 8892 images (mostly vehicles and birds). This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). Please cite this manuscript if you use this data set: ```bibtex @article{tabak2019machine, title={Machine learning to classify animal species in camera trap images: Applications in ecology}, author={Tabak, Michael A and Norouzzadeh, Mohammad S and Wolfson, David W and Sweeney, Steven J and VerCauteren, Kurt C and Snow, Nathan P and Halseth, Joseph M and Di Salvo, Paul A and Lewis, Jesse S and White, Michael D and others}, journal={Methods in Ecology and Evolution}, volume={10}, number={4}, pages={585--590}, year={2019}, publisher={Wiley Online Library} } ``` For questions about this data set, contact [northamericancameratrapimages@gmail.com](northamericancameratrapimages@gmail.com). </details> <details> <summary> WCS Camera Traps </summary> This data set contains approximately 1.4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly. Data were provided by the [Wildlife Conservation Society](https://www.wcs.org/). The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle). A complete list of classes and associated image counts is available here. Approximately 50% of images are empty. We have also added approximately 375,000 bounding box annotations to approximately 300,000 of those images, which come from sequences covering almost all locations. Sequences are inferred from timestamps, so may not strictly represent bursts. Images were labeled at a combination of image and sequence level, so – as is the case with most camera trap data sets – empty images may be labeled as non-empty (if an animal was present in one frame of a sequence but not in others). Images containing humans are referred to in metadata, but are not included in the data files. You can find more information about the data set [on the LILA website](https://lila.science/datasets/wcscameratraps). This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Wellington Camera Traps </summary> This data set contains 270,450 images from 187 camera locations in Wellington, New Zealand. The cameras (Bushnell 119537, 119476, and 119436) recorded sequences of three images when triggered. Each sequence was labelled by citizen scientists and/or professional ecologists from Victoria University of Wellington into 17 classes: 15 animal categories (for example, the most common labels are bird, cat, and hedgehog), empty, and unclassifiable. Approximately 17% of images are labeled as empty. Images within each sequence share the same species label (even though the animal may not have been recorded in all three images). If you use this data set, please cite the associated manuscript: ```bibtex @article{anton2018monitoring, title={Monitoring the mammalian fauna of urban areas using remote cameras and citizen science}, author={Anton, Victor and Hartley, Stephen and Geldenhuis, Andre and Wittmer, Heiko U}, journal={Journal of Urban Ecology}, volume={4}, number={1}, pages={juy002}, year={2018}, publisher={Oxford University Press} } ``` This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). For questions about this data set, contact [Victor Anton](vykanton@gmail.com). </details> <details> <summary> Island Conservation Camera Traps </summary> This data set contains approximately 123,000 camera trap images from 123 camera locations from 7 islands in 6 countries. Data were provided by Island Conservation during projects conducted to prevent the extinction of threatened species on islands. The most common classes are rabbit, rat, petrel, iguana, cat, goat, and pig, with both rat and cat represented between multiple island sites representing significantly different ecosystems (tropical forest, dry forest, and temperate forests). Additionally, this data set represents data from locations and ecosystems that, to our knowledge, are not well represented in publicly available datasets including >1,000 images each of iguanas, petrels, and shearwaters. A complete list of classes and associated image counts is available here. Approximately 60% of the images are empty. We have also included approximately 65,000 bounding box annotations for about 50,000 images. In general cameras were dispersed across each project site to detect the presence of invasive vertebrate species that threaten native island species. Cameras were set to capture bursts of photos for each motion detection event (between three and eight photos) with a set delay between events (10 to 30 seconds) to minimize the number of photos. Images containing humans are referred to in metadata, but are not included in the data files. For questions about this data set, contact [David Will](david.will@islandconservation.org) at Island Conservation. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). The original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license. </details> <details> <summary> Channel Islands Camera Traps </summary> This data set contains 246,529 camera trap images from 73 camera locations in the Channel Islands, California. All animals are annotated with bounding boxes. Data were provided by The Nature Conservancy. Animals are classified as rodent1 (82914), fox (48150), bird (11099), skunk (1071), or other (159). 114,949 images (47%) are empty. All images of rats were taken on islands already known to have rat populations. If you use these data in a publication or report, please use the following citation: The Nature Conservancy (2021): Channel Islands Camera Traps 1.0. The Nature Conservancy. Dataset. For questions about this data set, contact [Nathaniel Rindlaub](nathaniel.rindlaub@TNC.ORG) at The Nature Conservancy. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). The original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. </details> <details> <summary> Idaho Camera Traps </summary> This data set contains approximately 1.5 million camera trap images from Idaho. Labels are provided for 62 categories, most of which are animal classes (“deer”, “elk”, and “cattle” are the most common animal classes), but labels also include some state indicators (e.g. “snow on lens”, “foggy lens”). Approximately 70.5% of images are labeled as empty. Annotations were assigned to image sequences, rather than individual images, so annotations are meaningful only at the sequence level. The metadata contains references to images containing humans, but these have been removed from the dataset (along with images containing vehicles and domestic dogs). Images were provided by the Idaho Department of Fish and Game. No representations or warranties are made regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose. Some information shared under this agreement may not have undergone quality assurance procedures and should be considered provisional. Images may not be sold in any format, but may be used for scientific publications. Please acknowledge the Idaho Department of Fish and Game when using images for publication or scientific communication. </details> <details> <summary> Snapshot Serengeti </summary> This data set contains approximately 2.65M sequences of camera trap images, totaling 7.1M images, from seasons one through eleven of the [Snapshot Serengeti project](https://snapshotserengeti.org/) -- the flagship project of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Serengeti National Park in Tanzania is best known for the massive annual migrations of wildebeest and zebra that drive the cycling of its dynamic ecosystem. Labels are provided for 61 categories, primarily at the species level (for example, the most common labels are wildebeest, zebra, and Thomson’s gazelle). Approximately 76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshotserengeti-v-2-0/SnapshotSerengeti_S1-11_v2.1.species_list.csv). We have also added approximately 150,000 bounding box annotations to approximately 78,000 of those images. The images and species-level labels are described in more detail in the associated manuscript: ```bibtex @misc{dryad_5pt92, title = {Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna}, author = {Swanson, AB and Kosmala, M and Lintott, CJ and Simpson, RJ and Smith, A and Packer, C}, year = {2015}, journal = {Scientific Data}, URL = {https://doi.org/10.5061/dryad.5pt92}, doi = {doi:10.5061/dryad.5pt92}, publisher = {Dryad Digital Repository} } ``` For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Snapshot Karoo </summary> This data set contains 14889 sequences of camera trap images, totaling 38074 images, from the [Snapshot Karoo](https://www.zooniverse.org/projects/shuebner729/snapshot-karoo) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Karoo National Park, located in the arid Nama Karoo biome of South Africa, is defined by its endemic vegetation and mountain landscapes. Its unique topographical gradient has led to a surprising amount of biodiversity, with 58 mammals and more than 200 bird species recorded, as well as a multitude of reptilian species. Labels are provided for 38 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, hartebeestred, and kudu). Approximately 83.02% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KAR/SnapshotKaroo_S1_v1.0.species_list.csv). For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Snapshot Kgalagadi </summary> This data set contains 3611 sequences of camera trap images, totaling 10222 images, from the [Snapshot Kgalagadi](https://www.zooniverse.org/projects/shuebner729/snapshot-kgalagadi/) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. The Kgalagadi Transfrontier Park stretches from the Namibian border across South Africa and into Botswana, covering a landscape commonly referred to as the Kalahari – an arid savanna. This region is of great interest to help us understand how animals cope with extreme temperatures at both ends of the scale. Labels are provided for 31 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, birdother, and ostrich). Approximately 76.14% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KGA/SnapshotKgalagadi_S1_v1.0.species_list.csv). For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Snapshot Enonkishu </summary> This data set contains 13301 sequences of camera trap images, totaling 28544 images, from the [Snapshot Enonkishu](https://www.zooniverse.org/projects/aguthmann/snapshot-enonkishu) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Enonkishu Conservancy is located on the northern boundary of the Mara-Serengeti ecosystem in Kenya, and is managed by a consortium of stakeholders and land-owning Maasai families. Their aim is to promote coexistence between wildlife and livestock in order to encourage regenerative grazing and build stability in the Mara conservancies. Labels are provided for 39 categories, primarily at the species level (for example, the most common labels are impala, warthog, and zebra). Approximately 64.76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/ENO/SnapshotEnonkishu_S1_v1.0.species_list.csv). For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Snapshot Camdeboo </summary> This data set contains 12132 sequences of camera trap images, totaling 30227 images, from the [Snapshot Camdeboo](https://www.zooniverse.org/projects/shuebner729/snapshot-camdeboo) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Camdeboo National Park, South Africa is crucial habitat for many birds on a global scale, with greater than fifty endemic and near-endemic species and many migratory species. Labels are provided for 43 categories, primarily at the species level (for example, the most common labels are kudu, springbok, and ostrich). Approximately 43.74% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/CDB/SnapshotCamdeboo_S1_v1.0.species_list.csv). For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Snapshot Mountain Zebra </summary> This data set contains 71688 sequences of camera trap images, totaling 73034 images, from the [Snapshot Mountain Zebra](https://www.zooniverse.org/projects/meredithspalmer/snapshot-mountain-zebra/) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Mountain Zebra National Park is located in the Eastern Cape of South Africa in a transitional area between several distinct biomes, which means it is home to many endemic species. As the name suggests, this park contains the largest remnant population of Cape Mountain zebras, ~700 as of 2019 and increasing steadily every year. Labels are provided for 54 categories, primarily at the species level (for example, the most common labels are zebramountain, kudu, and springbok). Approximately 91.23% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/MTZ/SnapshotMountainZebra_S1_v1.0.species_list.csv). For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Snapshot Kruger </summary> This data set contains 4747 sequences of camera trap images, totaling 10072 images, from the [Snapshot Kruger](https://www.zooniverse.org/projects/shuebner729/snapshot-kruger) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Kruger National Park, South Africa has been a refuge for wildlife since its establishment in 1898, and it houses one of the most diverse wildlife assemblages remaining in Africa. The Snapshot Safari grid was established in 2018 as part of a research project assessing the impacts of large mammals on plant life as boundary fences were removed and wildlife reoccupied areas of previous extirpation. Labels are provided for 46 categories, primarily at the species level (for example, the most common labels are impala, elephant, and buffalo). Approximately 61.60% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KRU/SnapshotKruger_S1_v1.0.species_list.csv). For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> SWG Camera Traps </summary> This data set contains 436,617 sequences of camera trap images from 982 locations in Vietnam and Lao, totaling 2,039,657 images. Labels are provided for 120 categories, primarily at the species level (for example, the most common labels are “Eurasian Wild Pig”, “Large-antlered Muntjac”, and “Unidentified Murid”). Approximately 12.98% of images are labeled as empty. A full list of species and associated image counts is available here. 101,659 bounding boxes are provided on 88,135 images. This data set is provided by the Saola Working Group; providers include: - IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group (SWG) - Asian Arks - Wildlife Conservation Society (Lao) - WWF Lao - Integrated Conservation of Biodiversity and Forests project, Lao (ICBF) - Center for Environment and Rural Development, Vinh University, Vietnam If you use these data in a publication or report, please use the following citation: SWG (2021): Northern and Central Annamites Camera Traps 2.0. IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group. Dataset. For questions about this data set, contact saolawg@gmail.com. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Orinoquia Camera Traps </summary> This data set contains 104,782 images collected from a 50-camera-trap array deployed from January to July 2020 within the private natural reserves El Rey Zamuro (31 km2) and Las Unamas (40 km2), located in the Meta department in the Orinoquía region in central Colombia. We deployed cameras using a stratified random sampling design across forest core area strata. Cameras were spaced 1 km apart from one another, located facing wildlife trails, and deployed with no bait. Images were stored and reviewed by experts using the Wildlife Insights platform. This data set contains 51 classes, predominantly mammals such as the collared peccary, black agouti, spotted paca, white-lipped peccary, lowland tapir, and giant anteater. Approximately 20% of images are empty. The main purpose of the study is to understand how humans, wildlife, and domestic animals interact in multi-functional landscapes (e.g., agricultural livestock areas with native forest remnants). However, this data set was also used to review model performance of AI-powered platforms – Wildlife Insights (WI), MegaDetector (MD), and Machine Learning for Wildlife Image Classification (MLWIC2). We provide a demonstration of the use of WI, MD, and MLWIC2 and R code for evaluating model performance of these platforms in the accompanying [GitHub repository](https://github.com/julianavelez1/Processing-Camera-Trap-Data-Using-AI). If you use these data in a publication or report, please use the following citation: ```bibtex @article{velez2022choosing, title={Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence}, author={V{\'e}lez, Juliana and Castiblanco-Camacho, Paula J and Tabak, Michael A and Chalmers, Carl and Fergus, Paul and Fieberg, John}, journal={arXiv preprint arXiv:2202.02283}, year={2022} } ``` For questions about this data set, contact [Juliana Velez Gomez](julianavelezgomez@gmail.com). This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> ### Supported Tasks and Leaderboards No leaderboards exist for LILA. ### Languages The [LILA taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/) is provided in English. ## Dataset Structure ### Data Instances The data annotations are provided in [COCO Camera Traps](https://github.com/Microsoft/CameraTraps/blob/master/data_management/README.md#coco-cameratraps-format) format. All of the datasets share a common category taxonomy, which is defined on the [LILA website](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/). ### Data Fields Different datasets may have slightly varying fields, which include: `file_name`: the file name \ `width` and `height`: the dimensions of the image \ `study`: which research study the image was collected as part of \ `location` : the name of the location at which the image was taken \ `annotations`: information about image annotation, which includes the taxonomy information, bounding box/boxes (`bbox`/`bboxes`) if any, as well as any other annotation information. \ `image` : the `path` to download the image and any other information that is available, e.g. its size in `bytes`. ### Data Splits This dataset does not have a predefined train/test split. ## Dataset Creation ### Curation Rationale The datasets that constitute LILA have been provided by the organizations, projects and researchers who collected them. ### Source Data #### Initial data collection and normalization N/A #### Who are the source language producers? N/A ### Annotations #### Annotation process Each dataset has been annotated by the members of the project/organization that provided it. #### Who are the annotators? The annotations have been provided by domain experts in fields such as biology and ecology. ### Personal and Sensitive Information Some of the original data sets included a “human” class label; for privacy reasons, these images were removed. Those labels are still present in the metadata. If those images are important to your work, contact the [LILA maintainers](mailto:info@lila.science), since in some cases it will be possible to release those images under an alternative license. ## Considerations for Using the Data ### Social Impact of Dataset Machine learning depends on labeled data, but accessing such data in biology and conservation is a challenge. Consequently, everyone benefits when labeled data is made available. Biologists and conservation scientists benefit by having data to train on, and free hosting allows teams to multiply the impact of their data (we suggest listing this benefit in grant proposals that fund data collection). ML researchers benefit by having data to experiment with. ### Discussion of Biases These datasets do not represent global diversity, but are examples of local ecosystems and animals. ### Other Known Limitations N/A ## Additional Information ### Tutorial The [tutorial in this Google Colab notebook](https://colab.research.google.com/drive/17gPOIK-ksxPyX6yP9TaKIimlwf9DYe2R?usp=sharing) demonstrates how to work with this dataset, including filtering by species, collating configurations, and downloading images. ### Working with Taxonomies All the taxonomy categories are saved as ClassLabels, which can be converted to strings as needed. Strings can likewise be converted to integers as needed, to filter the dataset. In the example below we filter the "Caltech Camera Traps" dataset to find all the entries with a "felis catus" as the species for the first annotation. ```python dataset = load_dataset("society-ethics/lila_camera_traps", "Caltech Camera Traps", split="train") taxonomy = dataset.features["annotations"].feature["taxonomy"] # Filters to show only cats cats = dataset.filter(lambda x: x["annotations"]["taxonomy"][0]["species"] == taxonomy["species"].str2int("felis catus")) ``` The original common names have been saved with their taxonomy mappings in this repository in `common_names_to_tax.json`. These can be used, for example, to map from a taxonomy combination to a common name to help make queries more legible. Note, however, that there is a small number of duplicate common names with different taxonomy values which you will need to disambiguate. The following example loads the first "sea turtle" in the "Island Conservation Camera Traps" dataset. ```python LILA_COMMON_NAMES_TO_TAXONOMY = pd.read_json("https://huggingface.co/datasets/society-ethics/lila_camera_traps/raw/main/data/common_names_to_tax.json", lines=True).set_index("common_name") dataset = load_dataset("society-ethics/lila_camera_traps", "Island Conservation Camera Traps", split="train") taxonomy = dataset.features["annotations"].feature["taxonomy"] sea_turtle = LILA_COMMON_NAMES_TO_TAXONOMY.loc["sea turtle"].to_dict() sea_turtle = {k: taxonomy[k].str2int(v) if v is not None else v for k, v in sea_turtle.items()} # Map to ClassLabel integers sea_turtle_dataset = ds.filter(lambda x: x["annotations"]["taxonomy"][0] == sea_turtle) ``` The example below selects a random item from the dataset, and then maps from the taxonomy to a common name: ```python LILA_COMMON_NAMES_TO_TAXONOMY = pd.read_json("https://huggingface.co/datasets/society-ethics/lila_camera_traps/raw/main/data/common_names_to_tax.json", lines=True).set_index("common_name") dataset = load_dataset("society-ethics/lila_camera_traps", "Caltech Camera Traps", split="train") taxonomy = dataset.features["annotations"].feature["taxonomy"] random_entry = dataset.shuffle()[0] filter_taxonomy = random_entry["annotations"]["taxonomy"][0] filter_keys = list(map(lambda x: (x[0], taxonomy[x[0]].int2str(x[1])), filter(lambda x: x[1] is not None, list(filter_taxonomy.items())))) if len(filter_keys) > 0: print(LILA_COMMON_NAMES_TO_TAXONOMY[np.logical_and.reduce([ LILA_COMMON_NAMES_TO_TAXONOMY[k] == v for k,v in filter_keys ])]) else: print("No common name found for the item.") ``` ### Dataset Curators LILA BC is maintained by a working group that includes representatives from Ecologize, Zooniverse, the Evolving AI Lab, Snapshot Safari, and Microsoft AI for Earth. Hosting on Microsoft Azure is provided by Microsoft AI for Earth. ### Licensing Information Many, but not all, LILA data sets were released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). Check the details of the specific dataset you are using in its section above. ### Citation Information Citations for each dataset (if they exist) are provided in its section above. ### Contributions Thanks to [@NimaBoscarino](https://github.com/NimaBoscarino/) for adding this dataset.
open-llm-leaderboard/details_migtissera__Tess-10.7B-v1.5	--- pretty_name: Evaluation run of migtissera/Tess-10.7B-v1.5 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [migtissera/Tess-10.7B-v1.5](https://huggingface.co/migtissera/Tess-10.7B-v1.5)\ \ 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_migtissera__Tess-10.7B-v1.5\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-27T08:16:07.104140](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-10.7B-v1.5/blob/main/results_2024-01-27T08-16-07.104140.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\": 0.6513655424521041,\n\ \ \"acc_stderr\": 0.03165794538741113,\n \"acc_norm\": 0.6541051437423594,\n\ \ \"acc_norm_stderr\": 0.032296434557489546,\n \"mc1\": 0.32558139534883723,\n\ \ \"mc1_stderr\": 0.01640398946990783,\n \"mc2\": 0.47430080710659894,\n\ \ \"mc2_stderr\": 0.014677705750823734\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.60580204778157,\n \"acc_stderr\": 0.014280522667467325,\n\ \ \"acc_norm\": 0.6501706484641638,\n \"acc_norm_stderr\": 0.013936809212158289\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6490738896634136,\n\ \ \"acc_stderr\": 0.004762844770909862,\n \"acc_norm\": 0.8406691894045011,\n\ \ \"acc_norm_stderr\": 0.0036523632532895916\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5777777777777777,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7631578947368421,\n \"acc_stderr\": 0.03459777606810536,\n\ \ \"acc_norm\": 0.7631578947368421,\n \"acc_norm_stderr\": 0.03459777606810536\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.69,\n\ \ \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438665,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438665\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_computer_science\|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness\|hendrycksTest-college_mathematics\|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-college_medicine\|5\"\ : {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.035506839891655796,\n\ \ \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.035506839891655796\n\ \ },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.37254901960784315,\n\ \ \"acc_stderr\": 0.04810840148082635,\n \"acc_norm\": 0.37254901960784315,\n\ \ \"acc_norm_stderr\": 0.04810840148082635\n },\n \"harness\|hendrycksTest-computer_security\|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5957446808510638,\n\ \ \"acc_stderr\": 0.032081157507886836,\n \"acc_norm\": 0.5957446808510638,\n\ \ \"acc_norm_stderr\": 0.032081157507886836\n },\n \"harness\|hendrycksTest-econometrics\|5\"\ : {\n \"acc\": 0.4649122807017544,\n \"acc_stderr\": 0.046920083813689104,\n\ \ \"acc_norm\": 0.4649122807017544,\n \"acc_norm_stderr\": 0.046920083813689104\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\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.02542483508692399,\n \"\ acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.02542483508692399\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4126984126984127,\n\ \ \"acc_stderr\": 0.04403438954768177,\n \"acc_norm\": 0.4126984126984127,\n\ \ \"acc_norm_stderr\": 0.04403438954768177\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8,\n\ \ \"acc_stderr\": 0.02275520495954294,\n \"acc_norm\": 0.8,\n \ \ \"acc_norm_stderr\": 0.02275520495954294\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175008,\n\ \ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\ \ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822513,\n \"\ acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822513\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.01871899852067819,\n\ \ \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.01871899852067819\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6410256410256411,\n \"acc_stderr\": 0.024321738484602357,\n\ \ \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.024321738484602357\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \ \ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7016806722689075,\n \"acc_stderr\": 0.029719142876342853,\n\ \ \"acc_norm\": 0.7016806722689075,\n \"acc_norm_stderr\": 0.029719142876342853\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119995,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119995\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\ acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5787037037037037,\n \"acc_stderr\": 0.03367462138896078,\n \"\ acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.03367462138896078\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8284313725490197,\n \"acc_stderr\": 0.026460569561240644,\n \"\ acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.026460569561240644\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8481012658227848,\n \"acc_stderr\": 0.02336387809663245,\n \ \ \"acc_norm\": 0.8481012658227848,\n \"acc_norm_stderr\": 0.02336387809663245\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7130044843049327,\n\ \ \"acc_stderr\": 0.03036037971029195,\n \"acc_norm\": 0.7130044843049327,\n\ \ \"acc_norm_stderr\": 0.03036037971029195\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596915,\n\ \ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596915\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.03957835471980981,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.03957835471980981\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5178571428571429,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n\ \ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8275862068965517,\n\ \ \"acc_stderr\": 0.013507943909371803,\n \"acc_norm\": 0.8275862068965517,\n\ \ \"acc_norm_stderr\": 0.013507943909371803\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044283,\n\ \ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044283\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.29497206703910617,\n\ \ \"acc_stderr\": 0.015251931579208181,\n \"acc_norm\": 0.29497206703910617,\n\ \ \"acc_norm_stderr\": 0.015251931579208181\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7712418300653595,\n \"acc_stderr\": 0.024051029739912258,\n\ \ \"acc_norm\": 0.7712418300653595,\n \"acc_norm_stderr\": 0.024051029739912258\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7009646302250804,\n\ \ \"acc_stderr\": 0.026003301117885135,\n \"acc_norm\": 0.7009646302250804,\n\ \ \"acc_norm_stderr\": 0.026003301117885135\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\"\ : 0.48936170212765956,\n \"acc_stderr\": 0.02982074719142246,\n \"\ acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.02982074719142246\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.48565840938722293,\n\ \ \"acc_stderr\": 0.01276498182952427,\n \"acc_norm\": 0.48565840938722293,\n\ \ \"acc_norm_stderr\": 0.01276498182952427\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7279411764705882,\n \"acc_stderr\": 0.02703304115168146,\n\ \ \"acc_norm\": 0.7279411764705882,\n \"acc_norm_stderr\": 0.02703304115168146\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.673202614379085,\n \"acc_stderr\": 0.018975427920507208,\n \ \ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.018975427920507208\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\": 0.7591836734693878,\n \"acc_stderr\": 0.02737294220178816,\n\ \ \"acc_norm\": 0.7591836734693878,\n \"acc_norm_stderr\": 0.02737294220178816\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8557213930348259,\n\ \ \"acc_stderr\": 0.024845753212306053,\n \"acc_norm\": 0.8557213930348259,\n\ \ \"acc_norm_stderr\": 0.024845753212306053\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.03015113445777634,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.03015113445777634\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7953216374269005,\n \"acc_stderr\": 0.030944459778533193,\n\ \ \"acc_norm\": 0.7953216374269005,\n \"acc_norm_stderr\": 0.030944459778533193\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.32558139534883723,\n\ \ \"mc1_stderr\": 0.01640398946990783,\n \"mc2\": 0.47430080710659894,\n\ \ \"mc2_stderr\": 0.014677705750823734\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8334648776637726,\n \"acc_stderr\": 0.010470796496781091\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5435936315390447,\n \ \ \"acc_stderr\": 0.013720038270485327\n }\n}\n```" repo_url: https://huggingface.co/migtissera/Tess-10.7B-v1.5 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_01_27T08_16_07.104140 path: - '/details_harness\|arc:challenge\|25_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-27T08-16-07.104140.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|gsm8k\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hellaswag\|10_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T08-16-07.104140.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-27T08-16-07.104140.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '/details_harness\|winogrande\|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-27T08-16-07.104140.parquet' - config_name: results data_files: - split: 2024_01_27T08_16_07.104140 path: - results_2024-01-27T08-16-07.104140.parquet - split: latest path: - results_2024-01-27T08-16-07.104140.parquet --- # Dataset Card for Evaluation run of migtissera/Tess-10.7B-v1.5 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [migtissera/Tess-10.7B-v1.5](https://huggingface.co/migtissera/Tess-10.7B-v1.5) 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_migtissera__Tess-10.7B-v1.5", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-27T08:16:07.104140](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-10.7B-v1.5/blob/main/results_2024-01-27T08-16-07.104140.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": 0.6513655424521041, "acc_stderr": 0.03165794538741113, "acc_norm": 0.6541051437423594, "acc_norm_stderr": 0.032296434557489546, "mc1": 0.32558139534883723, "mc1_stderr": 0.01640398946990783, "mc2": 0.47430080710659894, "mc2_stderr": 0.014677705750823734 }, "harness\|arc:challenge\|25": { "acc": 0.60580204778157, "acc_stderr": 0.014280522667467325, "acc_norm": 0.6501706484641638, "acc_norm_stderr": 0.013936809212158289 }, "harness\|hellaswag\|10": { "acc": 0.6490738896634136, "acc_stderr": 0.004762844770909862, "acc_norm": 0.8406691894045011, "acc_norm_stderr": 0.0036523632532895916 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.31, "acc_stderr": 0.046482319871173156, "acc_norm": 0.31, "acc_norm_stderr": 0.046482319871173156 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7631578947368421, "acc_stderr": 0.03459777606810536, "acc_norm": 0.7631578947368421, "acc_norm_stderr": 0.03459777606810536 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.028254200344438665, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.028254200344438665 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6820809248554913, "acc_stderr": 0.035506839891655796, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.035506839891655796 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082635, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082635 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5957446808510638, "acc_stderr": 0.032081157507886836, "acc_norm": 0.5957446808510638, "acc_norm_stderr": 0.032081157507886836 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4649122807017544, "acc_stderr": 0.046920083813689104, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.046920083813689104 }, "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": 0.42063492063492064, "acc_stderr": 0.02542483508692399, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.02542483508692399 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4126984126984127, "acc_stderr": 0.04403438954768177, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.04403438954768177 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8, "acc_stderr": 0.02275520495954294, "acc_norm": 0.8, "acc_norm_stderr": 0.02275520495954294 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.47783251231527096, "acc_stderr": 0.03514528562175008, "acc_norm": 0.47783251231527096, "acc_norm_stderr": 0.03514528562175008 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8121212121212121, "acc_stderr": 0.03050193405942914, "acc_norm": 0.8121212121212121, "acc_norm_stderr": 0.03050193405942914 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8686868686868687, "acc_stderr": 0.024063156416822513, "acc_norm": 0.8686868686868687, "acc_norm_stderr": 0.024063156416822513 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.927461139896373, "acc_stderr": 0.01871899852067819, "acc_norm": 0.927461139896373, "acc_norm_stderr": 0.01871899852067819 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6410256410256411, "acc_stderr": 0.024321738484602357, "acc_norm": 0.6410256410256411, "acc_norm_stderr": 0.024321738484602357 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34444444444444444, "acc_stderr": 0.02897264888484427, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.02897264888484427 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7016806722689075, "acc_stderr": 0.029719142876342853, "acc_norm": 0.7016806722689075, "acc_norm_stderr": 0.029719142876342853 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119995, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119995 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5787037037037037, "acc_stderr": 0.03367462138896078, "acc_norm": 0.5787037037037037, "acc_norm_stderr": 0.03367462138896078 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8284313725490197, "acc_stderr": 0.026460569561240644, "acc_norm": 0.8284313725490197, "acc_norm_stderr": 0.026460569561240644 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8481012658227848, "acc_stderr": 0.02336387809663245, "acc_norm": 0.8481012658227848, "acc_norm_stderr": 0.02336387809663245 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7130044843049327, "acc_stderr": 0.03036037971029195, "acc_norm": 0.7130044843049327, "acc_norm_stderr": 0.03036037971029195 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596915, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596915 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.03957835471980981, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.03957835471980981 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5178571428571429, "acc_stderr": 0.047427623612430116, "acc_norm": 0.5178571428571429, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8446601941747572, "acc_stderr": 0.03586594738573974, "acc_norm": 0.8446601941747572, "acc_norm_stderr": 0.03586594738573974 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406957, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406957 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371803, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371803 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7427745664739884, "acc_stderr": 0.023532925431044283, "acc_norm": 0.7427745664739884, "acc_norm_stderr": 0.023532925431044283 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.29497206703910617, "acc_stderr": 0.015251931579208181, "acc_norm": 0.29497206703910617, "acc_norm_stderr": 0.015251931579208181 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7712418300653595, "acc_stderr": 0.024051029739912258, "acc_norm": 0.7712418300653595, "acc_norm_stderr": 0.024051029739912258 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7009646302250804, "acc_stderr": 0.026003301117885135, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.026003301117885135 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48936170212765956, "acc_stderr": 0.02982074719142246, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.02982074719142246 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.48565840938722293, "acc_stderr": 0.01276498182952427, "acc_norm": 0.48565840938722293, "acc_norm_stderr": 0.01276498182952427 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7279411764705882, "acc_stderr": 0.02703304115168146, "acc_norm": 0.7279411764705882, "acc_norm_stderr": 0.02703304115168146 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.673202614379085, "acc_stderr": 0.018975427920507208, "acc_norm": 0.673202614379085, "acc_norm_stderr": 0.018975427920507208 }, "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": 0.7591836734693878, "acc_stderr": 0.02737294220178816, "acc_norm": 0.7591836734693878, "acc_norm_stderr": 0.02737294220178816 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8557213930348259, "acc_stderr": 0.024845753212306053, "acc_norm": 0.8557213930348259, "acc_norm_stderr": 0.024845753212306053 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.9, "acc_stderr": 0.03015113445777634, "acc_norm": 0.9, "acc_norm_stderr": 0.03015113445777634 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7953216374269005, "acc_stderr": 0.030944459778533193, "acc_norm": 0.7953216374269005, "acc_norm_stderr": 0.030944459778533193 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.32558139534883723, "mc1_stderr": 0.01640398946990783, "mc2": 0.47430080710659894, "mc2_stderr": 0.014677705750823734 }, "harness\|winogrande\|5": { "acc": 0.8334648776637726, "acc_stderr": 0.010470796496781091 }, "harness\|gsm8k\|5": { "acc": 0.5435936315390447, "acc_stderr": 0.013720038270485327 } } ``` ## 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 <!-- 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jirong/grit_2m	--- license: apache-2.0 ---
CyberHarem/jacques_de_molay_fgo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of jacques_de_molay/ジャック・ド・モレー/雅克·德·莫莱 (Fate/Grand Order) This is the dataset of jacques_de_molay/ジャック・ド・モレー/雅克·德·莫莱 (Fate/Grand Order), containing 122 images and their tags. The core tags of this character are `short_hair, grey_hair, glasses, breasts, large_breasts, blue_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:----------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 122 \| 202.72 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jacques_de_molay_fgo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 122 \| 173.50 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jacques_de_molay_fgo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 314 \| 348.70 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jacques_de_molay_fgo/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/jacques_de_molay_fgo', 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 \| 10 \| ![](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, bare_shoulders, black_dress, horns, smile, solo, yellow_eyes, cleavage, grey_skin, looking_at_viewer, choker, collarbone, detached_sleeves, open_mouth, thighs \| \| 1 \| 7 \| ![](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) \| bare_shoulders, black_dress, cleavage, collarbone, grey_skin, horns, looking_at_viewer, yellow_eyes, 1girl, solo, choker, detached_sleeves, smile, short_dress, simple_background, sword, thighs \| \| 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, belt, black_dress, black_jacket, cleavage, cropped_jacket, hooded_jacket, long_sleeves, looking_at_viewer, open_jacket, short_dress, smile, sheep, thighs, sword, purple_eyes \| \| 3 \| 5 \| ![](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, belt, black_dress, black_jacket, cropped_jacket, hooded_jacket, long_sleeves, looking_at_viewer, open_jacket, open_mouth, short_dress, solo, sword, thighs, cleavage, smile, yellow_eyes \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, belt, black_dress, black_jacket, cleavage, cropped_jacket, hooded_jacket, long_sleeves, looking_at_viewer, open_jacket, short_dress, solo, sword, smile, sheath \| \| 5 \| 9 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, black_dress, black_jacket, cropped_jacket, hooded_jacket, long_sleeves, looking_at_viewer, open_jacket, solo, cleavage, smile, upper_body \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| black_dress \| horns \| smile \| solo \| yellow_eyes \| cleavage \| grey_skin \| looking_at_viewer \| choker \| collarbone \| detached_sleeves \| open_mouth \| thighs \| short_dress \| simple_background \| sword \| belt \| black_jacket \| cropped_jacket \| hooded_jacket \| long_sleeves \| open_jacket \| sheep \| purple_eyes \| sheath \| upper_body \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:--------------\|:--------\|:--------\|:-------\|:--------------\|:-----------\|:------------\|:--------------------\|:---------\|:-------------\|:-------------------\|:-------------\|:---------\|:--------------\|:--------------------\|:--------\|:-------\|:---------------\|:-----------------\|:----------------\|:---------------\|:--------------\|:--------\|:--------------\|:---------\|:-------------\| \| 0 \| 10 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 7 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| 3 \| 5 \| ![](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 \| X \| X \| \| \| \| \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| X \| \| X \| X \| \| X \| \| X \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| \| \| X \| \| \| 5 \| 9 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| X \| \| X \| X \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| X \|
Multimodal-Fatima/VQAv2_test_split_8	--- dataset_info: features: - name: question_type dtype: string - name: multiple_choice_answer dtype: string - name: answers sequence: string - name: answers_original list: - name: answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: id_image dtype: int64 - name: answer_type dtype: string - name: question_id dtype: int64 - name: question dtype: string - name: image dtype: image - name: id dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: blip_caption dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes list: - name: attribute dtype: string - name: box sequence: float32 - name: label dtype: string - name: location dtype: string - name: ratio dtype: float32 - name: size dtype: string - name: tag dtype: string - name: Attributes_ViT_L_14_descriptors_text_davinci_003_full sequence: string - name: clip_tags_ViT_L_14_wo_openai sequence: string - name: clip_tags_ViT_L_14_with_openai sequence: string - name: clip_tags_LAION_ViT_H_14_2B_wo_openai sequence: string - name: clip_tags_LAION_ViT_H_14_2B_with_openai sequence: string - name: clip_tags_LAION_ViT_bigG_14_2B_wo_openai sequence: string - name: clip_tags_LAION_ViT_bigG_14_2B_with_openai sequence: string - name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full sequence: string - name: Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full sequence: string - name: clip_tags_ViT_B_16_with_openai sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random list: - name: attribute dtype: string - name: box sequence: float64 - name: captions_module sequence: string - name: captions_module_filter sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string splits: - name: test num_bytes: 9157441080.0 num_examples: 44779 download_size: 1848746963 dataset_size: 9157441080.0 --- # Dataset Card for "VQAv2_test_split_8" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
crumb/tiny-slimpajama-k8-00001	--- dataset_info: features: - name: text dtype: string - name: meta dtype: string - name: __index_level_0__ dtype: int64 - name: cluster dtype: int64 splits: - name: train num_bytes: 25963772232 num_examples: 5899634 download_size: 15090997326 dataset_size: 25963772232 configs: - config_name: default data_files: - split: train path: data/train-* ---
HSJuan/korquad-aug-valid	--- 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: pos_aug dtype: string - name: neg_aug dtype: string splits: - name: validation num_bytes: 9298037 num_examples: 5774 download_size: 1728913 dataset_size: 9298037 --- # Dataset Card for "korquad-aug-valid" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
tartuNLP/liv4ever	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en - liv license: - cc-by-nc-sa-4.0 multilinguality: - translation size_categories: - unknown source_datasets: - original task_categories: - text2text-generation - translation task_ids: [] pretty_name: Liv4ever language_bcp47: - en-US - liv tags: - conditional-text-generation --- # liv4ever v1 This is the Livonian 4-lingual parallel corpus. Livonian is a Uralic / Finnic language with just about 20 fluent speakers and no native speakers (as of 2021). The texts and translations in this corpus were collected from all the digital text resources that could be found by the authors; scanned and printed materials are left for future work. The corpus includes parallel data for Livonian-Latvian, Livonian-Estonian and Livonian-English; the data has been collected in 2021. After retrieval it was normalized in terms of different orthographies of Livonian and manually sentence-aligned where needed. It was collected from the following sources, with sentence counts per language pair: * Dictionary - example sentences from the Livonian-Latvian-Estonian dictionary; * liv-lv: 10'388, * liv-et: 10'378 * Stalte - the alphabet book by Kōrli Stalte, translated into Estonian and Latvian; * liv-lv: 842, * liv-et: 685 * Poetry - the poetry collection book "Ma võtan su õnge, tursk / Ma akūb sīnda vizzõ, tūrska", with Estonian translations; * liv-et: 770 * Vääri - the book by Eduard Vääri about Livonian language and culture; * liv-et: 592 * Satversme - translations of the Latvian Constitution into Livonian, Estonian and English; * liv-en: 380, * liv-lv: 414, * liv-et: 413 * Facebook - social media posts by the Livonian Institute and Livonian Days with original translations; * liv-en: 123, * liv-lv: 124, * liv-et: 7 * JEFUL - article abstracts from the Journal of Estonian and Finno-Ugric Linguistics, special issues dedicated to Livonian studies, translated into Estonian and English; * liv-en: 36, * liv-et: 49 * Trilium - the book with a collection of Livonian poetry, foreword and afterword translated into Estonian and Latvian; * liv-lv: 51, * liv-et: 53 * Songs - material crawled off lyricstranslate.com; * liv-en: 54, * liv-lv: 54, * liv-fr: 31
TuringsSolutions/PFAF-Function	--- license: mit ---
kaleemWaheed/twitter_dataset_1713138543	--- 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: 13116 num_examples: 32 download_size: 10247 dataset_size: 13116 configs: - config_name: default data_files: - split: train path: data/train-* ---

bot-yaya/undl_ar2en_aligned

--- dataset_info: features: - name: record dtype: string - name: clean_para_index_set_pair dtype: string - name: src dtype: string - name: dst dtype: string - name: src_text dtype: string - name: dst_text dtype: string - name: src_rate dtype: float64 - name: dst_rate dtype: float64 splits: - name: train num_bytes: 12012712129 num_examples: 15217906 download_size: 0 dataset_size: 12012712129 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "undl_ar2en_aligned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/martha_santa_fgo

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of martha_santa/マルタ〔サンタ〕/玛尔达〔圣诞〕 (Fate/Grand Order) This is the dataset of martha_santa/マルタ〔サンタ〕/玛尔达〔圣诞〕 (Fate/Grand Order), containing 64 images and their tags. The core tags of this character are `purple_hair, long_hair, blue_eyes, hat, red_headwear, santa_hat, breasts`, 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 | 64 | 61.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/martha_santa_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 64 | 59.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/martha_santa_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 143 | 109.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/martha_santa_fgo/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/martha_santa_fgo', 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 | 64 | ![](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, long_sleeves, smile, solo, brown_shirt, blush, christmas, looking_at_viewer, mittens, white_apron, red_skirt, fur_trim, brooch, open_mouth, off_shoulder, belt | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | smile | solo | brown_shirt | blush | christmas | looking_at_viewer | mittens | white_apron | red_skirt | fur_trim | brooch | open_mouth | off_shoulder | belt | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------|:-------|:--------------|:--------|:------------|:--------------------|:----------|:--------------|:------------|:-----------|:---------|:-------------|:---------------|:-------| | 0 | 64 | ![](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 |

ZHLiu627/ultrafeedback_binarized_with_response_full_part1

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: reference_response dtype: string splits: - name: train_prefs num_bytes: 167825271 num_examples: 20000 download_size: 93223431 dataset_size: 167825271 configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* --- # Dataset Card for "ultrafeedback_binarized_with_response_full_part1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sekarmulyani/ulasan-ecommerce-classification

--- license: apache-2.0 task_categories: - text-classification language: - id size_categories: - 100K<n<1M ---

johannes-garstenauer/PerformanceTest

--- license: apache-2.0 ---

rajistics/electricity_demand

--- task_categories: - time-series-forecasting --- The Victoria electricity demand dataset from the [MAPIE github repository](https://github.com/scikit-learn-contrib/MAPIE/tree/master/examples/data). It consists of hourly electricity demand (in GW) of the Victoria state in Australia together with the temperature (in Celsius degrees).

Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_125m_Attributes_ns_5647

--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_0_bs_16 num_bytes: 84088557.125 num_examples: 5647 - name: fewshot_1_bs_16 num_bytes: 85276022.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 87656291.125 num_examples: 5647 - name: fewshot_5_bs_16 num_bytes: 90034037.125 num_examples: 5647 - name: fewshot_8_bs_16 num_bytes: 93580093.125 num_examples: 5647 download_size: 415553691 dataset_size: 440635000.625 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_125m_Attributes_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_jingyeom__SOLAR_KO_1.3_deup

--- pretty_name: Evaluation run of jingyeom/SOLAR_KO_1.3_deup dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jingyeom/SOLAR_KO_1.3_deup](https://huggingface.co/jingyeom/SOLAR_KO_1.3_deup)\ \ 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_jingyeom__SOLAR_KO_1.3_deup\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-17T00:23:55.496430](https://huggingface.co/datasets/open-llm-leaderboard/details_jingyeom__SOLAR_KO_1.3_deup/blob/main/results_2024-01-17T00-23-55.496430.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\": 0.5568308436610663,\n\ \ \"acc_stderr\": 0.03382759863491837,\n \"acc_norm\": 0.562882955720715,\n\ \ \"acc_norm_stderr\": 0.03456146092708182,\n \"mc1\": 0.3182374541003672,\n\ \ \"mc1_stderr\": 0.016305988648920623,\n \"mc2\": 0.4754562707057089,\n\ \ \"mc2_stderr\": 0.01501819768286651\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5238907849829352,\n \"acc_stderr\": 0.014594701798071654,\n\ \ \"acc_norm\": 0.5597269624573379,\n \"acc_norm_stderr\": 0.014506769524804234\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5974905397331209,\n\ \ \"acc_stderr\": 0.004894012555642646,\n \"acc_norm\": 0.7997410874327823,\n\ \ \"acc_norm_stderr\": 0.003993761698847879\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4962962962962963,\n\ \ \"acc_stderr\": 0.04319223625811331,\n \"acc_norm\": 0.4962962962962963,\n\ \ \"acc_norm_stderr\": 0.04319223625811331\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5723684210526315,\n \"acc_stderr\": 0.04026097083296564,\n\ \ \"acc_norm\": 0.5723684210526315,\n \"acc_norm_stderr\": 0.04026097083296564\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5924528301886792,\n \"acc_stderr\": 0.030242233800854494,\n\ \ \"acc_norm\": 0.5924528301886792,\n \"acc_norm_stderr\": 0.030242233800854494\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.039420826399272135,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.039420826399272135\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n\ \ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5606936416184971,\n\ \ \"acc_stderr\": 0.03784271932887468,\n \"acc_norm\": 0.5606936416184971,\n\ \ \"acc_norm_stderr\": 0.03784271932887468\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n\ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.5319148936170213,\n \"acc_stderr\": 0.03261936918467382,\n\ \ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.03261936918467382\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n\ \ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n\ \ \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n\ \ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.36243386243386244,\n \"acc_stderr\": 0.02475747390275206,\n \"\ acc_norm\": 0.36243386243386244,\n \"acc_norm_stderr\": 0.02475747390275206\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\ \ \"acc_stderr\": 0.043902592653775614,\n \"acc_norm\": 0.40476190476190477,\n\ \ \"acc_norm_stderr\": 0.043902592653775614\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.632258064516129,\n \"acc_stderr\": 0.02743086657997347,\n \"acc_norm\"\ : 0.632258064516129,\n \"acc_norm_stderr\": 0.02743086657997347\n },\n\ \ \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.3793103448275862,\n\ \ \"acc_stderr\": 0.03413963805906235,\n \"acc_norm\": 0.3793103448275862,\n\ \ \"acc_norm_stderr\": 0.03413963805906235\n },\n \"harness|hendrycksTest-high_school_computer_science|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"\ acc\": 0.6727272727272727,\n \"acc_stderr\": 0.036639749943912434,\n \ \ \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.036639749943912434\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7070707070707071,\n \"acc_stderr\": 0.03242497958178816,\n \"\ acc_norm\": 0.7070707070707071,\n \"acc_norm_stderr\": 0.03242497958178816\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7772020725388601,\n \"acc_stderr\": 0.030031147977641538,\n\ \ \"acc_norm\": 0.7772020725388601,\n \"acc_norm_stderr\": 0.030031147977641538\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5051282051282051,\n \"acc_stderr\": 0.025349672906838653,\n\ \ \"acc_norm\": 0.5051282051282051,\n \"acc_norm_stderr\": 0.025349672906838653\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253252,\n \ \ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253252\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5672268907563025,\n \"acc_stderr\": 0.03218358107742613,\n \ \ \"acc_norm\": 0.5672268907563025,\n \"acc_norm_stderr\": 0.03218358107742613\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.037101857261199946,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.037101857261199946\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7504587155963303,\n \"acc_stderr\": 0.018553897629501624,\n \"\ acc_norm\": 0.7504587155963303,\n \"acc_norm_stderr\": 0.018553897629501624\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4537037037037037,\n \"acc_stderr\": 0.03395322726375797,\n \"\ acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.03395322726375797\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\": 0.6962025316455697,\n \"acc_stderr\": 0.02993669638713861,\n \ \ \"acc_norm\": 0.6962025316455697,\n \"acc_norm_stderr\": 0.02993669638713861\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5877862595419847,\n \"acc_stderr\": 0.04317171194870255,\n\ \ \"acc_norm\": 0.5877862595419847,\n \"acc_norm_stderr\": 0.04317171194870255\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6942148760330579,\n \"acc_stderr\": 0.04205953933884122,\n \"\ acc_norm\": 0.6942148760330579,\n \"acc_norm_stderr\": 0.04205953933884122\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.04668408033024931,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.04668408033024931\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6503067484662577,\n \"acc_stderr\": 0.03746668325470021,\n\ \ \"acc_norm\": 0.6503067484662577,\n \"acc_norm_stderr\": 0.03746668325470021\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8333333333333334,\n\ \ \"acc_stderr\": 0.024414947304543678,\n \"acc_norm\": 0.8333333333333334,\n\ \ \"acc_norm_stderr\": 0.024414947304543678\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7611749680715197,\n\ \ \"acc_stderr\": 0.015246803197398687,\n \"acc_norm\": 0.7611749680715197,\n\ \ \"acc_norm_stderr\": 0.015246803197398687\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5867052023121387,\n \"acc_stderr\": 0.026511261369409247,\n\ \ \"acc_norm\": 0.5867052023121387,\n \"acc_norm_stderr\": 0.026511261369409247\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2759776536312849,\n\ \ \"acc_stderr\": 0.014950103002475365,\n \"acc_norm\": 0.2759776536312849,\n\ \ \"acc_norm_stderr\": 0.014950103002475365\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6274509803921569,\n \"acc_stderr\": 0.027684181883302877,\n\ \ \"acc_norm\": 0.6274509803921569,\n \"acc_norm_stderr\": 0.027684181883302877\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6655948553054662,\n\ \ \"acc_stderr\": 0.02679542232789393,\n \"acc_norm\": 0.6655948553054662,\n\ \ \"acc_norm_stderr\": 0.02679542232789393\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.654320987654321,\n \"acc_stderr\": 0.02646248777700187,\n\ \ \"acc_norm\": 0.654320987654321,\n \"acc_norm_stderr\": 0.02646248777700187\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3829787234042553,\n \"acc_stderr\": 0.02899908090480618,\n \ \ \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.02899908090480618\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4211212516297262,\n\ \ \"acc_stderr\": 0.012610325733489905,\n \"acc_norm\": 0.4211212516297262,\n\ \ \"acc_norm_stderr\": 0.012610325733489905\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5330882352941176,\n \"acc_stderr\": 0.030306257722468317,\n\ \ \"acc_norm\": 0.5330882352941176,\n \"acc_norm_stderr\": 0.030306257722468317\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5441176470588235,\n \"acc_stderr\": 0.020148939420415745,\n \ \ \"acc_norm\": 0.5441176470588235,\n \"acc_norm_stderr\": 0.020148939420415745\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5909090909090909,\n\ \ \"acc_stderr\": 0.04709306978661896,\n \"acc_norm\": 0.5909090909090909,\n\ \ \"acc_norm_stderr\": 0.04709306978661896\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6530612244897959,\n \"acc_stderr\": 0.030472526026726492,\n\ \ \"acc_norm\": 0.6530612244897959,\n \"acc_norm_stderr\": 0.030472526026726492\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7313432835820896,\n\ \ \"acc_stderr\": 0.03134328358208955,\n \"acc_norm\": 0.7313432835820896,\n\ \ \"acc_norm_stderr\": 0.03134328358208955\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.463855421686747,\n\ \ \"acc_stderr\": 0.03882310850890593,\n \"acc_norm\": 0.463855421686747,\n\ \ \"acc_norm_stderr\": 0.03882310850890593\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7426900584795322,\n \"acc_stderr\": 0.03352799844161865,\n\ \ \"acc_norm\": 0.7426900584795322,\n \"acc_norm_stderr\": 0.03352799844161865\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3182374541003672,\n\ \ \"mc1_stderr\": 0.016305988648920623,\n \"mc2\": 0.4754562707057089,\n\ \ \"mc2_stderr\": 0.01501819768286651\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.01185004012485051\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2259287338893101,\n \ \ \"acc_stderr\": 0.01151909877727995\n }\n}\n```" repo_url: https://huggingface.co/jingyeom/SOLAR_KO_1.3_deup 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_01_17T00_23_55.496430 path: - '**/details_harness|arc:challenge|25_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-17T00-23-55.496430.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|gsm8k|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hellaswag|10_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-17T00-23-55.496430.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-management|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-17T00-23-55.496430.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|truthfulqa:mc|0_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-17T00-23-55.496430.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_17T00_23_55.496430 path: - '**/details_harness|winogrande|5_2024-01-17T00-23-55.496430.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-17T00-23-55.496430.parquet' - config_name: results data_files: - split: 2024_01_17T00_23_55.496430 path: - results_2024-01-17T00-23-55.496430.parquet - split: latest path: - results_2024-01-17T00-23-55.496430.parquet --- # Dataset Card for Evaluation run of jingyeom/SOLAR_KO_1.3_deup  Dataset automatically created during the evaluation run of model [jingyeom/SOLAR_KO_1.3_deup](https://huggingface.co/jingyeom/SOLAR_KO_1.3_deup) 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_jingyeom__SOLAR_KO_1.3_deup", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-17T00:23:55.496430](https://huggingface.co/datasets/open-llm-leaderboard/details_jingyeom__SOLAR_KO_1.3_deup/blob/main/results_2024-01-17T00-23-55.496430.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": 0.5568308436610663, "acc_stderr": 0.03382759863491837, "acc_norm": 0.562882955720715, "acc_norm_stderr": 0.03456146092708182, "mc1": 0.3182374541003672, "mc1_stderr": 0.016305988648920623, "mc2": 0.4754562707057089, "mc2_stderr": 0.01501819768286651 }, "harness|arc:challenge|25": { "acc": 0.5238907849829352, "acc_stderr": 0.014594701798071654, "acc_norm": 0.5597269624573379, "acc_norm_stderr": 0.014506769524804234 }, "harness|hellaswag|10": { "acc": 0.5974905397331209, "acc_stderr": 0.004894012555642646, "acc_norm": 0.7997410874327823, "acc_norm_stderr": 0.003993761698847879 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4962962962962963, "acc_stderr": 0.04319223625811331, "acc_norm": 0.4962962962962963, "acc_norm_stderr": 0.04319223625811331 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5723684210526315, "acc_stderr": 0.04026097083296564, "acc_norm": 0.5723684210526315, "acc_norm_stderr": 0.04026097083296564 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5924528301886792, "acc_stderr": 0.030242233800854494, "acc_norm": 0.5924528301886792, "acc_norm_stderr": 0.030242233800854494 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6666666666666666, "acc_stderr": 0.039420826399272135, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.039420826399272135 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5606936416184971, "acc_stderr": 0.03784271932887468, "acc_norm": 0.5606936416184971, "acc_norm_stderr": 0.03784271932887468 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2647058823529412, "acc_stderr": 0.04389869956808778, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.04389869956808778 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.68, "acc_stderr": 0.04688261722621504, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5319148936170213, "acc_stderr": 0.03261936918467382, "acc_norm": 0.5319148936170213, "acc_norm_stderr": 0.03261936918467382 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.37719298245614036, "acc_stderr": 0.04559522141958216, "acc_norm": 0.37719298245614036, "acc_norm_stderr": 0.04559522141958216 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5241379310344828, "acc_stderr": 0.0416180850350153, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.0416180850350153 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.36243386243386244, "acc_stderr": 0.02475747390275206, "acc_norm": 0.36243386243386244, "acc_norm_stderr": 0.02475747390275206 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.40476190476190477, "acc_stderr": 0.043902592653775614, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.043902592653775614 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.632258064516129, "acc_stderr": 0.02743086657997347, "acc_norm": 0.632258064516129, "acc_norm_stderr": 0.02743086657997347 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3793103448275862, "acc_stderr": 0.03413963805906235, "acc_norm": 0.3793103448275862, "acc_norm_stderr": 0.03413963805906235 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6727272727272727, "acc_stderr": 0.036639749943912434, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.036639749943912434 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7070707070707071, "acc_stderr": 0.03242497958178816, "acc_norm": 0.7070707070707071, "acc_norm_stderr": 0.03242497958178816 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7772020725388601, "acc_stderr": 0.030031147977641538, "acc_norm": 0.7772020725388601, "acc_norm_stderr": 0.030031147977641538 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5051282051282051, "acc_stderr": 0.025349672906838653, "acc_norm": 0.5051282051282051, "acc_norm_stderr": 0.025349672906838653 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.337037037037037, "acc_stderr": 0.028820884666253252, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.028820884666253252 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5672268907563025, "acc_stderr": 0.03218358107742613, "acc_norm": 0.5672268907563025, "acc_norm_stderr": 0.03218358107742613 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2913907284768212, "acc_stderr": 0.037101857261199946, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.037101857261199946 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7504587155963303, "acc_stderr": 0.018553897629501624, "acc_norm": 0.7504587155963303, "acc_norm_stderr": 0.018553897629501624 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4537037037037037, "acc_stderr": 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0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7611749680715197, "acc_stderr": 0.015246803197398687, "acc_norm": 0.7611749680715197, "acc_norm_stderr": 0.015246803197398687 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5867052023121387, "acc_stderr": 0.026511261369409247, "acc_norm": 0.5867052023121387, "acc_norm_stderr": 0.026511261369409247 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2759776536312849, "acc_stderr": 0.014950103002475365, "acc_norm": 0.2759776536312849, "acc_norm_stderr": 0.014950103002475365 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6274509803921569, "acc_stderr": 0.027684181883302877, "acc_norm": 0.6274509803921569, "acc_norm_stderr": 0.027684181883302877 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6655948553054662, "acc_stderr": 0.02679542232789393, "acc_norm": 0.6655948553054662, "acc_norm_stderr": 0.02679542232789393 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.654320987654321, "acc_stderr": 0.02646248777700187, "acc_norm": 0.654320987654321, "acc_norm_stderr": 0.02646248777700187 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3829787234042553, "acc_stderr": 0.02899908090480618, "acc_norm": 0.3829787234042553, "acc_norm_stderr": 0.02899908090480618 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4211212516297262, "acc_stderr": 0.012610325733489905, "acc_norm": 0.4211212516297262, "acc_norm_stderr": 0.012610325733489905 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5330882352941176, "acc_stderr": 0.030306257722468317, "acc_norm": 0.5330882352941176, "acc_norm_stderr": 0.030306257722468317 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5441176470588235, "acc_stderr": 0.020148939420415745, "acc_norm": 0.5441176470588235, "acc_norm_stderr": 0.020148939420415745 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5909090909090909, "acc_stderr": 0.04709306978661896, "acc_norm": 0.5909090909090909, "acc_norm_stderr": 0.04709306978661896 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6530612244897959, "acc_stderr": 0.030472526026726492, "acc_norm": 0.6530612244897959, "acc_norm_stderr": 0.030472526026726492 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7313432835820896, "acc_stderr": 0.03134328358208955, "acc_norm": 0.7313432835820896, "acc_norm_stderr": 0.03134328358208955 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.8, "acc_stderr": 0.04020151261036846, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036846 }, "harness|hendrycksTest-virology|5": { "acc": 0.463855421686747, "acc_stderr": 0.03882310850890593, "acc_norm": 0.463855421686747, "acc_norm_stderr": 0.03882310850890593 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7426900584795322, "acc_stderr": 0.03352799844161865, "acc_norm": 0.7426900584795322, "acc_norm_stderr": 0.03352799844161865 }, "harness|truthfulqa:mc|0": { "mc1": 0.3182374541003672, "mc1_stderr": 0.016305988648920623, "mc2": 0.4754562707057089, "mc2_stderr": 0.01501819768286651 }, "harness|winogrande|5": { "acc": 0.7687450670876085, "acc_stderr": 0.01185004012485051 }, "harness|gsm8k|5": { "acc": 0.2259287338893101, "acc_stderr": 0.01151909877727995 } } ``` ## 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]

DJBanzin/Vozesvukvuk

--- license: openrail ---

dar-tau/lm-extraction-benchmark

--- dataset_info: features: - name: preprefix sequence: uint16 - name: prefix sequence: uint16 - name: suffix sequence: uint16 splits: - name: train num_bytes: 6180000 num_examples: 15000 download_size: 5792506 dataset_size: 6180000 configs: - config_name: default data_files: - split: train path: data/train-* ---

erikliu18/us-congress-hearing

--- task_categories: - text-classification language: - en tags: - finance - legal --- # U.S. Congressional Hearings Dataset This dataset currently contains cleaned sentences from all House Committee on Energy and Commerce hearings from 2002. A total of 1K+ hearing transcripts in txt formats from govinfo.gov were collected and cleaned.

feliipert/Reportes-radiologicos

--- license: apache-2.0 task_categories: - text-classification language: - es tags: - medical pretty_name: Radiologist size_categories: - n<1K ---

mnazari/nena_speech_1_0_test

--- pretty_name: NENA Speech Dataset 1.0 (test) annotations_creators: - crowdsourced - Geoffrey Khan language_creators: - crowdsourced language: - aii - cld - huy - lsd - trg - aij - bhn - hrt - kqd - syn license: - cc0-1.0 multilinguality: - multilingual task_categories: - automatic-speech-recognition - text-to-speech - translation size_categories: - 10K<n<100K - 1K<n<10K - n<1K --- # Dataset Card for NENA Speech Dataset 1.0 (test) ## Table of Contents - [Dataset Summary](#dataset-summary) - [Dataset Description](#dataset-description) - [Languages](#languages) - [How to Use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation)  - [Building the Dataset](#building-the-dataset) - [Personal and Sensitive Information](#personal-and-sensitive-information)  - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## ⚠️ This is a temperary repository that will be replaced by end of 2023 ## Dataset Summary NENA Speech is a multimodal dataset to help teach machines how real people speak the Northeastern Neo-Aramaic (NENA) dialects. The NENA dialects form a very diverse group of Aramaic dialects spoken by Christian and Jewish communities indigenous to northwestern Iran, northern Iraq, and southeastern Türkiye. NENA Speech consists of multimodal examples of speech of the NENA dialects. While all documented NENA dialects are included, not all have data yet, and some will never due to recent loss of their final speakers. ## Dataset Description - **Homepage**: https://crowdsource.nenadb.dev/ - **Point of Contact:** [Matthew Nazari](mailto:matthewnazari@college.harvard.edu) ## Languages The NENA dialects form a very diverse group of Aramaic dialects spoken by Christian and Jewish communities indigenous to northwestern Iran, northern Iraq, and southeastern Türkiye. Speakers of the Christian dialects call their language Assyrian and Chaldean in English. In their language these speakers use multiple different terms (e.g. suráy, sureth, ḥadiṯan, senaya). Speakers of the Jewish dialects call their language lišana deni, lišanət noshan, lišana nosha, lišana didan, all meaning "our language". Some names reflect the consciousness of it being a specifically Jewish language (e.g. lišan hozaye, hulaula). NENA Speech has a subset for all of the over 150 NENA dialects. Not all dialects have examples available yet. Some dialects will never have examples available due to the loss of their final speakers in recent years. ## How to Use The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, simply specify the corresponding language config name (e.g., "urmi (christian)" for the dialect of the Assyrian Christians of Urmi): ```python from datasets import load_dataset nena_speech = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train") ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). ## Dataset Structure ### Data Instances The NENA Speech dataset is a multimodal dataset that consists of three different kinds of examples: 1. **Unlabeled speech examples:** these contain audio of speech (`audio`) but no accompanying transcription (`transcription`) or translation (`translation`). This is useful for representation learning. 2. **Transcribed speech examples:** these contain both audio and transcription of speech. These are useful for machine learning tasks like automatic speech recognition and speech synthesis. 3. **Transcribed and translated speech examples:** these kinds of examples contain audio, transcription, and translation of speech. These are useful for tasks like multimodal translation. Make sure to filter for the kinds of examples you need for your task before before using it. ```json { "transcription": "gu-mdìta.ˈ", "translation": "in the town.", "audio": { "path": "et/train/nena_speech_0uk14ofpom196aj.mp3", "array": array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32), "sampling_rate": 48000 }, "locale": "IRN", "proficiency": "proficient as mom", "age": "70's", "crowdsourced": true, "unlabeled": true, "interrupted": true, "client_id": "gwurt1g1ln" , "path": "et/train/nena_speech_0uk14ofpom196aj.mp3", } ``` ### Data Fields - `transcription (string)`: The transcription of what was spoken (e.g. `"beta"`) - `translation (string)`: The translation of what was spoken in English (e.g. `"house"`) - `audio (dict)`: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`. - `locale (string)`: The locale of the speaker - `proficiency (string)`: The proficiency of the speaker - `age (string)`: The age of the speaker (e.g. `"20's"`, `"50's"`, `"100+"`) - `crowdsourced (bool)`: Indicates whether the example was crowdsourced as opposed to collected from existing language documentation resources - `interrupted (bool)`: Indicates whether the example was interrupted with the speaker making sound effects or switching into another language - `client_id (string)`: An id for which client (voice) made the recording - `path (string)`: The path to the audio file ### Data Splits The examples have been subdivided into three portions: 1. **dev:** the validation split (10%) 3. **test:** the test split (10%) 2. **train:** the train split (80%) The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train. ## Dataset Creation  ### Building the Dataset The NENA Speech dataset itself is built using `build.py`. First, install the necessary requirements. ``` pip install -r requirements.txt ``` Next, build the dataset. ``` python build.py --build ``` Finally, push to the HuggingFace dataset repository. ## Personal and Sensitive Information The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset. ## Data Preprocessing The dataset consists of three different kinds of examples (see [Data Instances](#data-instances)). Make sure to filter for the kinds of examples you need for your task before before using it. For example, for automatic speech recognition you will want to filter for examples with transcriptions. In most tasks, you will want to filter out examples that are interrupted (e.g. by the speaker making sound effects, by the speaker switching into a another language). ```python from datasets import load_dataset ds = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train") def filter_for_asr(example): return example['transcription'] and not example['interrupted'] ds = ds.filter(filter_for_asr, desc="filter dataset") ``` Transcriptions include markers of linguistic and acoustic features which may be removed in certain tasks (e.g. word stress, nuclear stress, intonation group markers, vowel length). ```python from datasets import load_dataset ds = load_dataset("mnazari/nena_speech_1_0_test", "urmi (christian)", split="train") def prepare_dataset(batch): chars_to_remove = ['ˈ', '̀', '́', '̄', '̆', '.', ',', '?', '!'] for char in chars_to_remove: batch["transcription"] = batch["transcription"].replace(char, "") return batch ds = ds.map(prepare_dataset, desc="preprocess dataset") ```  ## Additional Information ### Licensing Information Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/). ### Citation Information This work has not yet been published.

andersonbcdefg/doc_nli_pos_pairs

--- dataset_info: features: - name: query dtype: string - name: pos dtype: string splits: - name: train num_bytes: 888275454 num_examples: 528671 download_size: 467347853 dataset_size: 888275454 configs: - config_name: default data_files: - split: train path: data/train-* ---

happycute123/DL_dataset

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 679382539.464 num_examples: 1057 - name: test num_bytes: 167054773.0 num_examples: 264 download_size: 0 dataset_size: 846437312.464 --- # Dataset Card for "DL_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

xorsuyash/raft_datasetp1

--- license: mit ---

FDeRubeis/araft

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: label dtype: string - name: prediction dtype: string - name: trajectory dtype: string splits: - name: train num_bytes: 961824 num_examples: 413 download_size: 499573 dataset_size: 961824 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_uukuguy__GDC-Tiny-L1-1.8B

--- pretty_name: Evaluation run of uukuguy/GDC-Tiny-L1-1.8B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [uukuguy/GDC-Tiny-L1-1.8B](https://huggingface.co/uukuguy/GDC-Tiny-L1-1.8B) 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 4 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_uukuguy__GDC-Tiny-L1-1.8B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-07T19:08:55.303427](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__GDC-Tiny-L1-1.8B/blob/main/results_2024-04-07T19-08-55.303427.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\": 0.43818164678489546,\n\ \ \"acc_stderr\": 0.03446376592395749,\n \"acc_norm\": 0.44061901287311,\n\ \ \"acc_norm_stderr\": 0.03519031730786029,\n \"mc1\": 0.2594859241126071,\n\ \ \"mc1_stderr\": 0.015345409485557989,\n \"mc2\": 0.40282806404013544,\n\ \ \"mc2_stderr\": 0.014486104033756\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.3438566552901024,\n \"acc_stderr\": 0.01388064457015621,\n\ \ \"acc_norm\": 0.3651877133105802,\n \"acc_norm_stderr\": 0.014070265519268804\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4400517825134435,\n\ \ \"acc_stderr\": 0.004953787146510927,\n \"acc_norm\": 0.5866361282613025,\n\ \ \"acc_norm_stderr\": 0.004914305798575695\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720685,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720685\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.362962962962963,\n\ \ \"acc_stderr\": 0.04153948404742399,\n \"acc_norm\": 0.362962962962963,\n\ \ \"acc_norm_stderr\": 0.04153948404742399\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.46710526315789475,\n \"acc_stderr\": 0.040601270352363966,\n\ \ \"acc_norm\": 0.46710526315789475,\n \"acc_norm_stderr\": 0.040601270352363966\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.52,\n\ \ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.4528301886792453,\n \"acc_stderr\": 0.030635627957961816,\n\ \ \"acc_norm\": 0.4528301886792453,\n \"acc_norm_stderr\": 0.030635627957961816\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4305555555555556,\n\ \ \"acc_stderr\": 0.04140685639111503,\n \"acc_norm\": 0.4305555555555556,\n\ \ \"acc_norm_stderr\": 0.04140685639111503\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\"\ : 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3930635838150289,\n\ \ \"acc_stderr\": 0.0372424959581773,\n \"acc_norm\": 0.3930635838150289,\n\ \ \"acc_norm_stderr\": 0.0372424959581773\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179964,\n\ \ \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179964\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n\ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4297872340425532,\n \"acc_stderr\": 0.03236214467715564,\n\ \ \"acc_norm\": 0.4297872340425532,\n \"acc_norm_stderr\": 0.03236214467715564\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2982456140350877,\n\ \ \"acc_stderr\": 0.04303684033537314,\n \"acc_norm\": 0.2982456140350877,\n\ \ \"acc_norm_stderr\": 0.04303684033537314\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.4206896551724138,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.4206896551724138,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3544973544973545,\n \"acc_stderr\": 0.024636830602842,\n \"acc_norm\"\ : 0.3544973544973545,\n \"acc_norm_stderr\": 0.024636830602842\n },\n\ \ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2619047619047619,\n\ \ \"acc_stderr\": 0.0393253768039287,\n \"acc_norm\": 0.2619047619047619,\n\ \ \"acc_norm_stderr\": 0.0393253768039287\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.49032258064516127,\n \"acc_stderr\": 0.028438677998909558,\n \"\ acc_norm\": 0.49032258064516127,\n \"acc_norm_stderr\": 0.028438677998909558\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.3103448275862069,\n \"acc_stderr\": 0.03255086769970103,\n \"\ acc_norm\": 0.3103448275862069,\n \"acc_norm_stderr\": 0.03255086769970103\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.44,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\"\ : 0.44,\n \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.5818181818181818,\n \"acc_stderr\": 0.03851716319398395,\n\ \ \"acc_norm\": 0.5818181818181818,\n \"acc_norm_stderr\": 0.03851716319398395\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.5959595959595959,\n \"acc_stderr\": 0.03496130972056127,\n \"\ acc_norm\": 0.5959595959595959,\n \"acc_norm_stderr\": 0.03496130972056127\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.5440414507772021,\n \"acc_stderr\": 0.03594413711272437,\n\ \ \"acc_norm\": 0.5440414507772021,\n \"acc_norm_stderr\": 0.03594413711272437\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.37948717948717947,\n \"acc_stderr\": 0.024603626924097417,\n\ \ \"acc_norm\": 0.37948717948717947,\n \"acc_norm_stderr\": 0.024603626924097417\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3,\n \"acc_stderr\": 0.027940457136228412,\n \"acc_norm\"\ : 0.3,\n \"acc_norm_stderr\": 0.027940457136228412\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.0322529423239964,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.0322529423239964\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.25165562913907286,\n \"acc_stderr\": 0.035433042343899844,\n \"\ acc_norm\": 0.25165562913907286,\n \"acc_norm_stderr\": 0.035433042343899844\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.5596330275229358,\n \"acc_stderr\": 0.02128431062376155,\n \"\ acc_norm\": 0.5596330275229358,\n \"acc_norm_stderr\": 0.02128431062376155\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.25462962962962965,\n \"acc_stderr\": 0.029711275860005354,\n \"\ acc_norm\": 0.25462962962962965,\n \"acc_norm_stderr\": 0.029711275860005354\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.4852941176470588,\n \"acc_stderr\": 0.035077938347913236,\n \"\ acc_norm\": 0.4852941176470588,\n \"acc_norm_stderr\": 0.035077938347913236\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.5527426160337553,\n \"acc_stderr\": 0.03236564251614192,\n \ \ \"acc_norm\": 0.5527426160337553,\n \"acc_norm_stderr\": 0.03236564251614192\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5291479820627802,\n\ \ \"acc_stderr\": 0.03350073248773404,\n \"acc_norm\": 0.5291479820627802,\n\ \ \"acc_norm_stderr\": 0.03350073248773404\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.4961832061068702,\n \"acc_stderr\": 0.04385162325601553,\n\ \ \"acc_norm\": 0.4961832061068702,\n \"acc_norm_stderr\": 0.04385162325601553\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6198347107438017,\n \"acc_stderr\": 0.04431324501968431,\n \"\ acc_norm\": 0.6198347107438017,\n \"acc_norm_stderr\": 0.04431324501968431\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.48148148148148145,\n\ \ \"acc_stderr\": 0.04830366024635331,\n \"acc_norm\": 0.48148148148148145,\n\ \ \"acc_norm_stderr\": 0.04830366024635331\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.32515337423312884,\n \"acc_stderr\": 0.036803503712864595,\n\ \ \"acc_norm\": 0.32515337423312884,\n \"acc_norm_stderr\": 0.036803503712864595\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n\ \ \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.4107142857142857,\n\ \ \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.5825242718446602,\n \"acc_stderr\": 0.048828405482122375,\n\ \ \"acc_norm\": 0.5825242718446602,\n \"acc_norm_stderr\": 0.048828405482122375\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7606837606837606,\n\ \ \"acc_stderr\": 0.027951826808924336,\n \"acc_norm\": 0.7606837606837606,\n\ \ \"acc_norm_stderr\": 0.027951826808924336\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5644955300127714,\n\ \ \"acc_stderr\": 0.017730589927926595,\n \"acc_norm\": 0.5644955300127714,\n\ \ \"acc_norm_stderr\": 0.017730589927926595\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.49710982658959535,\n \"acc_stderr\": 0.02691864538323901,\n\ \ \"acc_norm\": 0.49710982658959535,\n \"acc_norm_stderr\": 0.02691864538323901\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23128491620111732,\n\ \ \"acc_stderr\": 0.014102223623152586,\n \"acc_norm\": 0.23128491620111732,\n\ \ \"acc_norm_stderr\": 0.014102223623152586\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.028607893699576063,\n\ \ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.028607893699576063\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.4340836012861736,\n\ \ \"acc_stderr\": 0.0281502322445356,\n \"acc_norm\": 0.4340836012861736,\n\ \ \"acc_norm_stderr\": 0.0281502322445356\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.45987654320987653,\n \"acc_stderr\": 0.027731022753539274,\n\ \ \"acc_norm\": 0.45987654320987653,\n \"acc_norm_stderr\": 0.027731022753539274\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.34397163120567376,\n \"acc_stderr\": 0.028338017428611317,\n \ \ \"acc_norm\": 0.34397163120567376,\n \"acc_norm_stderr\": 0.028338017428611317\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.34810951760104303,\n\ \ \"acc_stderr\": 0.012166738993698195,\n \"acc_norm\": 0.34810951760104303,\n\ \ \"acc_norm_stderr\": 0.012166738993698195\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.34191176470588236,\n \"acc_stderr\": 0.028814722422254187,\n\ \ \"acc_norm\": 0.34191176470588236,\n \"acc_norm_stderr\": 0.028814722422254187\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.4199346405228758,\n \"acc_stderr\": 0.019966811178256483,\n \ \ \"acc_norm\": 0.4199346405228758,\n \"acc_norm_stderr\": 0.019966811178256483\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5272727272727272,\n\ \ \"acc_stderr\": 0.04782001791380061,\n \"acc_norm\": 0.5272727272727272,\n\ \ \"acc_norm_stderr\": 0.04782001791380061\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.45714285714285713,\n \"acc_stderr\": 0.03189141832421397,\n\ \ \"acc_norm\": 0.45714285714285713,\n \"acc_norm_stderr\": 0.03189141832421397\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5373134328358209,\n\ \ \"acc_stderr\": 0.03525675167467974,\n \"acc_norm\": 0.5373134328358209,\n\ \ \"acc_norm_stderr\": 0.03525675167467974\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695238,\n \ \ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695238\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.39156626506024095,\n\ \ \"acc_stderr\": 0.03799857454479636,\n \"acc_norm\": 0.39156626506024095,\n\ \ \"acc_norm_stderr\": 0.03799857454479636\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.52046783625731,\n \"acc_stderr\": 0.0383161053282193,\n\ \ \"acc_norm\": 0.52046783625731,\n \"acc_norm_stderr\": 0.0383161053282193\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2594859241126071,\n\ \ \"mc1_stderr\": 0.015345409485557989,\n \"mc2\": 0.40282806404013544,\n\ \ \"mc2_stderr\": 0.014486104033756\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6101026045777427,\n \"acc_stderr\": 0.013707547317008465\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.29037149355572406,\n \ \ \"acc_stderr\": 0.01250359248181895\n }\n}\n```" repo_url: https://huggingface.co/uukuguy/GDC-Tiny-L1-1.8B 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_06T00_21_15.731330 path: - '**/details_harness|arc:challenge|25_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|arc:challenge|25_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|arc:challenge|25_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|arc:challenge|25_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-07T19-08-55.303427.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|gsm8k|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|gsm8k|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|gsm8k|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|gsm8k|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hellaswag|10_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hellaswag|10_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hellaswag|10_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hellaswag|10_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-06T00-21-15.731330.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-06T15-47-54.200639.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-07T05-02-03.235353.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-07T19-08-55.303427.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-management|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-management|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-management|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-management|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T19-08-55.303427.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|truthfulqa:mc|0_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|truthfulqa:mc|0_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|truthfulqa:mc|0_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|truthfulqa:mc|0_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-07T19-08-55.303427.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_06T00_21_15.731330 path: - '**/details_harness|winogrande|5_2024-04-06T00-21-15.731330.parquet' - split: 2024_04_06T15_47_54.200639 path: - '**/details_harness|winogrande|5_2024-04-06T15-47-54.200639.parquet' - split: 2024_04_07T05_02_03.235353 path: - '**/details_harness|winogrande|5_2024-04-07T05-02-03.235353.parquet' - split: 2024_04_07T19_08_55.303427 path: - '**/details_harness|winogrande|5_2024-04-07T19-08-55.303427.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-07T19-08-55.303427.parquet' - config_name: results data_files: - split: 2024_04_06T00_21_15.731330 path: - results_2024-04-06T00-21-15.731330.parquet - split: 2024_04_06T15_47_54.200639 path: - results_2024-04-06T15-47-54.200639.parquet - split: 2024_04_07T05_02_03.235353 path: - results_2024-04-07T05-02-03.235353.parquet - split: 2024_04_07T19_08_55.303427 path: - results_2024-04-07T19-08-55.303427.parquet - split: latest path: - results_2024-04-07T19-08-55.303427.parquet --- # Dataset Card for Evaluation run of uukuguy/GDC-Tiny-L1-1.8B  Dataset automatically created during the evaluation run of model [uukuguy/GDC-Tiny-L1-1.8B](https://huggingface.co/uukuguy/GDC-Tiny-L1-1.8B) 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 4 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_uukuguy__GDC-Tiny-L1-1.8B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-07T19:08:55.303427](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__GDC-Tiny-L1-1.8B/blob/main/results_2024-04-07T19-08-55.303427.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": 0.43818164678489546, "acc_stderr": 0.03446376592395749, "acc_norm": 0.44061901287311, "acc_norm_stderr": 0.03519031730786029, "mc1": 0.2594859241126071, "mc1_stderr": 0.015345409485557989, "mc2": 0.40282806404013544, "mc2_stderr": 0.014486104033756 }, "harness|arc:challenge|25": { "acc": 0.3438566552901024, "acc_stderr": 0.01388064457015621, "acc_norm": 0.3651877133105802, "acc_norm_stderr": 0.014070265519268804 }, "harness|hellaswag|10": { "acc": 0.4400517825134435, "acc_stderr": 0.004953787146510927, "acc_norm": 0.5866361282613025, "acc_norm_stderr": 0.004914305798575695 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.29, "acc_stderr": 0.04560480215720685, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720685 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.362962962962963, "acc_stderr": 0.04153948404742399, "acc_norm": 0.362962962962963, "acc_norm_stderr": 0.04153948404742399 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.46710526315789475, "acc_stderr": 0.040601270352363966, "acc_norm": 0.46710526315789475, "acc_norm_stderr": 0.040601270352363966 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4528301886792453, "acc_stderr": 0.030635627957961816, "acc_norm": 0.4528301886792453, "acc_norm_stderr": 0.030635627957961816 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4305555555555556, "acc_stderr": 0.04140685639111503, "acc_norm": 0.4305555555555556, "acc_norm_stderr": 0.04140685639111503 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.3930635838150289, "acc_stderr": 0.0372424959581773, "acc_norm": 0.3930635838150289, "acc_norm_stderr": 0.0372424959581773 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.19607843137254902, "acc_stderr": 0.03950581861179964, "acc_norm": 0.19607843137254902, "acc_norm_stderr": 0.03950581861179964 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4297872340425532, "acc_stderr": 0.03236214467715564, "acc_norm": 0.4297872340425532, "acc_norm_stderr": 0.03236214467715564 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2982456140350877, "acc_stderr": 0.04303684033537314, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.04303684033537314 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4206896551724138, "acc_stderr": 0.0411391498118926, "acc_norm": 0.4206896551724138, "acc_norm_stderr": 0.0411391498118926 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3544973544973545, "acc_stderr": 0.024636830602842, "acc_norm": 0.3544973544973545, "acc_norm_stderr": 0.024636830602842 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2619047619047619, "acc_stderr": 0.0393253768039287, "acc_norm": 0.2619047619047619, "acc_norm_stderr": 0.0393253768039287 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.49032258064516127, "acc_stderr": 0.028438677998909558, "acc_norm": 0.49032258064516127, "acc_norm_stderr": 0.028438677998909558 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3103448275862069, "acc_stderr": 0.03255086769970103, "acc_norm": 0.3103448275862069, "acc_norm_stderr": 0.03255086769970103 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.44, "acc_stderr": 0.049888765156985884, "acc_norm": 0.44, "acc_norm_stderr": 0.049888765156985884 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5818181818181818, "acc_stderr": 0.03851716319398395, "acc_norm": 0.5818181818181818, "acc_norm_stderr": 0.03851716319398395 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5959595959595959, "acc_stderr": 0.03496130972056127, "acc_norm": 0.5959595959595959, "acc_norm_stderr": 0.03496130972056127 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.5440414507772021, "acc_stderr": 0.03594413711272437, "acc_norm": 0.5440414507772021, "acc_norm_stderr": 0.03594413711272437 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.37948717948717947, "acc_stderr": 0.024603626924097417, "acc_norm": 0.37948717948717947, "acc_norm_stderr": 0.024603626924097417 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3, "acc_stderr": 0.027940457136228412, "acc_norm": 0.3, "acc_norm_stderr": 0.027940457136228412 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.0322529423239964, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.0322529423239964 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.25165562913907286, "acc_stderr": 0.035433042343899844, "acc_norm": 0.25165562913907286, "acc_norm_stderr": 0.035433042343899844 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.5596330275229358, "acc_stderr": 0.02128431062376155, "acc_norm": 0.5596330275229358, "acc_norm_stderr": 0.02128431062376155 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.25462962962962965, "acc_stderr": 0.029711275860005354, "acc_norm": 0.25462962962962965, "acc_norm_stderr": 0.029711275860005354 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.4852941176470588, "acc_stderr": 0.035077938347913236, "acc_norm": 0.4852941176470588, "acc_norm_stderr": 0.035077938347913236 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.5527426160337553, "acc_stderr": 0.03236564251614192, "acc_norm": 0.5527426160337553, "acc_norm_stderr": 0.03236564251614192 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.5291479820627802, "acc_stderr": 0.03350073248773404, "acc_norm": 0.5291479820627802, "acc_norm_stderr": 0.03350073248773404 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.4961832061068702, "acc_stderr": 0.04385162325601553, "acc_norm": 0.4961832061068702, "acc_norm_stderr": 0.04385162325601553 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6198347107438017, "acc_stderr": 0.04431324501968431, "acc_norm": 0.6198347107438017, "acc_norm_stderr": 0.04431324501968431 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.48148148148148145, "acc_stderr": 0.04830366024635331, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.04830366024635331 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.32515337423312884, "acc_stderr": 0.036803503712864595, "acc_norm": 0.32515337423312884, "acc_norm_stderr": 0.036803503712864595 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4107142857142857, "acc_stderr": 0.04669510663875191, "acc_norm": 0.4107142857142857, "acc_norm_stderr": 0.04669510663875191 }, "harness|hendrycksTest-management|5": { "acc": 0.5825242718446602, "acc_stderr": 0.048828405482122375, "acc_norm": 0.5825242718446602, "acc_norm_stderr": 0.048828405482122375 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7606837606837606, "acc_stderr": 0.027951826808924336, "acc_norm": 0.7606837606837606, "acc_norm_stderr": 0.027951826808924336 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.5644955300127714, "acc_stderr": 0.017730589927926595, "acc_norm": 0.5644955300127714, "acc_norm_stderr": 0.017730589927926595 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.49710982658959535, "acc_stderr": 0.02691864538323901, "acc_norm": 0.49710982658959535, "acc_norm_stderr": 0.02691864538323901 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23128491620111732, "acc_stderr": 0.014102223623152586, "acc_norm": 0.23128491620111732, "acc_norm_stderr": 0.014102223623152586 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5196078431372549, "acc_stderr": 0.028607893699576063, "acc_norm": 0.5196078431372549, "acc_norm_stderr": 0.028607893699576063 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.4340836012861736, "acc_stderr": 0.0281502322445356, "acc_norm": 0.4340836012861736, "acc_norm_stderr": 0.0281502322445356 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.45987654320987653, "acc_stderr": 0.027731022753539274, "acc_norm": 0.45987654320987653, "acc_norm_stderr": 0.027731022753539274 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.34397163120567376, "acc_stderr": 0.028338017428611317, "acc_norm": 0.34397163120567376, "acc_norm_stderr": 0.028338017428611317 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.34810951760104303, "acc_stderr": 0.012166738993698195, "acc_norm": 0.34810951760104303, "acc_norm_stderr": 0.012166738993698195 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.34191176470588236, "acc_stderr": 0.028814722422254187, "acc_norm": 0.34191176470588236, "acc_norm_stderr": 0.028814722422254187 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.4199346405228758, "acc_stderr": 0.019966811178256483, "acc_norm": 0.4199346405228758, "acc_norm_stderr": 0.019966811178256483 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5272727272727272, "acc_stderr": 0.04782001791380061, "acc_norm": 0.5272727272727272, "acc_norm_stderr": 0.04782001791380061 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.45714285714285713, "acc_stderr": 0.03189141832421397, "acc_norm": 0.45714285714285713, "acc_norm_stderr": 0.03189141832421397 }, "harness|hendrycksTest-sociology|5": { "acc": 0.5373134328358209, "acc_stderr": 0.03525675167467974, "acc_norm": 0.5373134328358209, "acc_norm_stderr": 0.03525675167467974 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.66, "acc_stderr": 0.04760952285695238, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695238 }, "harness|hendrycksTest-virology|5": { "acc": 0.39156626506024095, "acc_stderr": 0.03799857454479636, "acc_norm": 0.39156626506024095, "acc_norm_stderr": 0.03799857454479636 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.52046783625731, "acc_stderr": 0.0383161053282193, "acc_norm": 0.52046783625731, "acc_norm_stderr": 0.0383161053282193 }, "harness|truthfulqa:mc|0": { "mc1": 0.2594859241126071, "mc1_stderr": 0.015345409485557989, "mc2": 0.40282806404013544, "mc2_stderr": 0.014486104033756 }, "harness|winogrande|5": { "acc": 0.6101026045777427, "acc_stderr": 0.013707547317008465 }, "harness|gsm8k|5": { "acc": 0.29037149355572406, "acc_stderr": 0.01250359248181895 } } ``` ## 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]

liuyanchen1015/MULTI_VALUE_rte_completive_done

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 481976 num_examples: 1156 - name: train num_bytes: 421696 num_examples: 962 download_size: 578766 dataset_size: 903672 --- # Dataset Card for "MULTI_VALUE_rte_completive_done" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

toilaluan/t2i_reward_v3

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: model_type dtype: string - name: request_id dtype: int64 - name: topic dtype: string - name: reward dtype: float64 - name: individual_rewards struct: - name: image_rewarder dtype: float64 - name: hps_v2_rewarder dtype: float64 splits: - name: train num_bytes: 205400 num_examples: 2400 download_size: 49480 dataset_size: 205400 --- # Dataset Card for "t2i_reward_v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Slichi/universe

--- license: openrail ---

IndonesiaAI/dpo-dataset

--- dataset_info: features: - name: qid dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 14256438583.53102 num_examples: 3798835 - name: test num_bytes: 1584049565.4689815 num_examples: 422093 download_size: 8864292488 dataset_size: 15840488149.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

DynamicSuperb/StressDetection_MIRSD

--- dataset_info: features: - name: file dtype: string - name: audio dtype: audio - name: label dtype: string - name: word dtype: string - name: instruction dtype: string splits: - name: test num_bytes: 19241423.221727517 num_examples: 200 download_size: 17768718 dataset_size: 19241423.221727517 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "stress_dection_MIR_SD" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/tsubaki_bluearchive

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of tsubaki/春日ツバキ/椿 (Blue Archive) This is the dataset of tsubaki/春日ツバキ/椿 (Blue Archive), containing 500 images and their tags. The core tags of this character are `black_hair, short_hair, animal_ears, breasts, large_breasts, hair_between_eyes, red_halo, halo, black_eyes, tassel, raccoon_ears`, 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 | 500 | 952.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsubaki_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 784.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tsubaki_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1379 | 1.60 GiB | [Download](https://huggingface.co/datasets/CyberHarem/tsubaki_bluearchive/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/tsubaki_bluearchive', 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 | 15 | ![](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, blush, elbow_gloves, red_gloves, rope, solo, looking_at_viewer, red_sailor_collar, red_skirt, simple_background, white_background, armpits, arms_up, open_mouth, underboob, arms_behind_head, breast_curtain, sweat, sideboob, sideless_outfit | | 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, elbow_gloves, red_gloves, red_sailor_collar, red_skirt, revealing_clothes, sideboob, simple_background, solo, thighs, white_background, blush, looking_at_viewer, pleated_skirt, bare_shoulders, sideless_outfit, breast_curtain, underboob, shimenawa, two-tone_shirt | | 2 | 14 | ![](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) | 1boy, 1girl, blush, hetero, penis, pussy, red_sailor_collar, vaginal, open_mouth, solo_focus, elbow_gloves, mosaic_censoring, red_gloves, spread_legs, huge_breasts, nipples, sweat, red_skirt, breast_curtain, clothed_sex, on_back, rope_belt, cowgirl_position, cum, girl_on_top, looking_at_viewer, on_bed, thighs, two-tone_shirt | | 3 | 6 | ![](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, bare_shoulders, cleavage, navel, open_mouth, stomach, blush, solo, alternate_costume, cowboy_shot, looking_at_viewer, wet, white_bikini, choker, collarbone, halterneck, red_flower, sarong, simple_background, smile, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | elbow_gloves | red_gloves | rope | solo | looking_at_viewer | red_sailor_collar | red_skirt | simple_background | white_background | armpits | arms_up | open_mouth | underboob | arms_behind_head | breast_curtain | sweat | sideboob | sideless_outfit | revealing_clothes | thighs | pleated_skirt | bare_shoulders | shimenawa | two-tone_shirt | 1boy | hetero | penis | pussy | vaginal | solo_focus | mosaic_censoring | spread_legs | huge_breasts | nipples | clothed_sex | on_back | rope_belt | cowgirl_position | cum | girl_on_top | on_bed | cleavage | navel | stomach | alternate_costume | cowboy_shot | wet | white_bikini | choker | collarbone | halterneck | red_flower | sarong | smile | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:---------------|:-------------|:-------|:-------|:--------------------|:--------------------|:------------|:--------------------|:-------------------|:----------|:----------|:-------------|:------------|:-------------------|:-----------------|:--------|:-----------|:------------------|:--------------------|:---------|:----------------|:-----------------|:------------|:-----------------|:-------|:---------|:--------|:--------|:----------|:-------------|:-------------------|:--------------|:---------------|:----------|:--------------|:----------|:------------|:-------------------|:------|:--------------|:---------|:-----------|:--------|:----------|:--------------------|:--------------|:------|:---------------|:---------|:-------------|:-------------|:-------------|:---------|:--------| | 0 | 15 | ![](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 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 14 | ![](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 | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 3 | 6 | ![](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 | X | X | X | X | X | X |

Dogge/bluemoon-fandom-1-1-rp-cleaned-korean-tranlated

--- license: wtfpl ---

satendra4u2022/dpo

--- license: mit ---

tashkeela

--- annotations_creators: - no-annotation language_creators: - found language: - ar license: - gpl-2.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Tashkeela tags: - diacritics-prediction dataset_info: features: - name: text dtype: string - name: book dtype: string config_name: plain_text splits: - name: train num_bytes: 1081110249 num_examples: 97 download_size: 183393530 dataset_size: 1081110249 --- # Dataset Card for Tashkeela ## 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:** [Tashkeela](https://sourceforge.net/projects/tashkeela/) - **Repository:** [Tashkeela](https://sourceforge.net/projects/tashkeela/) - **Paper:** [Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems](https://www.sciencedirect.com/science/article/pii/S2352340917300112) - **Point of Contact:** [Taha Zerrouki](mailto:t_zerrouki@esi.dz) ### Dataset Summary It contains 75 million of fully vocalized words mainly 97 books from classical and modern Arabic language. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The dataset is based on Arabic. ## Dataset Structure ### Data Instances ``` {'book': 'zip://Tashkeela-arabic-diacritized-text-utf8-0.3/texts.txt/msa/al-kalema.org/أشكال-التجارب-في-مَثَل-الزارع.htm.txt::https://sourceforge.net/projects/tashkeela/files/latest/download', 'text': 'الكلمة\n\n\nصفحه اصلی\nاشترك\nالكتاب المقدس\nجميع المقالات\nالترتيب بالموضوع\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nهذا المقال على نسخة PDF\n\n\nأشكال التجارب في مَثَل الزارع\n\n\tقد رأينا في مقال " \nوسائل واشكال التجارب" الأشكال التي من الممكن أن تتخذها التجارب (وخاصة الاختبارات التي تأتي من خلال الآلام والاضطهاد وأشراك إطاعة شهوات الإنسان العتيق، الجسد)، نستطيع أيضاً أن نرى هذه الأقسام عاملة في مثال الزارع. هناك مجموعتين في مثال الزارع أنه برغم من سماعهم واستقبالهم للكلمة، إلا أنهم لم يجلبوا ثماراً. والسؤال هو لماذا؟\n\n1. التجارب في القسم الثاني من مثال الزارع\n\nفيما يخص القسم الثاني من مثال الزارع، تخبرنا عنها متى 13: 20- 21 ولوقا 8: 13 \nمتى 13: 20- 21\n" وَالْمَزْرُوعُ عَلَى الأَمَاكِنِ الْمُحْجِرَةِ هُوَ الَّذِي يَسْمَعُ الْكَلِمَةَ، وَحَالاً يَقْبَلُهَا بِفَرَحٍ، وَلكِنْ لَيْسَ لَهُ أَصْلٌ فِي ذَاتِهِ، بَلْ هُوَ إِلَى حِينٍ. فَإِذَا حَدَثَ ضِيقٌ أَوِ اضْطِهَادٌ مِنْ أَجْلِ الْكَلِمَةِ فَحَالاً يَعْثُرُ."\nلوقا 8: 13\n" وَالَّذِينَ عَلَى الصَّخْرِ هُمُ الَّذِينَ مَتَى سَمِعُوا يَقْبَلُونَ الْكَلِمَةَ بِفَرَحٍ، وَهؤُلاَءِ لَيْسَ لَهُمْ أَصْلٌ، فَيُؤْمِنُونَ إِلَى حِينٍ، وَفِي وَقْتِ التَّجْرِبَةِ يَرْتَدُّونَ."\n\nكما نرى، الناس في هذا القسم سمعوا الكلمة وحالاً قبلوها بفرح! بمعنى آخر، لقد كانوا متحمسين جداً تجاه الكلمة. ثم جاءت التجارب والاختبارات في شكل ضيق واضطهاد من أجل الكلمة، أي أنه بسبب الكلمة، اضطهد هؤلاء الناس. وعندئذ توقفوا. عوضاً عن أن يحفظوا ويتمسكوا بالكلمة التي قد حدث واستقبلوها بفرح، تراجعوا وسقطوا بعيداً، إن كنت مؤمناً صغيراً مليء بالحماسة تجاه الله، وبالرغم من أنه قد يبدو أنه لا يوجد شيطان من حولك، فهذا لن يستمر إلى الأبد. فالتجارب والاختبارات آتية. ستحتاج إلى أن تحفظ وتتمسك بالإيمان وبالكلمة التي قد حدث واستقبلتها بفرح. كما تقول لنا الكلمة:\nعبرانيين 10: 35- 39\n" فَلاَ تَطْرَحُوا ثِقَتَكُمُ الَّتِي لَهَا مُجَازَاةٌ عَظِيمَةٌ. لأَنَّكُمْ تَحْتَاجُونَ إِلَى الصَّبْرِ، حَتَّى إِذَا صَنَعْتُمْ مَشِيئَةَ اللهِ تَنَالُونَ الْمَوْعِدَ. لأَنَّهُ بَعْدَ قَلِيل جِدًّا «سَيَأْتِي الآتِي وَلاَ يُبْطِئُ. أَمَّا الْبَارُّ فَبِالإِيمَانِ يَحْيَا، وَإِنِ ارْتَدَّ لاَ تُسَرُّ بِهِ نَفْسِي». وَأَمَّا نَحْنُ فَلَسْنَا مِنَ الارْتِدَادِ لِلْهَلاَكِ، بَلْ مِنَ الإِيمَانِ لاقْتِنَاءِ النَّفْسِ."\n\nوالضيق قد يأخذ أشكالاً عديدة. رأيت أناساً يسقطون، تاركين الإيمان لأن آبائهم أو أقاربهم وأصدقائهم قد عارضوهم ورفضوهم بسبب إيمانهم. بالطبع قد يأخذ الاضطهاد أشكالاً أكثر من ذلك أيضاً، مثل أن تلقى في سجن أو أن تعذب لأجل إيمانك. قد يسبب الموت كذلك، كما حدث مع اسطفانوس ويعقوب أخو يوحنا. وتقول الكلمة من أجلك ومن أجل كل الذين حوكموا:\nرومية 16: 19- 20\n" لأَنَّ طَاعَتَكُمْ ذَاعَتْ إِلَى الْجَمِيعِ، فَأَفْرَحُ أَنَا بِكُمْ، وَأُرِيدُ أَنْ تَكُونُوا حُكَمَاءَ لِلْخَيْرِ وَبُسَطَاءَ لِلشَّرِّ. وَإِلهُ السَّلاَمِ سَيَسْحَقُ الشَّيْطَانَ تَحْتَ أَرْجُلِكُمْ سَرِيعًا."\nو بطرس الأولى 5: 8- 10\n" اُصْحُوا وَاسْهَرُوا. لأَنَّ إِبْلِيسَ خَصْمَكُمْ كَأَسَدٍ زَائِرٍ، يَجُولُ مُلْتَمِسًا مَنْ يَبْتَلِعُهُ هُوَ. فَقَاوِمُوهُ، رَاسِخِينَ فِي الإِيمَانِ، عَالِمِينَ أَنَّ نَفْسَ هذِهِ الآلاَمِ تُجْرَى عَلَى إِخْوَتِكُمُ الَّذِينَ فِي الْعَالَمِ. وَإِلهُ كُلِّ نِعْمَةٍ الَّذِي دَعَانَا إِلَى مَجْدِهِ الأَبَدِيِّ فِي الْمَسِيحِ يَسُوعَ، بَعْدَمَا تَأَلَّمْتُمْ يَسِيرًا، هُوَ يُكَمِّلُكُمْ، وَيُثَبِّتُكُمْ، وَيُقَوِّيكُمْ، وَيُمَكِّنُكُمْ."\n\nتمسك بالإيمان حتى النهاية. ضع حياتك ووضعك بين يدي الله وكن مستعداً لمواجهة أي شيء قد يحدث، أجل وحتى السخرية والعذاب. الله معك، سيقويك وسيعينك تماماً مثلما فعل مع يسوع في بستان جسثيماني. وتماماً مثلما فعل مع بولس في السجن عندما اضطهد من قِبَل اليهود (أعمال الرسل 23: 11). وكما قال بولس في كورنثوس الثانية 1: 7:" عَالِمِينَ أَنَّكُمْ كَمَا أَنْتُمْ شُرَكَاءُ فِي الآلاَمِ، كَذلِكَ فِي التَّعْزِيَةِ أَيْضًا." فالعزاء الآتي من الله يوازن أي سخرية أو أي عذاب قد يأتي إلينا من أي إنسان.\n\n2. التجارب في القسم الثالث من مثال الزارع\n\nبخصوص القسم الثالث من مثال الزارع، فنقرأ عنه في مرقس 4: 18- 19\n\n" وَهؤُلاَءِ هُمُ الَّذِينَ زُرِعُوا بَيْنَ الشَّوْكِ: هؤُلاَءِ هُمُ الَّذِينَ يَسْمَعُونَ الْكَلِمَةَ، وَهُمُومُ هذَا الْعَالَمِ وَغُرُورُ الْغِنَى وَشَهَوَاتُ سَائِرِ الأَشْيَاءِ تَدْخُلُ وَتَخْنُقُ الْكَلِمَةَ فَتَصِيرُ بِلاَ ثَمَرٍ."\nو لوقا 8: 14\n" وَالَّذِي سَقَطَ بَيْنَ الشَّوْكِ هُمُ الَّذِينَ يَسْمَعُونَ، ثُمَّ يَذْهَبُونَ فَيَخْتَنِقُونَ مِنْ هُمُومِ الْحَيَاةِ وَغِنَاهَا وَلَذَّاتِهَا، وَلاَ يُنْضِجُونَ ثَمَرًا."\n\nهؤلاء قد سمعوا الكلمة وفهموها ولكنهم صاروا بلا ثمر، وما هو السبب؟ السبب هو لأنهم تركوا أبواب قلوبهم مفتوحة لأشواك " وَهُمُومُ هذَا الْعَالَمِ وَغُرُورُ الْغِنَى وَشَهَوَاتُ سَائِرِ الأَشْيَاءِ" (مرقس 4: 19)، والتي تدخل فتخنق الكلمة، كما رأينا يعقوب دائماً ما يقول:\nيعقوب 1: 13- 15\n" لاَ يَقُلْ أَحَدٌ إِذَا جُرِّبَ: «إِنِّي أُجَرَّبُ مِنْ قِبَلِ اللهِ»، لأَنَّ اللهَ غَيْرُ مُجَرَّبٍ بِالشُّرُورِ، وَهُوَ لاَ يُجَرِّبُ أَحَدًا. وَلكِنَّ كُلَّ وَاحِدٍ يُجَرَّبُ إِذَا انْجَذَبَ وَانْخَدَعَ مِنْ شَهْوَتِهِ. ثُمَّ الشَّهْوَةُ إِذَا حَبِلَتْ تَلِدُ خَطِيَّةً، وَالْخَطِيَّةُ إِذَا كَمَلَتْ تُنْتِجُ مَوْتًا."\nوتيموثاوس الأولى 6: 9 تقول لنا\n" وَأَمَّا الَّذِينَ يُرِيدُونَ أَنْ يَكُونُوا أَغْنِيَاءَ، فَيَسْقُطُونَ فِي تَجْرِبَةٍ وَفَخٍّ وَشَهَوَاتٍ كَثِيرَةٍ غَبِيَّةٍ وَمُضِرَّةٍ، تُغَرِّقُ النَّاسَ فِي الْعَطَبِ وَالْهَلاَكِ."\n\nيجب أن نلاحظ شيئاً هنا: أن تأثير هموم الحياة هو نفس التأثير الذي لتجارب الغنى وشهوات الأشياء الأخرى. فهموم الحياة أيضاً لا تجلب الثمار، إذاً فإن اردت أن تكون مسيحياً مثمراً، أي مسيحي حقيقي وليس فقط مسيحي اسمي، فيجب عليك أن تزيل أشواك الهموم والغنى وملذات الحياة وأن تمنعهم من العودة مرة أخرى. تحتاج إلى أن تفعل شيئاً، تحتاج إلى أن تتغير والله سيعينك في هذا إن كنت حقاً تريده. التجارب في القسم الثالث من مثال الزارع لا تأتي من خلال الاضطهاد والآلام عن طريق الشيطان. ولكن هنا تأخذ التجارب صوراً أكثر مكراً والتي مع هذا تتطلب مقاومتنا. الاهتمام بما يهتم به هذا العالم ("هموم هذا العالم")، الرغبة في الغنى أو اشتهاء الأشياء الأخرى هي أمور خطيرة جداً. إنها أشواك يجب إزالتها. كما رأينا بولس يقول:\nرومية 13: 14\n" بَلِ الْبَسُوا الرَّبَّ يَسُوعَ الْمَسِيحَ، وَلاَ تَصْنَعُوا تَدْبِيرًا لِلْجَسَدِ لأَجْلِ الشَّهَوَاتِ."\n\n" لاَ تَصْنَعُوا تَدْبِيرًا لِلْجَسَدِ" والتي تعني أنه يجب علينا أن لا نهتم بالجسد وشهواته. ولكن عوضاً عن ذلك ينبغي لنا أن نطعم أنفسنا بلبن الكلمة الصافي الذي ننمو بواستطه (بطرس الأولى 2: 2).\n\n\nتاسوس كيولاشوجلو'} ``` ### Data Fields - `book` (str): Book filename. - `text` (str): Text of the book. ### Data Splits The dataset is not split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization The Modern Standard Arabic texts crawled from the Internet. #### Who are the source language producers? Websites. ### Annotations The dataset does not contain any additional 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 [GNU General Public License, version 2 (GPLv2)](https://opensource.org/licenses/GPL-2.0). ### Citation Information The dataset was published on this [paper](https://www.sciencedirect.com/science/article/pii/S2352340917300112#!): ``` @article{zerrouki2017tashkeela, title={Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems}, author={Zerrouki, Taha and Balla, Amar}, journal={Data in brief}, volume={11}, pages={147}, year={2017}, publisher={Elsevier} } ``` ### Contributions Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset.

CyberHarem/nearl_arknights

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of nearl/ニアール/临光 (Arknights) This is the dataset of nearl/ニアール/临光 (Arknights), containing 353 images and their tags. The core tags of this character are `animal_ears, blonde_hair, long_hair, horse_ears, animal_ear_fluff, horse_girl, yellow_eyes, tail, ponytail, horse_tail, hair_between_eyes, breasts, large_breasts`, 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 | 353 | 649.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nearl_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 353 | 537.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nearl_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 917 | 1.03 GiB | [Download](https://huggingface.co/datasets/CyberHarem/nearl_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/nearl_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 | 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, bare_shoulders, cowboy_shot, crop_top, looking_at_viewer, midriff, navel, solo, stomach, black_pants, black_sports_bra, leggings, simple_background, standing, sweat, alternate_costume, blush, thighs, white_background, armpits, arms_behind_head, arms_up, bare_arms, cropped_legs, parted_lips, smile, stretching, wristband | | 1 | 5 | ![](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_arms, bare_shoulders, cleavage, looking_at_viewer, midriff, navel, solo, stomach, black_shorts, short_shorts, simple_background, smile, standing, thighs, alternate_costume, black_sports_bra, cowboy_shot, crop_top, hand_up, medium_breasts, abs, bike_shorts, dolphin_shorts, parted_lips, very_long_hair, white_background | | 2 | 7 | ![](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, blush, looking_at_viewer, nipples, solo, collarbone, completely_nude, navel, pussy, cowboy_shot, standing, stomach, sidelocks, smile | | 3 | 5 | ![](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, blush, horse_penis, huge_penis, looking_at_viewer, solo, anus, ass, simple_background, from_behind, full-package_futanari, huge_breasts, huge_testicles, looking_back, nipples, pussy, uncensored, centaur, completely_nude, cum, erection, grey_background, monster_girl, multiple_legs, parted_lips, torn_clothes | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, bare_shoulders, looking_at_viewer, official_alternate_costume, outdoors, solo, swimsuit_cover-up, white_one-piece_swimsuit, cloud, covered_navel, cowboy_shot, off_shoulder, parted_lips, smile, blue_sky, competition_swimsuit, day, groin, medium_breasts, open_jacket, see-through, standing, thigh_strap, ass_visible_through_thighs, bird, blue_jacket, collarbone, eyewear_on_head, long_sleeves, sunglasses | | 5 | 7 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, bare_shoulders, cowboy_shot, solo, thighs, white_one-piece_swimsuit, collarbone, competition_swimsuit, covered_navel, eyewear_on_head, looking_at_viewer, official_alternate_costume, standing, sunglasses, outdoors, water, arm_up, bare_arms, day, smile, blue_sky, blush, cloud, hand_up, swimsuit_cover-up, thigh_strap, wading, wet | | 6 | 9 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, closed_mouth, headset, implied_extra_ears, looking_at_viewer, simple_background, solo, white_background, portrait, upper_body, smile, black_scarf, shoulder_armor | | 7 | 15 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, breastplate, headset, solo, upper_body, pauldrons, scarf, sidelocks, simple_background, closed_mouth, white_background, looking_at_viewer, parted_lips | | 8 | 6 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, headset, solo, weapon, breastplate, holding, looking_at_viewer, orange_eyes, pauldrons, scarf, shield, sidelocks, upper_body, v-shaped_eyebrows, open_mouth | | 9 | 19 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, solo, breastplate, headset, black_gloves, looking_at_viewer, pauldrons, sidelocks, holding_weapon, holding_shield, black_scarf, cowboy_shot, black_dress, closed_mouth, headphones | | 10 | 14 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | 1girl, breastplate, full_body, pauldrons, solo, black_gloves, headset, looking_at_viewer, standing, black_dress, black_footwear, sidelocks, black_skirt, high_heel_boots, black_scarf, simple_background, holding_weapon, torn_clothes, holding_shield, torn_scarf, closed_mouth, white_background, axe, floating_hair | | 11 | 5 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | 1girl, headset, looking_at_viewer, shoulder_armor, solo, black_gloves, holding_weapon, implied_extra_ears, upper_body, belt | | 12 | 7 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | 1girl, headset, implied_extra_ears, looking_at_viewer, solo, white_dress, black_gloves, official_alternate_costume, cowboy_shot, holding_weapon, simple_background, belt, shoulder_armor, black_background, medium_breasts, orange_eyes, polearm | | 13 | 6 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-sample4.png) | 1girl, headset, holding_polearm, implied_extra_ears, solo, armored_boots, looking_at_viewer, shoulder_armor, standing, white_dress, black_gloves, full_body, spear, outdoors, white_coat | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | cowboy_shot | crop_top | looking_at_viewer | midriff | navel | solo | stomach | black_pants | black_sports_bra | leggings | simple_background | standing | sweat | alternate_costume | blush | thighs | white_background | armpits | arms_behind_head | arms_up | bare_arms | cropped_legs | parted_lips | smile | stretching | wristband | cleavage | black_shorts | short_shorts | hand_up | medium_breasts | abs | bike_shorts | dolphin_shorts | very_long_hair | nipples | collarbone | completely_nude | pussy | sidelocks | horse_penis | huge_penis | anus | ass | from_behind | full-package_futanari | huge_breasts | huge_testicles | looking_back | uncensored | centaur | cum | erection | grey_background | monster_girl | multiple_legs | torn_clothes | official_alternate_costume | outdoors | swimsuit_cover-up | white_one-piece_swimsuit | cloud | covered_navel | off_shoulder | blue_sky | competition_swimsuit | day | groin | open_jacket | see-through | thigh_strap | ass_visible_through_thighs | bird | blue_jacket | eyewear_on_head | long_sleeves | sunglasses | water | arm_up | wading | wet | closed_mouth | headset | implied_extra_ears | portrait | upper_body | black_scarf | shoulder_armor | breastplate | pauldrons | scarf | weapon | holding | orange_eyes | shield | v-shaped_eyebrows | open_mouth | black_gloves | holding_weapon | holding_shield | black_dress | headphones | full_body | black_footwear | black_skirt | high_heel_boots | torn_scarf | axe | floating_hair | belt | white_dress | black_background | polearm | holding_polearm | armored_boots | spear | white_coat | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-----------------|:--------------|:-----------|:--------------------|:----------|:--------|:-------|:----------|:--------------|:-------------------|:-----------|:--------------------|:-----------|:--------|:--------------------|:--------|:---------|:-------------------|:----------|:-------------------|:----------|:------------|:---------------|:--------------|:--------|:-------------|:------------|:-----------|:---------------|:---------------|:----------|:-----------------|:------|:--------------|:-----------------|:-----------------|:----------|:-------------|:------------------|:--------|:------------|:--------------|:-------------|:-------|:------|:--------------|:------------------------|:---------------|:-----------------|:---------------|:-------------|:----------|:------|:-----------|:------------------|:---------------|:----------------|:---------------|:-----------------------------|:-----------|:--------------------|:---------------------------|:--------|:----------------|:---------------|:-----------|:-----------------------|:------|:--------|:--------------|:--------------|:--------------|:-----------------------------|:-------|:--------------|:------------------|:---------------|:-------------|:--------|:---------|:---------|:------|:---------------|:----------|:---------------------|:-----------|:-------------|:--------------|:-----------------|:--------------|:------------|:--------|:---------|:----------|:--------------|:---------|:--------------------|:-------------|:---------------|:-----------------|:-----------------|:--------------|:-------------|:------------|:-----------------|:--------------|:------------------|:-------------|:------|:----------------|:-------|:--------------|:-------------------|:----------|:------------------|:----------------|:--------|:-------------| | 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 | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 | ![](samples/2/clu2-sample0.png) | 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| | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 9 | 19 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | X | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | X | | X | X | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | 10 | 14 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | X | | | | X | | | X | | | | | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | X | | X | X | | | | | | | | X | X | X | X | | X | X | X | X | X | X | X | | | | | | | | | | 11 | 5 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | X | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | X | | X | | | | | | | | | | X | X | | | | | | | | | | | X | | | | | | | | | 12 | 7 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | X | | X | | X | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | X | | | | | | X | | | | X | X | | | | | | | | | 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simonveitner/samsum_rewritten_0_to_1200

--- dataset_info: features: - name: orginal_text dtype: string - name: rewrite_prompt dtype: string - name: rewritten_text dtype: string splits: - name: train num_bytes: 421524 num_examples: 1200 download_size: 263197 dataset_size: 421524 configs: - config_name: default data_files: - split: train path: data/train-* ---

ZhangShenao/0.00045_idpo_decalpha_ref_response

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: reference_response dtype: string splits: - name: train_prefs_1 num_bytes: 164111773 num_examples: 20378 - name: test_prefs_1 num_bytes: 16019213 num_examples: 2000 download_size: 99390696 dataset_size: 180130986 configs: - config_name: default data_files: - split: train_prefs_1 path: data/train_prefs_1-* - split: test_prefs_1 path: data/test_prefs_1-* --- # Dataset Card for "0.00045_idpo_decalpha_ref_response" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

fathyshalab/reklambox-balanced-no-stopwords

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: label_name dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 401120 num_examples: 1102 - name: test num_bytes: 140041 num_examples: 276 download_size: 335528 dataset_size: 541161 --- # Dataset Card for "reklambox-balanced-no-stopwords" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/matsuda_arisa_theidolmstermillionlive

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of matsuda_arisa/松田亜利沙 (THE iDOLM@STER: Million Live!) This is the dataset of matsuda_arisa/松田亜利沙 (THE iDOLM@STER: Million Live!), containing 134 images and their tags. The core tags of this character are `brown_hair, twintails, long_hair, brown_eyes, bangs`, 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 | 134 | 132.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsuda_arisa_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 134 | 91.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsuda_arisa_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 280 | 175.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsuda_arisa_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 134 | 120.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsuda_arisa_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 280 | 221.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matsuda_arisa_theidolmstermillionlive/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/matsuda_arisa_theidolmstermillionlive', 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 | 6 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, open_mouth, skirt, solo, :d, blush, boots, hair_bow, jewelry | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | open_mouth | skirt | solo | :d | blush | boots | hair_bow | jewelry | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------------|:--------|:-------|:-----|:--------|:--------|:-----------|:----------| | 0 | 6 | ![](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 |

mangostin2010/Korean-Wise-Saying

--- license: unknown ---

hirxn/custom_data

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 16378 num_examples: 7 download_size: 15015 dataset_size: 16378 configs: - config_name: default data_files: - split: train path: data/train-* ---

BangumiBase/mawarupenguindrum

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Mawaru Penguindrum This is the image base of bangumi Mawaru Penguindrum, we detected 23 characters, 1725 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 19 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 177 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 81 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 18 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 76 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 206 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 19 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 14 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 64 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 11 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 313 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 24 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 11 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 306 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 19 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 19 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 13 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 16 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 37 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 17 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 17 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 8 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | noise | 240 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

memray/kp20k

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

crylake/facesyntheticsspigacaptioned_30percent

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: spiga_seg dtype: image - name: image_caption dtype: string splits: - name: train num_bytes: 9080640177.0 num_examples: 30000 download_size: 9066954510 dataset_size: 9080640177.0 --- # Dataset Card for "facesyntheticsspigacaptioned_30percent" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

indiejoseph/wikipedia-zh-filtered

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 258992903 num_examples: 44344 download_size: 164712496 dataset_size: 258992903 --- # Dataset Card for "wikipedia-zh-filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rpadilla/ft-capstone2

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 36223 num_examples: 12 - name: test num_bytes: 23728 num_examples: 7 download_size: 57751 dataset_size: 59951 --- # Dataset Card for "ft-capstone2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 581821016 num_examples: 11666570 - name: validation num_bytes: 56120230 num_examples: 1026747 download_size: 421803322 dataset_size: 637941246 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* ---

nakayama/hh-rlhf-helpful-base-ja

--- license: mit language: - ja --- https://github.com/anthropics/hh-rlhf　の内容のうち、helpful-base内のchosenに記載されている英文をfuguMTで翻訳、うまく翻訳できていないものを除外、修正したものです。

gsh3729/sw2

--- dataset_info: features: - name: filename dtype: string - name: tif dtype: binary - name: tfw dtype: binary splits: - name: train num_bytes: 22663 num_examples: 2 download_size: 23838 dataset_size: 22663 configs: - config_name: default data_files: - split: train path: data/train-* ---

kyusque/github-issues

--- dataset_info: features: - name: url dtype: string - name: repository_url dtype: string - name: labels_url dtype: string - name: comments_url dtype: string - name: events_url dtype: string - name: html_url dtype: string - name: id dtype: int64 - name: node_id dtype: string - name: number dtype: int64 - name: title dtype: string - 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: 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: state dtype: string - name: locked dtype: bool - 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: 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: milestone struct: - name: closed_at dtype: string - 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: string - 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: comments sequence: string - name: created_at dtype: timestamp[ns, tz=UTC] - name: updated_at dtype: timestamp[ns, tz=UTC] - name: closed_at dtype: timestamp[ns, tz=UTC] - name: author_association dtype: string - name: active_lock_reason dtype: float64 - name: draft dtype: float64 - 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: body dtype: 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: timeline_url dtype: string - name: performed_via_github_app dtype: float64 - name: state_reason dtype: string - name: is_pull_request dtype: bool splits: - name: train num_bytes: 30854747 num_examples: 6106 download_size: 8722074 dataset_size: 30854747 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dinaaaaaa/lima_rand_sel_50_preference_self_reward

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: chosen-rating dtype: int64 - name: rejected dtype: string - name: rejected-rating dtype: int64 splits: - name: train num_bytes: 177447 num_examples: 223 download_size: 44667 dataset_size: 177447 configs: - config_name: default data_files: - split: train path: data/train-* ---

automated-research-group/llama2_7b_chat-commonsense_qa-results

--- dataset_info: config_name: '{''do_sample''=False, ''beams''=1}' features: - name: id dtype: string - name: prediction dtype: string - name: commonsense_qa_accuracy dtype: bool splits: - name: train num_bytes: 159243 num_examples: 1221 download_size: 83126 dataset_size: 159243 configs: - config_name: '{''do_sample''=False, ''beams''=1}' data_files: - split: train path: '{''do_sample''=False, ''beams''=1}/train-*' ---

avankumar/new_data_model_methanol_lca_500

--- dataset_info: features: - name: Train dtype: string splits: - name: train num_bytes: 378305 num_examples: 502 download_size: 145305 dataset_size: 378305 configs: - config_name: default data_files: - split: train path: data/train-* ---

ImperialIndians23/nlp_cw_data_processed

--- dataset_info: features: - name: par_id dtype: string - name: community dtype: string - name: text dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 1775055 num_examples: 8375 - name: valid num_bytes: 435628 num_examples: 2094 download_size: 1354140 dataset_size: 2210683 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* ---

sudeepag/sampled-t0_fsnoopt_data

--- dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: _template_idx dtype: int64 - name: _task_source dtype: string - name: _task_name dtype: string - name: _template_type dtype: string splits: - name: train num_bytes: 7365274691.188863 num_examples: 3219106 download_size: 4167856318 dataset_size: 7365274691.188863 configs: - config_name: default data_files: - split: train path: data/train-* ---

NebulaeWis/gelbooru_images

--- task_categories: - text-to-image language: - en pretty_name: gelbooru size_categories: - 1M<n<10M --- Collect images from https://gelbooru.com/ id range:0~9393795 encoding: UTF-8 search tags:"-animated -3d_(artwork) -webm -gif -video -real_life -comic -photo_(medium)" max shortest edge size ==1536 ,save using .webp with 90%quality The total number search iamges is 8364374, filtered out 18832. image not in it: gif/video truncated(more than 10+ repeat download) too large(over pillow default limit pixels) In the metainfo last 5 columns,[artist,character,copyright,metadata,tags],"None" means lack of anything, rather than string "None". *.txt from the crawler results,it's' not captions. please build captions from metainfo and tagger Disclaimer Disclaimer: By downloading or using this dataset, you agree to the following terms and conditions: Purpose of Crawling: The dataset is obtained by crawling a publicly available website. The purpose of this crawling behavior is to upload the dataset to Hugging Face in order to alleviate the load on the original booru site. Data Accuracy: We make efforts to ensure the accuracy of the dataset, but we cannot guarantee the completeness and accuracy of the data. Users are responsible for evaluating the quality and accuracy of the dataset and bear any consequences arising from inaccurate or incomplete data. Full Responsibility: The uploader of this dataset shall not be liable for any losses or damages (including but not limited to any direct, indirect, incidental damages) arising from the use, misuse, or inability to use the dataset in any way. Please read and understand the above terms and conditions carefully before using this dataset. If you do not agree to these terms and conditions, you are not allowed to use this dataset.

breno30/PhPacote

--- license: openrail ---

AdapterOcean/med_alpaca_standardized_cluster_19_std

--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 18706021 num_examples: 34698 download_size: 9286969 dataset_size: 18706021 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "med_alpaca_standardized_cluster_19_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

StevenLe456/viet-tones

--- dataset_info: features: - name: audio dtype: audio - name: label dtype: int64 splits: - name: train num_bytes: 177262390.44 num_examples: 1080 download_size: 0 dataset_size: 177262390.44 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "viet-tones" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_seyf1elislam__KuTrix-7b

--- pretty_name: Evaluation run of seyf1elislam/KuTrix-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [seyf1elislam/KuTrix-7b](https://huggingface.co/seyf1elislam/KuTrix-7b) 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_seyf1elislam__KuTrix-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-16T06:30:17.941313](https://huggingface.co/datasets/open-llm-leaderboard/details_seyf1elislam__KuTrix-7b/blob/main/results_2024-03-16T06-30-17.941313.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\": 0.6576587418096124,\n\ \ \"acc_stderr\": 0.03201598935752146,\n \"acc_norm\": 0.6575488737220112,\n\ \ \"acc_norm_stderr\": 0.03267746108371606,\n \"mc1\": 0.5410036719706243,\n\ \ \"mc1_stderr\": 0.017444544447661203,\n \"mc2\": 0.7084705277313671,\n\ \ \"mc2_stderr\": 0.014694482049743158\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6834470989761092,\n \"acc_stderr\": 0.013592431519068077,\n\ \ \"acc_norm\": 0.7047781569965871,\n \"acc_norm_stderr\": 0.013329750293382318\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7027484564827724,\n\ \ \"acc_stderr\": 0.004561141293448457,\n \"acc_norm\": 0.8794064927305317,\n\ \ \"acc_norm_stderr\": 0.0032498873947065044\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\ \ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569526,\n\ \ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569526\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\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\ \ \"acc_stderr\": 0.035676037996391706,\n \"acc_norm\": 0.6763005780346821,\n\ \ \"acc_norm_stderr\": 0.035676037996391706\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\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\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\ \ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\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\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440678,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440678\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\ \ \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n\ \ \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\ \ \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n\ \ \"acc_norm_stderr\": 0.02341529343356853\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\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\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\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n\ \ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.337037037037037,\n \"acc_stderr\": 0.02882088466625326,\n \ \ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.02882088466625326\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977934,\n\ \ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977934\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\ acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8532110091743119,\n \"acc_stderr\": 0.015173141845126243,\n \"\ acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.015173141845126243\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.02450980392156861,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.02450980392156861\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \ \ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\ \ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\ \ \"acc_norm_stderr\": 0.03755265865037181\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\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.03989139859531771,\n\ \ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.03989139859531771\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\ \ \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n\ \ \"acc_norm_stderr\": 0.022209309073165616\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8301404853128991,\n\ \ \"acc_stderr\": 0.013428186370608303,\n \"acc_norm\": 0.8301404853128991,\n\ \ \"acc_norm_stderr\": 0.013428186370608303\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.02402774515526502,\n\ \ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.02402774515526502\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.44581005586592176,\n\ \ \"acc_stderr\": 0.016623998513333103,\n \"acc_norm\": 0.44581005586592176,\n\ \ \"acc_norm_stderr\": 0.016623998513333103\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7091503267973857,\n \"acc_stderr\": 0.02600480036395213,\n\ \ \"acc_norm\": 0.7091503267973857,\n \"acc_norm_stderr\": 0.02600480036395213\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.02378858355165854,\n\ \ \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 0.02378858355165854\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \ \ \"acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4804432855280313,\n\ \ \"acc_stderr\": 0.012760464028289299,\n \"acc_norm\": 0.4804432855280313,\n\ \ \"acc_norm_stderr\": 0.012760464028289299\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6875,\n \"acc_stderr\": 0.02815637344037142,\n \ \ \"acc_norm\": 0.6875,\n \"acc_norm_stderr\": 0.02815637344037142\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.684640522875817,\n \"acc_stderr\": 0.018798086284886887,\n \ \ \"acc_norm\": 0.684640522875817,\n \"acc_norm_stderr\": 0.018798086284886887\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399677,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399677\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\ \ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\ \ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061456,\n\ \ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061456\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5410036719706243,\n\ \ \"mc1_stderr\": 0.017444544447661203,\n \"mc2\": 0.7084705277313671,\n\ \ \"mc2_stderr\": 0.014694482049743158\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.819258089976322,\n \"acc_stderr\": 0.010814911009613981\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7005307050796058,\n \ \ \"acc_stderr\": 0.012616300735519665\n }\n}\n```" repo_url: https://huggingface.co/seyf1elislam/KuTrix-7b 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_16T06_30_17.941313 path: - '**/details_harness|arc:challenge|25_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-16T06-30-17.941313.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|gsm8k|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hellaswag|10_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-16T06-30-17.941313.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-management|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-16T06-30-17.941313.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|truthfulqa:mc|0_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-16T06-30-17.941313.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_16T06_30_17.941313 path: - '**/details_harness|winogrande|5_2024-03-16T06-30-17.941313.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-16T06-30-17.941313.parquet' - config_name: results data_files: - split: 2024_03_16T06_30_17.941313 path: - results_2024-03-16T06-30-17.941313.parquet - split: latest path: - results_2024-03-16T06-30-17.941313.parquet --- # Dataset Card for Evaluation run of seyf1elislam/KuTrix-7b  Dataset automatically created during the evaluation run of model [seyf1elislam/KuTrix-7b](https://huggingface.co/seyf1elislam/KuTrix-7b) 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_seyf1elislam__KuTrix-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-16T06:30:17.941313](https://huggingface.co/datasets/open-llm-leaderboard/details_seyf1elislam__KuTrix-7b/blob/main/results_2024-03-16T06-30-17.941313.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": 0.6576587418096124, "acc_stderr": 0.03201598935752146, "acc_norm": 0.6575488737220112, "acc_norm_stderr": 0.03267746108371606, "mc1": 0.5410036719706243, "mc1_stderr": 0.017444544447661203, "mc2": 0.7084705277313671, "mc2_stderr": 0.014694482049743158 }, "harness|arc:challenge|25": { "acc": 0.6834470989761092, "acc_stderr": 0.013592431519068077, "acc_norm": 0.7047781569965871, "acc_norm_stderr": 0.013329750293382318 }, "harness|hellaswag|10": { "acc": 0.7027484564827724, "acc_stderr": 0.004561141293448457, "acc_norm": 0.8794064927305317, "acc_norm_stderr": 0.0032498873947065044 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7039473684210527, "acc_stderr": 0.03715062154998904, "acc_norm": 0.7039473684210527, "acc_norm_stderr": 0.03715062154998904 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7056603773584905, "acc_stderr": 0.02804918631569526, "acc_norm": 0.7056603773584905, "acc_norm_stderr": 0.02804918631569526 }, "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": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6763005780346821, "acc_stderr": 0.035676037996391706, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.035676037996391706 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "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": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "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": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42328042328042326, "acc_stderr": 0.02544636563440678, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.02544636563440678 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.46825396825396826, "acc_stderr": 0.04463112720677172, "acc_norm": 0.46825396825396826, "acc_norm_stderr": 0.04463112720677172 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7838709677419354, "acc_stderr": 0.02341529343356853, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.02341529343356853 }, "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": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "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": 0.8860103626943006, "acc_stderr": 0.022935144053919443, "acc_norm": 0.8860103626943006, "acc_norm_stderr": 0.022935144053919443 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563976, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563976 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.337037037037037, "acc_stderr": 0.02882088466625326, "acc_norm": 0.337037037037037, "acc_norm_stderr": 0.02882088466625326 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6890756302521008, "acc_stderr": 0.030066761582977934, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.030066761582977934 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8532110091743119, "acc_stderr": 0.015173141845126243, "acc_norm": 0.8532110091743119, "acc_norm_stderr": 0.015173141845126243 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8578431372549019, "acc_stderr": 0.02450980392156861, "acc_norm": 0.8578431372549019, "acc_norm_stderr": 0.02450980392156861 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8143459915611815, "acc_stderr": 0.025310495376944856, "acc_norm": 0.8143459915611815, "acc_norm_stderr": 0.025310495376944856 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7938931297709924, "acc_stderr": 0.03547771004159465, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159465 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037181, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037181 }, "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": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness|hendrycksTest-management|5": { "acc": 0.7961165048543689, "acc_stderr": 0.03989139859531771, "acc_norm": 0.7961165048543689, "acc_norm_stderr": 0.03989139859531771 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8675213675213675, "acc_stderr": 0.022209309073165616, "acc_norm": 0.8675213675213675, "acc_norm_stderr": 0.022209309073165616 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8301404853128991, "acc_stderr": 0.013428186370608303, "acc_norm": 0.8301404853128991, "acc_norm_stderr": 0.013428186370608303 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7254335260115607, "acc_stderr": 0.02402774515526502, "acc_norm": 0.7254335260115607, "acc_norm_stderr": 0.02402774515526502 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.44581005586592176, "acc_stderr": 0.016623998513333103, "acc_norm": 0.44581005586592176, "acc_norm_stderr": 0.016623998513333103 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7091503267973857, "acc_stderr": 0.02600480036395213, "acc_norm": 0.7091503267973857, "acc_norm_stderr": 0.02600480036395213 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7592592592592593, "acc_stderr": 0.02378858355165854, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.02378858355165854 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5070921985815603, "acc_stderr": 0.02982449855912901, "acc_norm": 0.5070921985815603, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4804432855280313, "acc_stderr": 0.012760464028289299, "acc_norm": 0.4804432855280313, "acc_norm_stderr": 0.012760464028289299 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6875, "acc_stderr": 0.02815637344037142, "acc_norm": 0.6875, "acc_norm_stderr": 0.02815637344037142 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.684640522875817, "acc_stderr": 0.018798086284886887, "acc_norm": 0.684640522875817, "acc_norm_stderr": 0.018798086284886887 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.746938775510204, "acc_stderr": 0.027833023871399677, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.027833023871399677 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.026193923544454115, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.026193923544454115 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness|hendrycksTest-virology|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.847953216374269, "acc_stderr": 0.027539122889061456, "acc_norm": 0.847953216374269, "acc_norm_stderr": 0.027539122889061456 }, "harness|truthfulqa:mc|0": { "mc1": 0.5410036719706243, "mc1_stderr": 0.017444544447661203, "mc2": 0.7084705277313671, "mc2_stderr": 0.014694482049743158 }, "harness|winogrande|5": { "acc": 0.819258089976322, "acc_stderr": 0.010814911009613981 }, "harness|gsm8k|5": { "acc": 0.7005307050796058, "acc_stderr": 0.012616300735519665 } } ``` ## 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]

DeepFoldProtein/openfold_msa_contrastive_cards_000

--- dataset_info: features: - name: query_accession dtype: string - name: excludes sequence: string - name: query_sequence dtype: string - name: target_accessions sequence: string - name: target_sequences sequence: string splits: - name: train num_bytes: 1368566426 num_examples: 259752 download_size: 1342381431 dataset_size: 1368566426 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "openfold_msa_contrastive_cards_000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Lancelot53/srbd1_v2_annotated

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: xml dtype: string - name: html dtype: string - name: response dtype: string - name: annotated dtype: string splits: - name: train num_bytes: 29595348.121978022 num_examples: 1077 download_size: 3598400 dataset_size: 29595348.121978022 --- # Dataset Card for "srbd1_v2_annotated" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

gokuls/wiki_book_corpus_processed_bert_dataset_small

--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 5550400800.0 num_examples: 1541778 download_size: 1636779213 dataset_size: 5550400800.0 --- # Dataset Card for "wiki_book_corpus_processed_bert_dataset_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_1880

--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_dtd_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 852862 num_examples: 1880 - name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 1030510 num_examples: 1880 download_size: 477779 dataset_size: 1883372 --- # Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_A_ns_1880" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-eval-samsum-samsum-1bb2ba-1486554327

--- type: predictions tags: - autotrain - evaluation datasets: - samsum eval_info: task: summarization model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum metrics: [] dataset_name: samsum dataset_config: samsum dataset_split: validation col_mapping: text: dialogue target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum * Dataset: samsum * Config: samsum * Split: validation To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@samuelallen123](https://huggingface.co/samuelallen123) for evaluating this model.

Dahoas/unet-cifar10-32

--- dataset_info: features: - name: images sequence: sequence: sequence: uint8 splits: - name: train num_bytes: 7110656 num_examples: 2048 download_size: 6350172 dataset_size: 7110656 --- # Dataset Card for "unet-cifar10-32" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_xxyyy123__10k_v1_lora_qkvo_rank14_v3

--- pretty_name: Evaluation run of xxyyy123/10k_v1_lora_qkvo_rank14_v3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [xxyyy123/10k_v1_lora_qkvo_rank14_v3](https://huggingface.co/xxyyy123/10k_v1_lora_qkvo_rank14_v3)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 61 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 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_xxyyy123__10k_v1_lora_qkvo_rank14_v3\"\ ,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\ \nThese are the [latest results from run 2023-09-03T13:17:02.987872](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__10k_v1_lora_qkvo_rank14_v3/blob/main/results_2023-09-03T13%3A17%3A02.987872.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\": 0.5091352266849982,\n\ \ \"acc_stderr\": 0.03495474191892426,\n \"acc_norm\": 0.5128128131582483,\n\ \ \"acc_norm_stderr\": 0.03493935725866389,\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5344202990692574,\n\ \ \"mc2_stderr\": 0.015729161957393895\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5298634812286689,\n \"acc_stderr\": 0.014585305840007105,\n\ \ \"acc_norm\": 0.5597269624573379,\n \"acc_norm_stderr\": 0.01450676952480424\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6050587532364071,\n\ \ \"acc_stderr\": 0.004878390226591715,\n \"acc_norm\": 0.7921728739294961,\n\ \ \"acc_norm_stderr\": 0.00404923158643323\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.48148148148148145,\n\ \ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.48148148148148145,\n\ \ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04068942293855797,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04068942293855797\n },\n\ \ \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.49,\n \ \ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \ \ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5584905660377358,\n \"acc_stderr\": 0.030561590426731833,\n\ \ \"acc_norm\": 0.5584905660377358,\n \"acc_norm_stderr\": 0.030561590426731833\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5486111111111112,\n\ \ \"acc_stderr\": 0.041614023984032786,\n \"acc_norm\": 0.5486111111111112,\n\ \ \"acc_norm_stderr\": 0.041614023984032786\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.39,\n\ \ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.45664739884393063,\n\ \ \"acc_stderr\": 0.03798106566014498,\n \"acc_norm\": 0.45664739884393063,\n\ \ \"acc_norm_stderr\": 0.03798106566014498\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n\ \ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n\ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.46808510638297873,\n \"acc_stderr\": 0.03261936918467382,\n\ \ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.03261936918467382\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.34210526315789475,\n\ \ \"acc_stderr\": 0.04462917535336936,\n \"acc_norm\": 0.34210526315789475,\n\ \ \"acc_norm_stderr\": 0.04462917535336936\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\ \ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.29365079365079366,\n \"acc_stderr\": 0.023456037383982022,\n \"\ acc_norm\": 0.29365079365079366,\n \"acc_norm_stderr\": 0.023456037383982022\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2698412698412698,\n\ \ \"acc_stderr\": 0.03970158273235173,\n \"acc_norm\": 0.2698412698412698,\n\ \ \"acc_norm_stderr\": 0.03970158273235173\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5451612903225806,\n\ \ \"acc_stderr\": 0.028327743091561077,\n \"acc_norm\": 0.5451612903225806,\n\ \ \"acc_norm_stderr\": 0.028327743091561077\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.3793103448275862,\n \"acc_stderr\": 0.034139638059062345,\n\ \ \"acc_norm\": 0.3793103448275862,\n \"acc_norm_stderr\": 0.034139638059062345\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\"\ : 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885415,\n\ \ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885415\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.6363636363636364,\n \"acc_stderr\": 0.03427308652999934,\n \"\ acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.03427308652999934\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.7253886010362695,\n \"acc_stderr\": 0.03221024508041153,\n\ \ \"acc_norm\": 0.7253886010362695,\n \"acc_norm_stderr\": 0.03221024508041153\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.4948717948717949,\n \"acc_stderr\": 0.02534967290683866,\n \ \ \"acc_norm\": 0.4948717948717949,\n \"acc_norm_stderr\": 0.02534967290683866\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275805,\n \ \ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275805\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5126050420168067,\n \"acc_stderr\": 0.03246816765752174,\n \ \ \"acc_norm\": 0.5126050420168067,\n \"acc_norm_stderr\": 0.03246816765752174\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"\ acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7211009174311926,\n \"acc_stderr\": 0.0192274688764635,\n \"acc_norm\"\ : 0.7211009174311926,\n \"acc_norm_stderr\": 0.0192274688764635\n },\n\ \ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4166666666666667,\n\ \ \"acc_stderr\": 0.03362277436608043,\n \"acc_norm\": 0.4166666666666667,\n\ \ \"acc_norm_stderr\": 0.03362277436608043\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.696078431372549,\n \"acc_stderr\": 0.03228210387037892,\n\ \ \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.03228210387037892\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7341772151898734,\n \"acc_stderr\": 0.02875679962965834,\n \ \ \"acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.02875679962965834\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5515695067264574,\n\ \ \"acc_stderr\": 0.03337883736255098,\n \"acc_norm\": 0.5515695067264574,\n\ \ \"acc_norm_stderr\": 0.03337883736255098\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6030534351145038,\n \"acc_stderr\": 0.04291135671009224,\n\ \ \"acc_norm\": 0.6030534351145038,\n \"acc_norm_stderr\": 0.04291135671009224\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7024793388429752,\n \"acc_stderr\": 0.04173349148083499,\n \"\ acc_norm\": 0.7024793388429752,\n \"acc_norm_stderr\": 0.04173349148083499\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6388888888888888,\n\ \ \"acc_stderr\": 0.04643454608906275,\n \"acc_norm\": 0.6388888888888888,\n\ \ \"acc_norm_stderr\": 0.04643454608906275\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.5398773006134969,\n \"acc_stderr\": 0.03915857291436971,\n\ \ \"acc_norm\": 0.5398773006134969,\n \"acc_norm_stderr\": 0.03915857291436971\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3392857142857143,\n\ \ \"acc_stderr\": 0.04493949068613539,\n \"acc_norm\": 0.3392857142857143,\n\ \ \"acc_norm_stderr\": 0.04493949068613539\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\ \ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7649572649572649,\n\ \ \"acc_stderr\": 0.027778835904935434,\n \"acc_norm\": 0.7649572649572649,\n\ \ \"acc_norm_stderr\": 0.027778835904935434\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7037037037037037,\n\ \ \"acc_stderr\": 0.016328814422102052,\n \"acc_norm\": 0.7037037037037037,\n\ \ \"acc_norm_stderr\": 0.016328814422102052\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5664739884393064,\n \"acc_stderr\": 0.026680134761679214,\n\ \ \"acc_norm\": 0.5664739884393064,\n \"acc_norm_stderr\": 0.026680134761679214\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2547486033519553,\n\ \ \"acc_stderr\": 0.014572650383409155,\n \"acc_norm\": 0.2547486033519553,\n\ \ \"acc_norm_stderr\": 0.014572650383409155\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5359477124183006,\n \"acc_stderr\": 0.02855582751652878,\n\ \ \"acc_norm\": 0.5359477124183006,\n \"acc_norm_stderr\": 0.02855582751652878\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5819935691318328,\n\ \ \"acc_stderr\": 0.028013651891995072,\n \"acc_norm\": 0.5819935691318328,\n\ \ \"acc_norm_stderr\": 0.028013651891995072\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.5524691358024691,\n \"acc_stderr\": 0.027667138569422704,\n\ \ \"acc_norm\": 0.5524691358024691,\n \"acc_norm_stderr\": 0.027667138569422704\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.36879432624113473,\n \"acc_stderr\": 0.028782227561347243,\n \ \ \"acc_norm\": 0.36879432624113473,\n \"acc_norm_stderr\": 0.028782227561347243\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3820078226857888,\n\ \ \"acc_stderr\": 0.012409564470235567,\n \"acc_norm\": 0.3820078226857888,\n\ \ \"acc_norm_stderr\": 0.012409564470235567\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.48161764705882354,\n \"acc_stderr\": 0.030352303395351964,\n\ \ \"acc_norm\": 0.48161764705882354,\n \"acc_norm_stderr\": 0.030352303395351964\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.4820261437908497,\n \"acc_stderr\": 0.020214761037872404,\n \ \ \"acc_norm\": 0.4820261437908497,\n \"acc_norm_stderr\": 0.020214761037872404\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5818181818181818,\n\ \ \"acc_stderr\": 0.04724577405731572,\n \"acc_norm\": 0.5818181818181818,\n\ \ \"acc_norm_stderr\": 0.04724577405731572\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6040816326530613,\n \"acc_stderr\": 0.03130802899065686,\n\ \ \"acc_norm\": 0.6040816326530613,\n \"acc_norm_stderr\": 0.03130802899065686\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5373134328358209,\n\ \ \"acc_stderr\": 0.035256751674679745,\n \"acc_norm\": 0.5373134328358209,\n\ \ \"acc_norm_stderr\": 0.035256751674679745\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.42168674698795183,\n\ \ \"acc_stderr\": 0.03844453181770917,\n \"acc_norm\": 0.42168674698795183,\n\ \ \"acc_norm_stderr\": 0.03844453181770917\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.6783625730994152,\n \"acc_stderr\": 0.03582529442573122,\n\ \ \"acc_norm\": 0.6783625730994152,\n \"acc_norm_stderr\": 0.03582529442573122\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5344202990692574,\n\ \ \"mc2_stderr\": 0.015729161957393895\n }\n}\n```" repo_url: https://huggingface.co/xxyyy123/10k_v1_lora_qkvo_rank14_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_03T13_17_02.987872 path: - '**/details_harness|arc:challenge|25_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hellaswag|10_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-management|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-09-03T13:17:02.987872.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-management|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-virology|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T13:17:02.987872.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_09_03T13_17_02.987872 path: - '**/details_harness|truthfulqa:mc|0_2023-09-03T13:17:02.987872.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-09-03T13:17:02.987872.parquet' - config_name: results data_files: - split: 2023_09_03T13_17_02.987872 path: - results_2023-09-03T13:17:02.987872.parquet - split: latest path: - results_2023-09-03T13:17:02.987872.parquet --- # Dataset Card for Evaluation run of xxyyy123/10k_v1_lora_qkvo_rank14_v3 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/xxyyy123/10k_v1_lora_qkvo_rank14_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 [xxyyy123/10k_v1_lora_qkvo_rank14_v3](https://huggingface.co/xxyyy123/10k_v1_lora_qkvo_rank14_v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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 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_xxyyy123__10k_v1_lora_qkvo_rank14_v3", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-03T13:17:02.987872](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__10k_v1_lora_qkvo_rank14_v3/blob/main/results_2023-09-03T13%3A17%3A02.987872.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": 0.5091352266849982, "acc_stderr": 0.03495474191892426, "acc_norm": 0.5128128131582483, "acc_norm_stderr": 0.03493935725866389, "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5344202990692574, "mc2_stderr": 0.015729161957393895 }, "harness|arc:challenge|25": { "acc": 0.5298634812286689, "acc_stderr": 0.014585305840007105, "acc_norm": 0.5597269624573379, "acc_norm_stderr": 0.01450676952480424 }, "harness|hellaswag|10": { "acc": 0.6050587532364071, "acc_stderr": 0.004878390226591715, "acc_norm": 0.7921728739294961, "acc_norm_stderr": 0.00404923158643323 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.48148148148148145, "acc_stderr": 0.043163785995113245, "acc_norm": 0.48148148148148145, "acc_norm_stderr": 0.043163785995113245 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5, "acc_stderr": 0.04068942293855797, "acc_norm": 0.5, "acc_norm_stderr": 0.04068942293855797 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5584905660377358, "acc_stderr": 0.030561590426731833, "acc_norm": 0.5584905660377358, "acc_norm_stderr": 0.030561590426731833 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.5486111111111112, "acc_stderr": 0.041614023984032786, "acc_norm": 0.5486111111111112, "acc_norm_stderr": 0.041614023984032786 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.45664739884393063, "acc_stderr": 0.03798106566014498, "acc_norm": 0.45664739884393063, "acc_norm_stderr": 0.03798106566014498 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2647058823529412, "acc_stderr": 0.04389869956808778, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.04389869956808778 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.46808510638297873, "acc_stderr": 0.03261936918467382, "acc_norm": 0.46808510638297873, "acc_norm_stderr": 0.03261936918467382 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.34210526315789475, "acc_stderr": 0.04462917535336936, "acc_norm": 0.34210526315789475, "acc_norm_stderr": 0.04462917535336936 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.46206896551724136, "acc_stderr": 0.041546596717075474, "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.29365079365079366, "acc_stderr": 0.023456037383982022, "acc_norm": 0.29365079365079366, "acc_norm_stderr": 0.023456037383982022 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2698412698412698, "acc_stderr": 0.03970158273235173, "acc_norm": 0.2698412698412698, "acc_norm_stderr": 0.03970158273235173 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5451612903225806, "acc_stderr": 0.028327743091561077, "acc_norm": 0.5451612903225806, "acc_norm_stderr": 0.028327743091561077 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3793103448275862, "acc_stderr": 0.034139638059062345, "acc_norm": 0.3793103448275862, "acc_norm_stderr": 0.034139638059062345 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7333333333333333, "acc_stderr": 0.03453131801885415, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.03453131801885415 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6363636363636364, "acc_stderr": 0.03427308652999934, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.03427308652999934 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.7253886010362695, "acc_stderr": 0.03221024508041153, "acc_norm": 0.7253886010362695, "acc_norm_stderr": 0.03221024508041153 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.4948717948717949, "acc_stderr": 0.02534967290683866, "acc_norm": 0.4948717948717949, "acc_norm_stderr": 0.02534967290683866 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24074074074074073, "acc_stderr": 0.026067159222275805, "acc_norm": 0.24074074074074073, "acc_norm_stderr": 0.026067159222275805 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5126050420168067, "acc_stderr": 0.03246816765752174, "acc_norm": 0.5126050420168067, "acc_norm_stderr": 0.03246816765752174 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31125827814569534, "acc_stderr": 0.03780445850526733, "acc_norm": 0.31125827814569534, "acc_norm_stderr": 0.03780445850526733 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7211009174311926, "acc_stderr": 0.0192274688764635, "acc_norm": 0.7211009174311926, "acc_norm_stderr": 0.0192274688764635 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4166666666666667, "acc_stderr": 0.03362277436608043, "acc_norm": 0.4166666666666667, "acc_norm_stderr": 0.03362277436608043 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.696078431372549, "acc_stderr": 0.03228210387037892, "acc_norm": 0.696078431372549, "acc_norm_stderr": 0.03228210387037892 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7341772151898734, "acc_stderr": 0.02875679962965834, "acc_norm": 0.7341772151898734, "acc_norm_stderr": 0.02875679962965834 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.5515695067264574, "acc_stderr": 0.03337883736255098, "acc_norm": 0.5515695067264574, "acc_norm_stderr": 0.03337883736255098 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6030534351145038, "acc_stderr": 0.04291135671009224, "acc_norm": 0.6030534351145038, "acc_norm_stderr": 0.04291135671009224 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7024793388429752, "acc_stderr": 0.04173349148083499, "acc_norm": 0.7024793388429752, "acc_norm_stderr": 0.04173349148083499 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6388888888888888, "acc_stderr": 0.04643454608906275, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.04643454608906275 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.5398773006134969, "acc_stderr": 0.03915857291436971, "acc_norm": 0.5398773006134969, "acc_norm_stderr": 0.03915857291436971 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.3392857142857143, "acc_stderr": 0.04493949068613539, "acc_norm": 0.3392857142857143, "acc_norm_stderr": 0.04493949068613539 }, "harness|hendrycksTest-management|5": { "acc": 0.7281553398058253, "acc_stderr": 0.044052680241409216, "acc_norm": 0.7281553398058253, "acc_norm_stderr": 0.044052680241409216 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7649572649572649, "acc_stderr": 0.027778835904935434, "acc_norm": 0.7649572649572649, "acc_norm_stderr": 0.027778835904935434 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7037037037037037, "acc_stderr": 0.016328814422102052, "acc_norm": 0.7037037037037037, "acc_norm_stderr": 0.016328814422102052 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5664739884393064, "acc_stderr": 0.026680134761679214, "acc_norm": 0.5664739884393064, "acc_norm_stderr": 0.026680134761679214 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2547486033519553, "acc_stderr": 0.014572650383409155, "acc_norm": 0.2547486033519553, "acc_norm_stderr": 0.014572650383409155 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5359477124183006, "acc_stderr": 0.02855582751652878, "acc_norm": 0.5359477124183006, "acc_norm_stderr": 0.02855582751652878 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5819935691318328, "acc_stderr": 0.028013651891995072, "acc_norm": 0.5819935691318328, "acc_norm_stderr": 0.028013651891995072 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.5524691358024691, "acc_stderr": 0.027667138569422704, "acc_norm": 0.5524691358024691, "acc_norm_stderr": 0.027667138569422704 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.36879432624113473, "acc_stderr": 0.028782227561347243, "acc_norm": 0.36879432624113473, "acc_norm_stderr": 0.028782227561347243 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3820078226857888, "acc_stderr": 0.012409564470235567, "acc_norm": 0.3820078226857888, "acc_norm_stderr": 0.012409564470235567 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.48161764705882354, "acc_stderr": 0.030352303395351964, "acc_norm": 0.48161764705882354, "acc_norm_stderr": 0.030352303395351964 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.4820261437908497, "acc_stderr": 0.020214761037872404, "acc_norm": 0.4820261437908497, "acc_norm_stderr": 0.020214761037872404 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5818181818181818, "acc_stderr": 0.04724577405731572, "acc_norm": 0.5818181818181818, "acc_norm_stderr": 0.04724577405731572 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6040816326530613, "acc_stderr": 0.03130802899065686, "acc_norm": 0.6040816326530613, "acc_norm_stderr": 0.03130802899065686 }, "harness|hendrycksTest-sociology|5": { "acc": 0.5373134328358209, "acc_stderr": 0.035256751674679745, "acc_norm": 0.5373134328358209, "acc_norm_stderr": 0.035256751674679745 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-virology|5": { "acc": 0.42168674698795183, "acc_stderr": 0.03844453181770917, "acc_norm": 0.42168674698795183, "acc_norm_stderr": 0.03844453181770917 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.6783625730994152, "acc_stderr": 0.03582529442573122, "acc_norm": 0.6783625730994152, "acc_norm_stderr": 0.03582529442573122 }, "harness|truthfulqa:mc|0": { "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5344202990692574, "mc2_stderr": 0.015729161957393895 } } ``` ### 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]

passionMan/usda_tokenized_target

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 541970 num_examples: 2527 - name: test num_bytes: 180736 num_examples: 843 download_size: 136249 dataset_size: 722706 --- # Dataset Card for "usda_tokenized_target" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

RUCAIBox/Erya-dataset

--- license: apache-2.0 task_categories: - translation - text-generation --- **monolingual.tgz** contains a vast collection of Ancient Chinese sentences. **trans.tgz** combines vairous types of parallel corpora, with Ancient Chinese sentences aligned with Modern Chinese sentences. **finetune.tgz** selects certain classical books as benchmarks, with Ancient Chinese sentences aligned with Modern Chinese sentences. The data in **trans.tgz** and **finetune.tgz** does not overlap. More information can be found here [RUCAIBox/Erya (github.com)](https://github.com/RUCAIBox/Erya).

semiotic/spider_dataset_tuning

--- dataset_info: features: - name: type dtype: string - name: question dtype: string - name: query dtype: string - name: db_id dtype: string - name: schema dtype: string splits: - name: train num_bytes: 125169641 num_examples: 97317 - name: val num_bytes: 10757137 num_examples: 7909 - name: test num_bytes: 1384246 num_examples: 1292 download_size: 7245840 dataset_size: 137311024 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---

liuyanchen1015/MULTI_VALUE_wnli_you_ye

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 1605 num_examples: 7 - name: train num_bytes: 849 num_examples: 6 download_size: 6423 dataset_size: 2454 --- # Dataset Card for "MULTI_VALUE_wnli_you_ye" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kanishka/counterfactual_training_test

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 79390 num_examples: 1000 - name: validation num_bytes: 56120230 num_examples: 1026747 download_size: 0 dataset_size: 56199620 --- # Dataset Card for "counterfactual_training_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dahwinsingularity/dahyun-v-l-0

--- license: apache-2.0 ---

intfloat/query2doc_msmarco

--- license: cc-by-4.0 language: - en size_categories: - 100K<n<1M --- ### Dataset Summary This dataset contains GPT-3.5 (`text-davinci-003`) generations from MS-MARCO queries. [Query2doc: Query Expansion with Large Language Models](https://arxiv.org/pdf/2303.07678.pdf) Liang Wang, Nan Yang and Furu Wei ### Data Instances An example looks as follows. ``` { "query_id": "1030303", "query": "who is aziz hashim", "pseudo_doc": "Aziz Hashim is a renowned entrepreneur, business leader, and one of the most successful restaurant franchise operators in the US. He is the founder of NRD Capital, a private equity firm focused on investments in multi-unit restaurant franchised businesses. Hashim has built a formidable track record of success in the franchise industry, with brands such as Outback Steakhouse and Jamba Juice. His accomplishments and philanthropic initiatives have earned him numerous awards, including the prestigious Ernst and Young Entrepreneur of the Year award." } ``` ### Data Fields - `query_id`: a `string` feature. - `query`: a `string` feature. - `pseudo_doc`: a `string` feature. ### Data Splits | train | dev | test | trec_dl2019 | trec_dl2020 | |--------|------:|------:|------:|------:| | 502939 | 6980 | 6837 | 43 | 54 | ### How to use this dataset ```python from datasets import load_dataset dataset = load_dataset('intfloat/query2doc_msmarco') print(dataset['trec_dl2019'][0]) ``` ### Reproducing our results We provide a python script [repro_bm25.py](https://huggingface.co/datasets/intfloat/query2doc_msmarco/blob/main/repro_bm25.py) to reproduce our results with BM25 retrieval. First install some python dependency packages: ``` pip install pyserini==0.15.0 pytrec_eval datasets tqdm ``` Then download and run the python code: ``` python repro_bm25.py ``` This script utilizes the pre-built Lucene index from [Pyserini](https://github.com/castorini/pyserini/blob/pyserini-0.15.0/docs/prebuilt-indexes.md) and might yield slightly different results compared to the paper. ### Citation Information ``` @article{wang2023query2doc, title={Query2doc: Query Expansion with Large Language Models}, author={Wang, Liang and Yang, Nan and Wei, Furu}, journal={arXiv preprint arXiv:2303.07678}, year={2023} } ```

mespinosami/map2sat-central-belt-clarity-old-map20-samples

--- dataset_info: features: - name: input_image dtype: image - name: edit_prompt dtype: string - name: edited_image dtype: image splits: - name: train num_bytes: 857306.8 num_examples: 16 - name: test num_bytes: 201058.2 num_examples: 4 download_size: 1061836 dataset_size: 1058365.0 --- # Dataset Card for "map2sat-central-belt-clarity-old-map20-samples" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ricahrd/McKevin

--- license: openrail ---

harpreetsahota/Instruction-Following-Evaluation-for-Large-Language-Models

--- dataset_info: features: - name: key dtype: int64 - name: prompt dtype: string - name: instruction_id_list sequence: string - name: kwargs dtype: string splits: - name: train num_bytes: 181824 num_examples: 541 download_size: 80840 dataset_size: 181824 configs: - config_name: default data_files: - split: train path: data/train-* --- # Instruction-Following Evaluation Dataset ## 📜 Overview This dataset, specifically designed for the **evaluation of large language models in instruction-following tasks**, is directly inspired by the methodologies and experiments described in the paper titled _"Instruction-Following Evaluation for Large Language Models"_. The dataset's creation and availability on HuggingFace are aimed at enhancing research and application in the field of natural language understanding, particularly in the context of instruction interpretation and execution by AI models. ## 🌐 Source The dataset draws its structure and content from the insights provided in: - **Original Research Paper**: [_"Instruction-Following Evaluation for Large Language Models"_](https://arxiv.org/abs/2311.07911) - **Original Data Repository**: [Google Research on GitHub](https://github.com/google-research/google-research/tree/master/instruction_following_eval) ## 📊 Dataset Structure Comprising primarily of **'prompts'**, this dataset is tailored to challenge and assess language models on various facets of understanding and executing instructions. Each prompt represents a unique scenario or task, simulating real-world applications where accurate interpretation of instructions is crucial. ## 💡 Usage Targeted for use within the **HuggingFace ecosystem**, this dataset serves as a pivotal tool for researchers and developers focusing on the advancement of language models. It stands as a benchmark for: - 📈 Evaluating model performance in instruction-following tasks. - 🔍 Identifying model capabilities and areas of improvement. - 🤖 Enhancing AI's understanding of complex, human-like commands. ## 🙏 Acknowledgements This dataset is a tribute to the foundational work presented in the original paper and is intended for academic and research purposes. It reflects a commitment to furthering the understanding of AI's interaction with human language, particularly in processing and responding to diverse and complex instructions.

open-llm-leaderboard/details_TFLai__gpt2-turkish-uncased

--- pretty_name: Evaluation run of TFLai/gpt2-turkish-uncased dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TFLai/gpt2-turkish-uncased](https://huggingface.co/TFLai/gpt2-turkish-uncased)\ \ 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 3 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_TFLai__gpt2-turkish-uncased\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-02T15:29:40.186292](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__gpt2-turkish-uncased/blob/main/results_2023-12-02T15-29-40.186292.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\": 0.0,\n \"\ acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \ \ \"acc_stderr\": 0.0\n }\n}\n```" repo_url: https://huggingface.co/TFLai/gpt2-turkish-uncased 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_07_24T09_48_46.264649 path: - '**/details_harness|arc:challenge|25_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-24T09:48:46.264649.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_22T01_34_05.823968 path: - '**/details_harness|drop|3_2023-10-22T01-34-05.823968.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-22T01-34-05.823968.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_22T01_34_05.823968 path: - '**/details_harness|gsm8k|5_2023-10-22T01-34-05.823968.parquet' - split: 2023_12_02T15_29_40.186292 path: - '**/details_harness|gsm8k|5_2023-12-02T15-29-40.186292.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-02T15-29-40.186292.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hellaswag|10_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-24T09:48:46.264649.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-management|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-24T09:48:46.264649.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_24T09_48_46.264649 path: - '**/details_harness|truthfulqa:mc|0_2023-07-24T09:48:46.264649.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-24T09:48:46.264649.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_22T01_34_05.823968 path: - '**/details_harness|winogrande|5_2023-10-22T01-34-05.823968.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-22T01-34-05.823968.parquet' - config_name: results data_files: - split: 2023_07_24T09_48_46.264649 path: - results_2023-07-24T09:48:46.264649.parquet - split: 2023_10_22T01_34_05.823968 path: - results_2023-10-22T01-34-05.823968.parquet - split: 2023_12_02T15_29_40.186292 path: - results_2023-12-02T15-29-40.186292.parquet - split: latest path: - results_2023-12-02T15-29-40.186292.parquet --- # Dataset Card for Evaluation run of TFLai/gpt2-turkish-uncased ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TFLai/gpt2-turkish-uncased - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [TFLai/gpt2-turkish-uncased](https://huggingface.co/TFLai/gpt2-turkish-uncased) 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 3 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_TFLai__gpt2-turkish-uncased", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-02T15:29:40.186292](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__gpt2-turkish-uncased/blob/main/results_2023-12-02T15-29-40.186292.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": 0.0, "acc_stderr": 0.0 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ### 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]

AdapterOcean/python3-standardized_cluster_16_std

--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 3711623 num_examples: 3834 download_size: 762202 dataset_size: 3711623 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "python3-standardized_cluster_16_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ibupari/mini-learnilatypus

--- dataset_info: features: - name: title dtype: string - name: subtitle dtype: string - name: paragraph dtype: string - name: sentences dtype: string splits: - name: train num_bytes: 1324745 num_examples: 1000 download_size: 264663 dataset_size: 1324745 configs: - config_name: default data_files: - split: train path: data/train-* ---

humane-lab/K-HATERS

--- license: cc-by-4.0 language: - ko pretty_name: K-Haters tags: - hate speech detection ---

open-llm-leaderboard/details_Sharathhebbar24__ssh_1.8B

--- pretty_name: Evaluation run of Sharathhebbar24/ssh_1.8B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Sharathhebbar24/ssh_1.8B](https://huggingface.co/Sharathhebbar24/ssh_1.8B) 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_Sharathhebbar24__ssh_1.8B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-03T16:03:37.862164](https://huggingface.co/datasets/open-llm-leaderboard/details_Sharathhebbar24__ssh_1.8B/blob/main/results_2024-02-03T16-03-37.862164.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\": 0.4400975999737303,\n\ \ \"acc_stderr\": 0.0345967614345703,\n \"acc_norm\": 0.4431186866947614,\n\ \ \"acc_norm_stderr\": 0.03532660922667111,\n \"mc1\": 0.2668298653610771,\n\ \ \"mc1_stderr\": 0.015483691939237265,\n \"mc2\": 0.4314996062576424,\n\ \ \"mc2_stderr\": 0.015306262833109105\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.3728668941979522,\n \"acc_stderr\": 0.014131176760131165,\n\ \ \"acc_norm\": 0.39078498293515357,\n \"acc_norm_stderr\": 0.014258563880513778\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.47560246962756425,\n\ \ \"acc_stderr\": 0.004983837641502896,\n \"acc_norm\": 0.6236805417247561,\n\ \ \"acc_norm_stderr\": 0.00483471581420811\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.37777777777777777,\n\ \ \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.37777777777777777,\n\ \ \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.4605263157894737,\n \"acc_stderr\": 0.04056242252249033,\n\ \ \"acc_norm\": 0.4605263157894737,\n \"acc_norm_stderr\": 0.04056242252249033\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n\ \ \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5056603773584906,\n \"acc_stderr\": 0.030770900763851316,\n\ \ \"acc_norm\": 0.5056603773584906,\n \"acc_norm_stderr\": 0.030770900763851316\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4166666666666667,\n\ \ \"acc_stderr\": 0.041227287076512825,\n \"acc_norm\": 0.4166666666666667,\n\ \ \"acc_norm_stderr\": 0.041227287076512825\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\"\ : 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.044084400227680794,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.044084400227680794\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3872832369942196,\n\ \ \"acc_stderr\": 0.037143259063020656,\n \"acc_norm\": 0.3872832369942196,\n\ \ \"acc_norm_stderr\": 0.037143259063020656\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.040233822736177476,\n\ \ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.040233822736177476\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.032555253593403555,\n\ \ \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.032555253593403555\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.39473684210526316,\n\ \ \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.39473684210526316,\n\ \ \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.4206896551724138,\n \"acc_stderr\": 0.0411391498118926,\n\ \ \"acc_norm\": 0.4206896551724138,\n \"acc_norm_stderr\": 0.0411391498118926\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.30952380952380953,\n \"acc_stderr\": 0.023809523809523867,\n \"\ acc_norm\": 0.30952380952380953,\n \"acc_norm_stderr\": 0.023809523809523867\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\ \ \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n\ \ \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.028444006199428714,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.028444006199428714\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.33004926108374383,\n \"acc_stderr\": 0.033085304262282574,\n\ \ \"acc_norm\": 0.33004926108374383,\n \"acc_norm_stderr\": 0.033085304262282574\n\ \ },\n \"harness|hendrycksTest-high_school_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-high_school_european_history|5\"\ : {\n \"acc\": 0.5878787878787879,\n \"acc_stderr\": 0.03843566993588717,\n\ \ \"acc_norm\": 0.5878787878787879,\n \"acc_norm_stderr\": 0.03843566993588717\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.035623524993954825,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.035623524993954825\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\"\ : {\n \"acc\": 0.6113989637305699,\n \"acc_stderr\": 0.035177397963731316,\n\ \ \"acc_norm\": 0.6113989637305699,\n \"acc_norm_stderr\": 0.035177397963731316\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.40512820512820513,\n \"acc_stderr\": 0.024890471769938145,\n\ \ \"acc_norm\": 0.40512820512820513,\n \"acc_norm_stderr\": 0.024890471769938145\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3148148148148148,\n \"acc_stderr\": 0.028317533496066482,\n \ \ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.028317533496066482\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.46638655462184875,\n \"acc_stderr\": 0.03240501447690071,\n\ \ \"acc_norm\": 0.46638655462184875,\n \"acc_norm_stderr\": 0.03240501447690071\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389024,\n \"\ acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389024\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.5467889908256881,\n \"acc_stderr\": 0.021343255165546037,\n \"\ acc_norm\": 0.5467889908256881,\n \"acc_norm_stderr\": 0.021343255165546037\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.35648148148148145,\n \"acc_stderr\": 0.032664783315272714,\n \"\ acc_norm\": 0.35648148148148145,\n \"acc_norm_stderr\": 0.032664783315272714\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.49019607843137253,\n \"acc_stderr\": 0.03508637358630572,\n \"\ acc_norm\": 0.49019607843137253,\n \"acc_norm_stderr\": 0.03508637358630572\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.5316455696202531,\n \"acc_stderr\": 0.032481974005110756,\n \ \ \"acc_norm\": 0.5316455696202531,\n \"acc_norm_stderr\": 0.032481974005110756\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.4977578475336323,\n\ \ \"acc_stderr\": 0.033557465352232634,\n \"acc_norm\": 0.4977578475336323,\n\ \ \"acc_norm_stderr\": 0.033557465352232634\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.4732824427480916,\n \"acc_stderr\": 0.04379024936553893,\n\ \ \"acc_norm\": 0.4732824427480916,\n \"acc_norm_stderr\": 0.04379024936553893\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.628099173553719,\n \"acc_stderr\": 0.044120158066245044,\n \"\ acc_norm\": 0.628099173553719,\n \"acc_norm_stderr\": 0.044120158066245044\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5648148148148148,\n\ \ \"acc_stderr\": 0.04792898170907062,\n \"acc_norm\": 0.5648148148148148,\n\ \ \"acc_norm_stderr\": 0.04792898170907062\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.4723926380368098,\n \"acc_stderr\": 0.039223782906109894,\n\ \ \"acc_norm\": 0.4723926380368098,\n \"acc_norm_stderr\": 0.039223782906109894\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.32142857142857145,\n\ \ \"acc_stderr\": 0.044328040552915185,\n \"acc_norm\": 0.32142857142857145,\n\ \ \"acc_norm_stderr\": 0.044328040552915185\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6601941747572816,\n \"acc_stderr\": 0.046897659372781335,\n\ \ \"acc_norm\": 0.6601941747572816,\n \"acc_norm_stderr\": 0.046897659372781335\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6581196581196581,\n\ \ \"acc_stderr\": 0.03107502852650775,\n \"acc_norm\": 0.6581196581196581,\n\ \ \"acc_norm_stderr\": 0.03107502852650775\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.5644955300127714,\n\ \ \"acc_stderr\": 0.017730589927926588,\n \"acc_norm\": 0.5644955300127714,\n\ \ \"acc_norm_stderr\": 0.017730589927926588\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5115606936416185,\n \"acc_stderr\": 0.026911898686377927,\n\ \ \"acc_norm\": 0.5115606936416185,\n \"acc_norm_stderr\": 0.026911898686377927\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2435754189944134,\n\ \ \"acc_stderr\": 0.014355911964767857,\n \"acc_norm\": 0.2435754189944134,\n\ \ \"acc_norm_stderr\": 0.014355911964767857\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5359477124183006,\n \"acc_stderr\": 0.028555827516528787,\n\ \ \"acc_norm\": 0.5359477124183006,\n \"acc_norm_stderr\": 0.028555827516528787\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.45016077170418006,\n\ \ \"acc_stderr\": 0.028256660723360184,\n \"acc_norm\": 0.45016077170418006,\n\ \ \"acc_norm_stderr\": 0.028256660723360184\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.027744313443376536,\n\ \ \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.027744313443376536\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3404255319148936,\n \"acc_stderr\": 0.028267657482650144,\n \ \ \"acc_norm\": 0.3404255319148936,\n \"acc_norm_stderr\": 0.028267657482650144\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.34485006518904826,\n\ \ \"acc_stderr\": 0.012139881006287058,\n \"acc_norm\": 0.34485006518904826,\n\ \ \"acc_norm_stderr\": 0.012139881006287058\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.3786764705882353,\n \"acc_stderr\": 0.029465133639776132,\n\ \ \"acc_norm\": 0.3786764705882353,\n \"acc_norm_stderr\": 0.029465133639776132\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.4084967320261438,\n \"acc_stderr\": 0.01988622103750188,\n \ \ \"acc_norm\": 0.4084967320261438,\n \"acc_norm_stderr\": 0.01988622103750188\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5363636363636364,\n\ \ \"acc_stderr\": 0.04776449162396197,\n \"acc_norm\": 0.5363636363636364,\n\ \ \"acc_norm_stderr\": 0.04776449162396197\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.5183673469387755,\n \"acc_stderr\": 0.03198761546763127,\n\ \ \"acc_norm\": 0.5183673469387755,\n \"acc_norm_stderr\": 0.03198761546763127\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.3781094527363184,\n\ \ \"acc_stderr\": 0.03428867848778658,\n \"acc_norm\": 0.3781094527363184,\n\ \ \"acc_norm_stderr\": 0.03428867848778658\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3795180722891566,\n\ \ \"acc_stderr\": 0.037777988227480165,\n \"acc_norm\": 0.3795180722891566,\n\ \ \"acc_norm_stderr\": 0.037777988227480165\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.5146198830409356,\n \"acc_stderr\": 0.038331852752130254,\n\ \ \"acc_norm\": 0.5146198830409356,\n \"acc_norm_stderr\": 0.038331852752130254\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2668298653610771,\n\ \ \"mc1_stderr\": 0.015483691939237265,\n \"mc2\": 0.4314996062576424,\n\ \ \"mc2_stderr\": 0.015306262833109105\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5927387529597474,\n \"acc_stderr\": 0.013808654122417848\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.27520849128127367,\n \ \ \"acc_stderr\": 0.012302114305862647\n }\n}\n```" repo_url: https://huggingface.co/Sharathhebbar24/ssh_1.8B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|arc:challenge|25_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-03T16-03-37.862164.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|gsm8k|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hellaswag|10_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-03T16-03-37.862164.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-management|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-03T16-03-37.862164.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|truthfulqa:mc|0_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-03T16-03-37.862164.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_03T16_03_37.862164 path: - '**/details_harness|winogrande|5_2024-02-03T16-03-37.862164.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-03T16-03-37.862164.parquet' - config_name: results data_files: - split: 2024_02_03T16_03_37.862164 path: - results_2024-02-03T16-03-37.862164.parquet - split: latest path: - results_2024-02-03T16-03-37.862164.parquet --- # Dataset Card for Evaluation run of Sharathhebbar24/ssh_1.8B  Dataset automatically created during the evaluation run of model [Sharathhebbar24/ssh_1.8B](https://huggingface.co/Sharathhebbar24/ssh_1.8B) 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_Sharathhebbar24__ssh_1.8B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-03T16:03:37.862164](https://huggingface.co/datasets/open-llm-leaderboard/details_Sharathhebbar24__ssh_1.8B/blob/main/results_2024-02-03T16-03-37.862164.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": 0.4400975999737303, "acc_stderr": 0.0345967614345703, "acc_norm": 0.4431186866947614, "acc_norm_stderr": 0.03532660922667111, "mc1": 0.2668298653610771, "mc1_stderr": 0.015483691939237265, "mc2": 0.4314996062576424, "mc2_stderr": 0.015306262833109105 }, "harness|arc:challenge|25": { "acc": 0.3728668941979522, "acc_stderr": 0.014131176760131165, "acc_norm": 0.39078498293515357, "acc_norm_stderr": 0.014258563880513778 }, "harness|hellaswag|10": { "acc": 0.47560246962756425, "acc_stderr": 0.004983837641502896, "acc_norm": 0.6236805417247561, "acc_norm_stderr": 0.00483471581420811 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.37777777777777777, "acc_stderr": 0.04188307537595853, "acc_norm": 0.37777777777777777, "acc_norm_stderr": 0.04188307537595853 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.4605263157894737, "acc_stderr": 0.04056242252249033, "acc_norm": 0.4605263157894737, "acc_norm_stderr": 0.04056242252249033 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5056603773584906, "acc_stderr": 0.030770900763851316, "acc_norm": 0.5056603773584906, "acc_norm_stderr": 0.030770900763851316 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4166666666666667, "acc_stderr": 0.041227287076512825, "acc_norm": 0.4166666666666667, "acc_norm_stderr": 0.041227287076512825 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.26, "acc_stderr": 0.044084400227680794, "acc_norm": 0.26, "acc_norm_stderr": 0.044084400227680794 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.3872832369942196, "acc_stderr": 0.037143259063020656, "acc_norm": 0.3872832369942196, "acc_norm_stderr": 0.037143259063020656 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.20588235294117646, "acc_stderr": 0.040233822736177476, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.040233822736177476 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4553191489361702, "acc_stderr": 0.032555253593403555, "acc_norm": 0.4553191489361702, "acc_norm_stderr": 0.032555253593403555 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.39473684210526316, "acc_stderr": 0.045981880578165414, "acc_norm": 0.39473684210526316, "acc_norm_stderr": 0.045981880578165414 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4206896551724138, "acc_stderr": 0.0411391498118926, "acc_norm": 0.4206896551724138, "acc_norm_stderr": 0.0411391498118926 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.30952380952380953, "acc_stderr": 0.023809523809523867, "acc_norm": 0.30952380952380953, "acc_norm_stderr": 0.023809523809523867 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2857142857142857, "acc_stderr": 0.04040610178208841, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.04040610178208841 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5, "acc_stderr": 0.028444006199428714, "acc_norm": 0.5, "acc_norm_stderr": 0.028444006199428714 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.33004926108374383, "acc_stderr": 0.033085304262282574, "acc_norm": 0.33004926108374383, "acc_norm_stderr": 0.033085304262282574 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5878787878787879, "acc_stderr": 0.03843566993588717, "acc_norm": 0.5878787878787879, "acc_norm_stderr": 0.03843566993588717 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5, "acc_stderr": 0.035623524993954825, "acc_norm": 0.5, "acc_norm_stderr": 0.035623524993954825 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6113989637305699, "acc_stderr": 0.035177397963731316, "acc_norm": 0.6113989637305699, "acc_norm_stderr": 0.035177397963731316 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.40512820512820513, "acc_stderr": 0.024890471769938145, "acc_norm": 0.40512820512820513, "acc_norm_stderr": 0.024890471769938145 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3148148148148148, "acc_stderr": 0.028317533496066482, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.028317533496066482 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.46638655462184875, "acc_stderr": 0.03240501447690071, "acc_norm": 0.46638655462184875, "acc_norm_stderr": 0.03240501447690071 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2847682119205298, "acc_stderr": 0.03684881521389024, "acc_norm": 0.2847682119205298, "acc_norm_stderr": 0.03684881521389024 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.5467889908256881, "acc_stderr": 0.021343255165546037, "acc_norm": 0.5467889908256881, "acc_norm_stderr": 0.021343255165546037 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.35648148148148145, "acc_stderr": 0.032664783315272714, "acc_norm": 0.35648148148148145, "acc_norm_stderr": 0.032664783315272714 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.49019607843137253, "acc_stderr": 0.03508637358630572, "acc_norm": 0.49019607843137253, "acc_norm_stderr": 0.03508637358630572 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.5316455696202531, "acc_stderr": 0.032481974005110756, "acc_norm": 0.5316455696202531, "acc_norm_stderr": 0.032481974005110756 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.4977578475336323, "acc_stderr": 0.033557465352232634, "acc_norm": 0.4977578475336323, "acc_norm_stderr": 0.033557465352232634 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.4732824427480916, "acc_stderr": 0.04379024936553893, "acc_norm": 0.4732824427480916, "acc_norm_stderr": 0.04379024936553893 }, "harness|hendrycksTest-international_law|5": { "acc": 0.628099173553719, "acc_stderr": 0.044120158066245044, "acc_norm": 0.628099173553719, "acc_norm_stderr": 0.044120158066245044 }, 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"harness|truthfulqa:mc|0": { "mc1": 0.2668298653610771, "mc1_stderr": 0.015483691939237265, "mc2": 0.4314996062576424, "mc2_stderr": 0.015306262833109105 }, "harness|winogrande|5": { "acc": 0.5927387529597474, "acc_stderr": 0.013808654122417848 }, "harness|gsm8k|5": { "acc": 0.27520849128127367, "acc_stderr": 0.012302114305862647 } } ``` ## 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]

FUBUKIBG/rosepronto

--- license: openrail ---

heliosprime/twitter_dataset_1713204665

--- 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: 12837 num_examples: 36 download_size: 14403 dataset_size: 12837 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713204665" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo2_100_kl_0.1_prm_160m_thr_0.3_seed_3

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: index dtype: int64 - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43586042 num_examples: 18929 - name: epoch_1 num_bytes: 44122478 num_examples: 18929 - name: epoch_2 num_bytes: 44219397 num_examples: 18929 - name: epoch_3 num_bytes: 44261340 num_examples: 18929 - name: epoch_4 num_bytes: 44282722 num_examples: 18929 - name: epoch_5 num_bytes: 44300108 num_examples: 18929 - name: epoch_6 num_bytes: 44310569 num_examples: 18929 - name: epoch_7 num_bytes: 44316296 num_examples: 18929 - name: epoch_8 num_bytes: 44323129 num_examples: 18929 - name: epoch_9 num_bytes: 44326498 num_examples: 18929 - name: epoch_10 num_bytes: 44326077 num_examples: 18929 - name: epoch_11 num_bytes: 44327725 num_examples: 18929 - name: epoch_12 num_bytes: 44328350 num_examples: 18929 - name: epoch_13 num_bytes: 44330594 num_examples: 18929 - name: epoch_14 num_bytes: 44330360 num_examples: 18929 - name: epoch_15 num_bytes: 44332404 num_examples: 18929 - name: epoch_16 num_bytes: 44331677 num_examples: 18929 - name: epoch_17 num_bytes: 44332499 num_examples: 18929 - name: epoch_18 num_bytes: 44332572 num_examples: 18929 - name: epoch_19 num_bytes: 44334032 num_examples: 18929 - name: epoch_20 num_bytes: 44334167 num_examples: 18929 - name: epoch_21 num_bytes: 44333390 num_examples: 18929 - name: epoch_22 num_bytes: 44335365 num_examples: 18929 - name: epoch_23 num_bytes: 44334419 num_examples: 18929 - name: epoch_24 num_bytes: 44334230 num_examples: 18929 - name: epoch_25 num_bytes: 44333923 num_examples: 18929 - name: epoch_26 num_bytes: 44333784 num_examples: 18929 - name: epoch_27 num_bytes: 44334765 num_examples: 18929 - name: epoch_28 num_bytes: 44334889 num_examples: 18929 - name: epoch_29 num_bytes: 44334778 num_examples: 18929 download_size: 699687675 dataset_size: 1328698579 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

liuyanchen1015/MULTI_VALUE_wnli_indefinite_for_zero

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 12594 num_examples: 65 - name: test num_bytes: 40930 num_examples: 144 - name: train num_bytes: 115381 num_examples: 604 download_size: 62465 dataset_size: 168905 --- # Dataset Card for "MULTI_VALUE_wnli_indefinite_for_zero" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

MatsuoDochiai/LUISA

--- license: openrail ---

FidelOdok/SOFA_DOA

--- dataset_info: features: - name: audio dtype: audio - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' splits: - name: train num_bytes: 21491814313.0 num_examples: 22500 download_size: 21492710615 dataset_size: 21491814313.0 --- # Dataset Card for "SOFA_DOA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/tachibana_arisu_theidolmastercinderellagirlsu149

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Tachibana Arisu This is the dataset of Tachibana Arisu, containing 200 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 486 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 486 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 486 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 486 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

liuyanchen1015/MULTI_VALUE_rte_inverted_indirect_question

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 28900 num_examples: 53 - name: train num_bytes: 19696 num_examples: 41 download_size: 43113 dataset_size: 48596 --- # Dataset Card for "MULTI_VALUE_rte_inverted_indirect_question" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pierre-pessarossi/climate-question-answers

--- dataset_info: features: - name: instruction dtype: string - name: answer dtype: string splits: - name: train num_bytes: 2467048 num_examples: 7033 - name: test num_bytes: 622679 num_examples: 1758 download_size: 1892524 dataset_size: 3089727 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* license: mit task_categories: - question-answering language: - en tags: - climate pretty_name: Climate change questions/answers size_categories: - 1K<n<10K --- Dataset Card for Climate change questions / answers dataset Dataset Description\ This is a first version of a question/answer dataset on climate change and ecology. The dataset has been created based on a curated list of wikipedia articles on climate change from https://huggingface.co/datasets/pierre-pessarossi/wikipedia-climate-data For each wikipedia article of the original dataset, a set of question/answers pairs was created. The number of question depends on the initial size of the wikipedia article. Currently, there are only question / answer pairs (i.e. no chat mode with several messages). Open-mixtral-8x7b was used to generate the question and answers. The dataset can be useful for supervised fine-tuning on the topic of climate change.\ In forthcoming releases the dataset will be expanded in length and different LLMs might be used to generate the question / answer.

bclavie/mmarco-japanese-hard-negatives

--- language: - ja task_categories: - text-retrieval dataset_info: features: - name: query dtype: string - name: positives sequence: string - name: negatives sequence: string - name: bm25_negatives sequence: string - name: original_negatives sequence: string splits: - name: train num_bytes: 24494938913 num_examples: 391061 download_size: 11664534369 dataset_size: 24494938913 configs: - config_name: default data_files: - split: train path: data/train-* --- [Under Construction] This is a repository containing all the queries from the Japanese part of the MMarco dataset, the multilingual version of the MSMarco dataset. For each query, there are matching hard negatives: - 25 of them retrieved by the multilingual e5 base model. - Up to 10 of them retrieved by the basic implementation of BM25 from Japanese in the Anserini library.

cacheop/red-right-hand

--- license: other dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 711683 num_examples: 315 download_size: 338831 dataset_size: 711683 configs: - config_name: default data_files: - split: train path: data/train-* ---

vishal323/heart

--- license: openrail ---

CyberHarem/saori_bluearchive

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of saori/錠前サオリ/纱织 (Blue Archive) This is the dataset of saori/錠前サオリ/纱织 (Blue Archive), containing 500 images and their tags. The core tags of this character are `long_hair, breasts, halo, blue_eyes, blue_hair, black_hair, large_breasts, multicolored_hair, baseball_cap, hat`, 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 | 500 | 1.11 GiB | [Download](https://huggingface.co/datasets/CyberHarem/saori_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 904.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/saori_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1354 | 1.80 GiB | [Download](https://huggingface.co/datasets/CyberHarem/saori_bluearchive/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/saori_bluearchive', 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 | 19 | ![](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, bare_shoulders, black_headwear, black_pants, black_shirt, crop_top, holding_gun, midriff, navel, sig_sauer, sleeveless_shirt, solo, stomach, white_coat, assault_rifle, black_belt, off_shoulder, open_coat, black_gloves, looking_at_viewer, long_sleeves, mouth_mask, standing, leggings, buckle, armband, cowboy_shot | | 1 | 7 | ![](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, black_headwear, black_pants, black_shirt, crop_top, looking_at_viewer, midriff, navel, off_shoulder, simple_background, sleeveless_shirt, solo, stomach, white_coat, cowboy_shot, standing, white_background, leggings, mouth_mask, open_coat, long_sleeves, black_belt | | 2 | 11 | ![](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, black_pants, black_shirt, crop_top, long_sleeves, looking_at_viewer, midriff, mouth_mask, navel, sleeveless_shirt, solo, stomach, white_coat, bare_shoulders, black_belt, off_shoulder, open_coat, black_gloves, black_headwear, black_mask, cowboy_shot, standing, jacket, chest_harness, snap-fit_buckle, groin, holding_mask, medium_breasts, underbust, unworn_mask | | 3 | 7 | ![](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, alternate_costume, black_bikini, navel, solo, stomach, cleavage, closed_mouth, collarbone, looking_at_viewer, thighs, bare_shoulders, blush, wet, black_headwear, cowboy_shot, outdoors, sideboob, string_bikini, long_sleeves, open_clothes, skindentation, standing, underboob, wading, water | | 4 | 63 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | white_dress, 1girl, alternate_costume, elbow_gloves, solo, white_gloves, cleavage, bare_shoulders, white_choker, looking_at_viewer, earrings, collarbone, colored_inner_hair, simple_background, white_background, closed_mouth, blush, blue_halo, strapless_dress, covered_navel, hair_flower | | 5 | 27 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, elbow_gloves, looking_at_viewer, solo, white_dress, white_gloves, alternate_costume, ass, bare_shoulders, white_background, ponytail, simple_background, from_behind, looking_back, weapon, white_thighhighs, thigh_holster, blush, closed_mouth, knife, garter_straps, thigh_strap, colored_inner_hair | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, alternate_costume, bare_shoulders, elbow_gloves, holding_gun, looking_at_viewer, ponytail, solo, white_dress, white_gloves, from_behind, looking_back, thigh_holster, simple_background, thigh_strap, white_background, assault_rifle, closed_mouth, handgun, mask, side_slit | | 7 | 8 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1boy, 1girl, hetero, penis, pussy, solo_focus, vaginal, anus, looking_at_viewer, looking_back, sex_from_behind, blush, pov, sweat, black_shirt, girl_on_top, indoors, reverse_cowgirl_position, sleeveless_shirt, bare_shoulders, mosaic_censoring, shirt_lift, all_fours, ass_focus, backboob, bottomless, completely_nude, dark-skinned_male, doggystyle, uncensored | | 8 | 12 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1boy, 1girl, blush, hetero, nipples, pussy, sex, solo_focus, vaginal, penis, looking_at_viewer, navel, censored, colored_inner_hair, pov, sweat, completely_nude, female_pubic_hair, choker, hair_ornament, missionary, on_back, spread_legs, elbow_gloves, open_mouth, white_gloves, white_thighhighs | | 9 | 6 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, blush, gangbang, hetero, mosaic_censoring, penis, solo_focus, 3boys, erection, fellatio, nipples, sweat, completely_nude, testicles, cum, double_handjob, male_pubic_hair, navel, pussy, vaginal, veins | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | black_headwear | black_pants | black_shirt | crop_top | holding_gun | midriff | navel | sig_sauer | sleeveless_shirt | solo | stomach | white_coat | assault_rifle | black_belt | off_shoulder | open_coat | black_gloves | looking_at_viewer | long_sleeves | mouth_mask | standing | leggings | buckle | armband | cowboy_shot | simple_background | white_background | black_mask | jacket | chest_harness | snap-fit_buckle | groin | holding_mask | medium_breasts | underbust | unworn_mask | alternate_costume | black_bikini | cleavage | closed_mouth | collarbone | thighs | blush | wet | outdoors | sideboob | string_bikini | open_clothes | skindentation | underboob | wading | water | white_dress | elbow_gloves | white_gloves | white_choker | earrings | colored_inner_hair | blue_halo | strapless_dress | covered_navel | hair_flower | ass | ponytail | from_behind | looking_back | weapon | white_thighhighs | thigh_holster | knife | garter_straps | thigh_strap | handgun | mask | side_slit | 1boy | hetero | penis | pussy | solo_focus | vaginal | anus | sex_from_behind | pov | sweat | girl_on_top | indoors | reverse_cowgirl_position | mosaic_censoring | shirt_lift | all_fours | ass_focus | backboob | bottomless | completely_nude | dark-skinned_male | doggystyle | uncensored | nipples | sex | censored | female_pubic_hair | choker | hair_ornament | missionary | on_back | spread_legs | open_mouth | gangbang | 3boys | erection | fellatio | testicles | cum | double_handjob | male_pubic_hair | veins | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-----------------|:--------------|:--------------|:-----------|:--------------|:----------|:--------|:------------|:-------------------|:-------|:----------|:-------------|:----------------|:-------------|:---------------|:------------|:---------------|:--------------------|:---------------|:-------------|:-----------|:-----------|:---------|:----------|:--------------|:--------------------|:-------------------|:-------------|:---------|:----------------|:------------------|:--------|:---------------|:-----------------|:------------|:--------------|:--------------------|:---------------|:-----------|:---------------|:-------------|:---------|:--------|:------|:-----------|:-----------|:----------------|:---------------|:----------------|:------------|:---------|:--------|:--------------|:---------------|:---------------|:---------------|:-----------|:---------------------|:------------|:------------------|:----------------|:--------------|:------|:-----------|:--------------|:---------------|:---------|:-------------------|:----------------|:--------|:----------------|:--------------|:----------|:-------|:------------|:-------|:---------|:--------|:--------|:-------------|:----------|:-------|:------------------|:------|:--------|:--------------|:----------|:---------------------------|:-------------------|:-------------|:------------|:------------|:-----------|:-------------|:------------------|:--------------------|:-------------|:-------------|:----------|:------|:-----------|:--------------------|:---------|:----------------|:-------------|:----------|:--------------|:-------------|:-----------|:--------|:-----------|:-----------|:------------|:------|:-----------------|:------------------|:--------| | 0 | 19 | ![](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 | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 7 | ![](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 | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 11 | ![](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 | | | | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](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 | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 63 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | | | | | | | | | | X | | | | | | | | X | | | | | | | | X | X | | | | | | | | | | X | | X | X | X | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 27 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | | | | | | | | | | X | | | | | | | | X | | | | | | | | X | X | | | | | | | | | | X | | | X | | | X | | | | | | | | | | X | X | X | | | X | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | | | | | X | | | | | X | | | X | | | | | X | | | | | | | | X | X | | | | | | | | | | X | | | X | | | | | | | | | | | | | X | X | X | | | | | | | | | X | X | X | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 8 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | | | X | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 8 | 12 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | | | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | X | | | X | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | | | X | X | | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | 9 | 6 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | X | | | | X | | | | | | X | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X |

nicholasbien/custom-txt

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 3295001.7540148846 num_examples: 2042 - name: test num_bytes: 824557.2459851155 num_examples: 511 download_size: 1904830 dataset_size: 4119559.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Dinite/vozisaac

--- license: openrail ---

society-ethics/lila_camera_traps

--- annotations_creators: - expert-generated license: - other language_creators: - expert-generated language: - en multilinguality: - monolingual size_categories: - 10M<n<100M source_datasets: - original task_categories: - image-classification tags: - biodiversity - camera trap data - wildlife monitoring pretty_name: LILA Camera Traps --- # Dataset Card for LILA ## Table of Contents - [Table of Contents](#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) - [Tutorial](#tutorial) - [Working with Taxonomies](#working-with-taxonomies) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://lila.science/ - **Repository:** N/A - **Paper:** N/A - **Leaderboard:** N/A - **Point of Contact:** [info@lila.science](info@lila.science) ### Dataset Summary LILA Camera Traps is an aggregate data set of images taken by camera traps, which are devices that automatically (e.g. via motion detection) capture images of wild animals to help ecological research. This data set is the first time when disparate camera trap data sets have been aggregated into a single training environment with a single [taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/). This data set consists of only camera trap image data sets, whereas the broader [LILA](lila.science/) website also has other data sets related to biology and conservation, intended as a resource for both machine learning (ML) researchers and those that want to harness ML for this topic. See below for information about each specific dataset that LILA contains: <details> <summary> Caltech Camera Traps </summary> This data set contains 243,100 images from 140 camera locations in the Southwestern United States, with labels for 21 animal categories (plus empty), primarily at the species level (for example, the most common labels are opossum, raccoon, and coyote), and approximately 66,000 bounding box annotations. Approximately 70% of images are labeled as empty. More information about this data set is available [here](https://beerys.github.io/CaltechCameraTraps/). This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). For questions about this data set, contact caltechcameratraps@gmail.com. If you use this data set, please cite the associated manuscript: ```bibtex @inproceedings{DBLP:conf/eccv/BeeryHP18, author = {Sara Beery and Grant Van Horn and Pietro Perona}, title = {Recognition in Terra Incognita}, booktitle = {Computer Vision - {ECCV} 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part {XVI}}, pages = {472--489}, year = {2018}, crossref = {DBLP:conf/eccv/2018-16}, url = {https://doi.org/10.1007/978-3-030-01270-0\_28}, doi = {10.1007/978-3-030-01270-0\_28}, timestamp = {Mon, 08 Oct 2018 17:08:07 +0200}, biburl = {https://dblp.org/rec/bib/conf/eccv/BeeryHP18}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` </details> <details> <summary> ENA24 </summary> This data set contains approximately 10,000 camera trap images representing 23 classes from Eastern North America, with bounding boxes on each image. The most common classes are “American Crow”, “American Black Bear”, and “Dog”. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). Please cite this manuscript if you use this data set: ```bibtex @article{yousif2019dynamic, title={Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild}, author={Yousif, Hayder and Kays, Roland and He, Zhihai}, journal={IEEE Transactions on Circuits and Systems for Video Technology}, year={2019}, publisher={IEEE} } ``` For questions about this data set, contact [Hayder Yousif](hyypp5@mail.missouri.edu). </details> <details> <summary> Missouri Camera Traps </summary> This data set contains approximately 25,000 camera trap images representing 20 species (for example, the most common labels are red deer, mouflon, and white-tailed deer). Images within each sequence share the same species label (even though the animal may not have been recorded in all the images in the sequence). Around 900 bounding boxes are included. These are very challenging sequences with highly cluttered and dynamic scenes. Spatial resolutions of the images vary from 1920 × 1080 to 2048 × 1536. Sequence lengths vary from 3 to more than 300 frames. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). If you use this data set, please cite the associated manuscript: ```bibtex @article{zhang2016animal, title={Animal detection from highly cluttered natural scenes using spatiotemporal object region proposals and patch verification}, author={Zhang, Zhi and He, Zhihai and Cao, Guitao and Cao, Wenming}, journal={IEEE Transactions on Multimedia}, volume={18}, number={10}, pages={2079--2092}, year={2016}, publisher={IEEE} } ``` For questions about this data set, contact [Hayder Yousif](hyypp5@mail.missouri.edu) and [Zhi Zhang](zzbhf@mail.missouri.edu). </details> <details> <summary> North American Camera Trap Images (NACTI) </summary> This data set contains 3.7M camera trap images from five locations across the United States, with labels for 28 animal categories, primarily at the species level (for example, the most common labels are cattle, boar, and red deer). Approximately 12% of images are labeled as empty. We have also added bounding box annotations to 8892 images (mostly vehicles and birds). This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). Please cite this manuscript if you use this data set: ```bibtex @article{tabak2019machine, title={Machine learning to classify animal species in camera trap images: Applications in ecology}, author={Tabak, Michael A and Norouzzadeh, Mohammad S and Wolfson, David W and Sweeney, Steven J and VerCauteren, Kurt C and Snow, Nathan P and Halseth, Joseph M and Di Salvo, Paul A and Lewis, Jesse S and White, Michael D and others}, journal={Methods in Ecology and Evolution}, volume={10}, number={4}, pages={585--590}, year={2019}, publisher={Wiley Online Library} } ``` For questions about this data set, contact [northamericancameratrapimages@gmail.com](northamericancameratrapimages@gmail.com). </details> <details> <summary> WCS Camera Traps </summary> This data set contains approximately 1.4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly. Data were provided by the [Wildlife Conservation Society](https://www.wcs.org/). The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle). A complete list of classes and associated image counts is available here. Approximately 50% of images are empty. We have also added approximately 375,000 bounding box annotations to approximately 300,000 of those images, which come from sequences covering almost all locations. Sequences are inferred from timestamps, so may not strictly represent bursts. Images were labeled at a combination of image and sequence level, so – as is the case with most camera trap data sets – empty images may be labeled as non-empty (if an animal was present in one frame of a sequence but not in others). Images containing humans are referred to in metadata, but are not included in the data files. You can find more information about the data set [on the LILA website](https://lila.science/datasets/wcscameratraps). This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Wellington Camera Traps </summary> This data set contains 270,450 images from 187 camera locations in Wellington, New Zealand. The cameras (Bushnell 119537, 119476, and 119436) recorded sequences of three images when triggered. Each sequence was labelled by citizen scientists and/or professional ecologists from Victoria University of Wellington into 17 classes: 15 animal categories (for example, the most common labels are bird, cat, and hedgehog), empty, and unclassifiable. Approximately 17% of images are labeled as empty. Images within each sequence share the same species label (even though the animal may not have been recorded in all three images). If you use this data set, please cite the associated manuscript: ```bibtex @article{anton2018monitoring, title={Monitoring the mammalian fauna of urban areas using remote cameras and citizen science}, author={Anton, Victor and Hartley, Stephen and Geldenhuis, Andre and Wittmer, Heiko U}, journal={Journal of Urban Ecology}, volume={4}, number={1}, pages={juy002}, year={2018}, publisher={Oxford University Press} } ``` This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). For questions about this data set, contact [Victor Anton](vykanton@gmail.com). </details> <details> <summary> Island Conservation Camera Traps </summary> This data set contains approximately 123,000 camera trap images from 123 camera locations from 7 islands in 6 countries. Data were provided by Island Conservation during projects conducted to prevent the extinction of threatened species on islands. The most common classes are rabbit, rat, petrel, iguana, cat, goat, and pig, with both rat and cat represented between multiple island sites representing significantly different ecosystems (tropical forest, dry forest, and temperate forests). Additionally, this data set represents data from locations and ecosystems that, to our knowledge, are not well represented in publicly available datasets including >1,000 images each of iguanas, petrels, and shearwaters. A complete list of classes and associated image counts is available here. Approximately 60% of the images are empty. We have also included approximately 65,000 bounding box annotations for about 50,000 images. In general cameras were dispersed across each project site to detect the presence of invasive vertebrate species that threaten native island species. Cameras were set to capture bursts of photos for each motion detection event (between three and eight photos) with a set delay between events (10 to 30 seconds) to minimize the number of photos. Images containing humans are referred to in metadata, but are not included in the data files. For questions about this data set, contact [David Will](david.will@islandconservation.org) at Island Conservation. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). The original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. If those images are important to your work, contact us; in some cases it will be possible to release those images under an alternative license. </details> <details> <summary> Channel Islands Camera Traps </summary> This data set contains 246,529 camera trap images from 73 camera locations in the Channel Islands, California. All animals are annotated with bounding boxes. Data were provided by The Nature Conservancy. Animals are classified as rodent1 (82914), fox (48150), bird (11099), skunk (1071), or other (159). 114,949 images (47%) are empty. All images of rats were taken on islands already known to have rat populations. If you use these data in a publication or report, please use the following citation: The Nature Conservancy (2021): Channel Islands Camera Traps 1.0. The Nature Conservancy. Dataset. For questions about this data set, contact [Nathaniel Rindlaub](nathaniel.rindlaub@TNC.ORG) at The Nature Conservancy. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). The original data set included a “human” class label; for privacy reasons, we have removed those images from this version of the data set. Those labels are still present in the metadata. </details> <details> <summary> Idaho Camera Traps </summary> This data set contains approximately 1.5 million camera trap images from Idaho. Labels are provided for 62 categories, most of which are animal classes (“deer”, “elk”, and “cattle” are the most common animal classes), but labels also include some state indicators (e.g. “snow on lens”, “foggy lens”). Approximately 70.5% of images are labeled as empty. Annotations were assigned to image sequences, rather than individual images, so annotations are meaningful only at the sequence level. The metadata contains references to images containing humans, but these have been removed from the dataset (along with images containing vehicles and domestic dogs). Images were provided by the Idaho Department of Fish and Game. No representations or warranties are made regarding the data, including but not limited to warranties of non-infringement or fitness for a particular purpose. Some information shared under this agreement may not have undergone quality assurance procedures and should be considered provisional. Images may not be sold in any format, but may be used for scientific publications. Please acknowledge the Idaho Department of Fish and Game when using images for publication or scientific communication. </details> <details> <summary> Snapshot Serengeti </summary> This data set contains approximately 2.65M sequences of camera trap images, totaling 7.1M images, from seasons one through eleven of the [Snapshot Serengeti project](https://snapshotserengeti.org/) -- the flagship project of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Serengeti National Park in Tanzania is best known for the massive annual migrations of wildebeest and zebra that drive the cycling of its dynamic ecosystem. Labels are provided for 61 categories, primarily at the species level (for example, the most common labels are wildebeest, zebra, and Thomson’s gazelle). Approximately 76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshotserengeti-v-2-0/SnapshotSerengeti_S1-11_v2.1.species_list.csv). We have also added approximately 150,000 bounding box annotations to approximately 78,000 of those images. The images and species-level labels are described in more detail in the associated manuscript: ```bibtex @misc{dryad_5pt92, title = {Data from: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna}, author = {Swanson, AB and Kosmala, M and Lintott, CJ and Simpson, RJ and Smith, A and Packer, C}, year = {2015}, journal = {Scientific Data}, URL = {https://doi.org/10.5061/dryad.5pt92}, doi = {doi:10.5061/dryad.5pt92}, publisher = {Dryad Digital Repository} } ``` For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Snapshot Karoo </summary> This data set contains 14889 sequences of camera trap images, totaling 38074 images, from the [Snapshot Karoo](https://www.zooniverse.org/projects/shuebner729/snapshot-karoo) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Karoo National Park, located in the arid Nama Karoo biome of South Africa, is defined by its endemic vegetation and mountain landscapes. Its unique topographical gradient has led to a surprising amount of biodiversity, with 58 mammals and more than 200 bird species recorded, as well as a multitude of reptilian species. Labels are provided for 38 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, hartebeestred, and kudu). Approximately 83.02% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KAR/SnapshotKaroo_S1_v1.0.species_list.csv). For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Snapshot Kgalagadi </summary> This data set contains 3611 sequences of camera trap images, totaling 10222 images, from the [Snapshot Kgalagadi](https://www.zooniverse.org/projects/shuebner729/snapshot-kgalagadi/) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. The Kgalagadi Transfrontier Park stretches from the Namibian border across South Africa and into Botswana, covering a landscape commonly referred to as the Kalahari – an arid savanna. This region is of great interest to help us understand how animals cope with extreme temperatures at both ends of the scale. Labels are provided for 31 categories, primarily at the species level (for example, the most common labels are gemsbokoryx, birdother, and ostrich). Approximately 76.14% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KGA/SnapshotKgalagadi_S1_v1.0.species_list.csv). For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Snapshot Enonkishu </summary> This data set contains 13301 sequences of camera trap images, totaling 28544 images, from the [Snapshot Enonkishu](https://www.zooniverse.org/projects/aguthmann/snapshot-enonkishu) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Enonkishu Conservancy is located on the northern boundary of the Mara-Serengeti ecosystem in Kenya, and is managed by a consortium of stakeholders and land-owning Maasai families. Their aim is to promote coexistence between wildlife and livestock in order to encourage regenerative grazing and build stability in the Mara conservancies. Labels are provided for 39 categories, primarily at the species level (for example, the most common labels are impala, warthog, and zebra). Approximately 64.76% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/ENO/SnapshotEnonkishu_S1_v1.0.species_list.csv). For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Snapshot Camdeboo </summary> This data set contains 12132 sequences of camera trap images, totaling 30227 images, from the [Snapshot Camdeboo](https://www.zooniverse.org/projects/shuebner729/snapshot-camdeboo) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Camdeboo National Park, South Africa is crucial habitat for many birds on a global scale, with greater than fifty endemic and near-endemic species and many migratory species. Labels are provided for 43 categories, primarily at the species level (for example, the most common labels are kudu, springbok, and ostrich). Approximately 43.74% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/CDB/SnapshotCamdeboo_S1_v1.0.species_list.csv). For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Snapshot Mountain Zebra </summary> This data set contains 71688 sequences of camera trap images, totaling 73034 images, from the [Snapshot Mountain Zebra](https://www.zooniverse.org/projects/meredithspalmer/snapshot-mountain-zebra/) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Mountain Zebra National Park is located in the Eastern Cape of South Africa in a transitional area between several distinct biomes, which means it is home to many endemic species. As the name suggests, this park contains the largest remnant population of Cape Mountain zebras, ~700 as of 2019 and increasing steadily every year. Labels are provided for 54 categories, primarily at the species level (for example, the most common labels are zebramountain, kudu, and springbok). Approximately 91.23% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/MTZ/SnapshotMountainZebra_S1_v1.0.species_list.csv). For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Snapshot Kruger </summary> This data set contains 4747 sequences of camera trap images, totaling 10072 images, from the [Snapshot Kruger](https://www.zooniverse.org/projects/shuebner729/snapshot-kruger) project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Kruger National Park, South Africa has been a refuge for wildlife since its establishment in 1898, and it houses one of the most diverse wildlife assemblages remaining in Africa. The Snapshot Safari grid was established in 2018 as part of a research project assessing the impacts of large mammals on plant life as boundary fences were removed and wildlife reoccupied areas of previous extirpation. Labels are provided for 46 categories, primarily at the species level (for example, the most common labels are impala, elephant, and buffalo). Approximately 61.60% of images are labeled as empty. A full list of species and associated image counts is available [here](https://lilablobssc.blob.core.windows.net/snapshot-safari/KRU/SnapshotKruger_S1_v1.0.species_list.csv). For questions about this data set, contact [Sarah Huebner](huebn090@umn.edu) at the University of Minnesota. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> SWG Camera Traps </summary> This data set contains 436,617 sequences of camera trap images from 982 locations in Vietnam and Lao, totaling 2,039,657 images. Labels are provided for 120 categories, primarily at the species level (for example, the most common labels are “Eurasian Wild Pig”, “Large-antlered Muntjac”, and “Unidentified Murid”). Approximately 12.98% of images are labeled as empty. A full list of species and associated image counts is available here. 101,659 bounding boxes are provided on 88,135 images. This data set is provided by the Saola Working Group; providers include: - IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group (SWG) - Asian Arks - Wildlife Conservation Society (Lao) - WWF Lao - Integrated Conservation of Biodiversity and Forests project, Lao (ICBF) - Center for Environment and Rural Development, Vinh University, Vietnam If you use these data in a publication or report, please use the following citation: SWG (2021): Northern and Central Annamites Camera Traps 2.0. IUCN SSC Asian Wild Cattle Specialist Group’s Saola Working Group. Dataset. For questions about this data set, contact saolawg@gmail.com. This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> <details> <summary> Orinoquia Camera Traps </summary> This data set contains 104,782 images collected from a 50-camera-trap array deployed from January to July 2020 within the private natural reserves El Rey Zamuro (31 km2) and Las Unamas (40 km2), located in the Meta department in the Orinoquía region in central Colombia. We deployed cameras using a stratified random sampling design across forest core area strata. Cameras were spaced 1 km apart from one another, located facing wildlife trails, and deployed with no bait. Images were stored and reviewed by experts using the Wildlife Insights platform. This data set contains 51 classes, predominantly mammals such as the collared peccary, black agouti, spotted paca, white-lipped peccary, lowland tapir, and giant anteater. Approximately 20% of images are empty. The main purpose of the study is to understand how humans, wildlife, and domestic animals interact in multi-functional landscapes (e.g., agricultural livestock areas with native forest remnants). However, this data set was also used to review model performance of AI-powered platforms – Wildlife Insights (WI), MegaDetector (MD), and Machine Learning for Wildlife Image Classification (MLWIC2). We provide a demonstration of the use of WI, MD, and MLWIC2 and R code for evaluating model performance of these platforms in the accompanying [GitHub repository](https://github.com/julianavelez1/Processing-Camera-Trap-Data-Using-AI). If you use these data in a publication or report, please use the following citation: ```bibtex @article{velez2022choosing, title={Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence}, author={V{\'e}lez, Juliana and Castiblanco-Camacho, Paula J and Tabak, Michael A and Chalmers, Carl and Fergus, Paul and Fieberg, John}, journal={arXiv preprint arXiv:2202.02283}, year={2022} } ``` For questions about this data set, contact [Juliana Velez Gomez](julianavelezgomez@gmail.com). This data set is released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). </details> ### Supported Tasks and Leaderboards No leaderboards exist for LILA. ### Languages The [LILA taxonomy](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/) is provided in English. ## Dataset Structure ### Data Instances The data annotations are provided in [COCO Camera Traps](https://github.com/Microsoft/CameraTraps/blob/master/data_management/README.md#coco-cameratraps-format) format. All of the datasets share a common category taxonomy, which is defined on the [LILA website](https://lila.science/taxonomy-mapping-for-camera-trap-data-sets/). ### Data Fields Different datasets may have slightly varying fields, which include: `file_name`: the file name \ `width` and `height`: the dimensions of the image \ `study`: which research study the image was collected as part of \ `location` : the name of the location at which the image was taken \ `annotations`: information about image annotation, which includes the taxonomy information, bounding box/boxes (`bbox`/`bboxes`) if any, as well as any other annotation information. \ `image` : the `path` to download the image and any other information that is available, e.g. its size in `bytes`. ### Data Splits This dataset does not have a predefined train/test split. ## Dataset Creation ### Curation Rationale The datasets that constitute LILA have been provided by the organizations, projects and researchers who collected them. ### Source Data #### Initial data collection and normalization N/A #### Who are the source language producers? N/A ### Annotations #### Annotation process Each dataset has been annotated by the members of the project/organization that provided it. #### Who are the annotators? The annotations have been provided by domain experts in fields such as biology and ecology. ### Personal and Sensitive Information Some of the original data sets included a “human” class label; for privacy reasons, these images were removed. Those labels are still present in the metadata. If those images are important to your work, contact the [LILA maintainers](mailto:info@lila.science), since in some cases it will be possible to release those images under an alternative license. ## Considerations for Using the Data ### Social Impact of Dataset Machine learning depends on labeled data, but accessing such data in biology and conservation is a challenge. Consequently, everyone benefits when labeled data is made available. Biologists and conservation scientists benefit by having data to train on, and free hosting allows teams to multiply the impact of their data (we suggest listing this benefit in grant proposals that fund data collection). ML researchers benefit by having data to experiment with. ### Discussion of Biases These datasets do not represent global diversity, but are examples of local ecosystems and animals. ### Other Known Limitations N/A ## Additional Information ### Tutorial The [tutorial in this Google Colab notebook](https://colab.research.google.com/drive/17gPOIK-ksxPyX6yP9TaKIimlwf9DYe2R?usp=sharing) demonstrates how to work with this dataset, including filtering by species, collating configurations, and downloading images. ### Working with Taxonomies All the taxonomy categories are saved as ClassLabels, which can be converted to strings as needed. Strings can likewise be converted to integers as needed, to filter the dataset. In the example below we filter the "Caltech Camera Traps" dataset to find all the entries with a "felis catus" as the species for the first annotation. ```python dataset = load_dataset("society-ethics/lila_camera_traps", "Caltech Camera Traps", split="train") taxonomy = dataset.features["annotations"].feature["taxonomy"] # Filters to show only cats cats = dataset.filter(lambda x: x["annotations"]["taxonomy"][0]["species"] == taxonomy["species"].str2int("felis catus")) ``` The original common names have been saved with their taxonomy mappings in this repository in `common_names_to_tax.json`. These can be used, for example, to map from a taxonomy combination to a common name to help make queries more legible. Note, however, that there is a small number of duplicate common names with different taxonomy values which you will need to disambiguate. The following example loads the first "sea turtle" in the "Island Conservation Camera Traps" dataset. ```python LILA_COMMON_NAMES_TO_TAXONOMY = pd.read_json("https://huggingface.co/datasets/society-ethics/lila_camera_traps/raw/main/data/common_names_to_tax.json", lines=True).set_index("common_name") dataset = load_dataset("society-ethics/lila_camera_traps", "Island Conservation Camera Traps", split="train") taxonomy = dataset.features["annotations"].feature["taxonomy"] sea_turtle = LILA_COMMON_NAMES_TO_TAXONOMY.loc["sea turtle"].to_dict() sea_turtle = {k: taxonomy[k].str2int(v) if v is not None else v for k, v in sea_turtle.items()} # Map to ClassLabel integers sea_turtle_dataset = ds.filter(lambda x: x["annotations"]["taxonomy"][0] == sea_turtle) ``` The example below selects a random item from the dataset, and then maps from the taxonomy to a common name: ```python LILA_COMMON_NAMES_TO_TAXONOMY = pd.read_json("https://huggingface.co/datasets/society-ethics/lila_camera_traps/raw/main/data/common_names_to_tax.json", lines=True).set_index("common_name") dataset = load_dataset("society-ethics/lila_camera_traps", "Caltech Camera Traps", split="train") taxonomy = dataset.features["annotations"].feature["taxonomy"] random_entry = dataset.shuffle()[0] filter_taxonomy = random_entry["annotations"]["taxonomy"][0] filter_keys = list(map(lambda x: (x[0], taxonomy[x[0]].int2str(x[1])), filter(lambda x: x[1] is not None, list(filter_taxonomy.items())))) if len(filter_keys) > 0: print(LILA_COMMON_NAMES_TO_TAXONOMY[np.logical_and.reduce([ LILA_COMMON_NAMES_TO_TAXONOMY[k] == v for k,v in filter_keys ])]) else: print("No common name found for the item.") ``` ### Dataset Curators LILA BC is maintained by a working group that includes representatives from Ecologize, Zooniverse, the Evolving AI Lab, Snapshot Safari, and Microsoft AI for Earth. Hosting on Microsoft Azure is provided by Microsoft AI for Earth. ### Licensing Information Many, but not all, LILA data sets were released under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/). Check the details of the specific dataset you are using in its section above. ### Citation Information Citations for each dataset (if they exist) are provided in its section above. ### Contributions Thanks to [@NimaBoscarino](https://github.com/NimaBoscarino/) for adding this dataset.

open-llm-leaderboard/details_migtissera__Tess-10.7B-v1.5

--- pretty_name: Evaluation run of migtissera/Tess-10.7B-v1.5 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [migtissera/Tess-10.7B-v1.5](https://huggingface.co/migtissera/Tess-10.7B-v1.5)\ \ 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_migtissera__Tess-10.7B-v1.5\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-27T08:16:07.104140](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-10.7B-v1.5/blob/main/results_2024-01-27T08-16-07.104140.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\": 0.6513655424521041,\n\ \ \"acc_stderr\": 0.03165794538741113,\n \"acc_norm\": 0.6541051437423594,\n\ \ \"acc_norm_stderr\": 0.032296434557489546,\n \"mc1\": 0.32558139534883723,\n\ \ \"mc1_stderr\": 0.01640398946990783,\n \"mc2\": 0.47430080710659894,\n\ \ \"mc2_stderr\": 0.014677705750823734\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.60580204778157,\n \"acc_stderr\": 0.014280522667467325,\n\ \ \"acc_norm\": 0.6501706484641638,\n \"acc_norm_stderr\": 0.013936809212158289\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6490738896634136,\n\ \ \"acc_stderr\": 0.004762844770909862,\n \"acc_norm\": 0.8406691894045011,\n\ \ \"acc_norm_stderr\": 0.0036523632532895916\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7631578947368421,\n \"acc_stderr\": 0.03459777606810536,\n\ \ \"acc_norm\": 0.7631578947368421,\n \"acc_norm_stderr\": 0.03459777606810536\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.69,\n\ \ \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \ \ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438665,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438665\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\"\ : {\n \"acc\": 0.6820809248554913,\n \"acc_stderr\": 0.035506839891655796,\n\ \ \"acc_norm\": 0.6820809248554913,\n \"acc_norm_stderr\": 0.035506839891655796\n\ \ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.37254901960784315,\n\ \ \"acc_stderr\": 0.04810840148082635,\n \"acc_norm\": 0.37254901960784315,\n\ \ \"acc_norm_stderr\": 0.04810840148082635\n },\n \"harness|hendrycksTest-computer_security|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5957446808510638,\n\ \ \"acc_stderr\": 0.032081157507886836,\n \"acc_norm\": 0.5957446808510638,\n\ \ \"acc_norm_stderr\": 0.032081157507886836\n },\n \"harness|hendrycksTest-econometrics|5\"\ : {\n \"acc\": 0.4649122807017544,\n \"acc_stderr\": 0.046920083813689104,\n\ \ \"acc_norm\": 0.4649122807017544,\n \"acc_norm_stderr\": 0.046920083813689104\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\"\ : 0.42063492063492064,\n \"acc_stderr\": 0.02542483508692399,\n \"\ acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.02542483508692399\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n\ \ \"acc_stderr\": 0.04403438954768177,\n \"acc_norm\": 0.4126984126984127,\n\ \ \"acc_norm_stderr\": 0.04403438954768177\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8,\n\ \ \"acc_stderr\": 0.02275520495954294,\n \"acc_norm\": 0.8,\n \ \ \"acc_norm_stderr\": 0.02275520495954294\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175008,\n\ \ \"acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\ \ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822513,\n \"\ acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822513\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.01871899852067819,\n\ \ \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.01871899852067819\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6410256410256411,\n \"acc_stderr\": 0.024321738484602357,\n\ \ \"acc_norm\": 0.6410256410256411,\n \"acc_norm_stderr\": 0.024321738484602357\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \ \ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7016806722689075,\n \"acc_stderr\": 0.029719142876342853,\n\ \ \"acc_norm\": 0.7016806722689075,\n \"acc_norm_stderr\": 0.029719142876342853\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119995,\n \"\ acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119995\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669237,\n \"\ acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669237\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5787037037037037,\n \"acc_stderr\": 0.03367462138896078,\n \"\ acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.03367462138896078\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8284313725490197,\n \"acc_stderr\": 0.026460569561240644,\n \"\ acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.026460569561240644\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8481012658227848,\n \"acc_stderr\": 0.02336387809663245,\n \ \ \"acc_norm\": 0.8481012658227848,\n \"acc_norm_stderr\": 0.02336387809663245\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7130044843049327,\n\ \ \"acc_stderr\": 0.03036037971029195,\n \"acc_norm\": 0.7130044843049327,\n\ \ \"acc_norm_stderr\": 0.03036037971029195\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596915,\n\ \ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596915\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.03957835471980981,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.03957835471980981\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\ \ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n\ \ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406957,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406957\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8275862068965517,\n\ \ \"acc_stderr\": 0.013507943909371803,\n \"acc_norm\": 0.8275862068965517,\n\ \ \"acc_norm_stderr\": 0.013507943909371803\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044283,\n\ \ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044283\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.29497206703910617,\n\ \ \"acc_stderr\": 0.015251931579208181,\n \"acc_norm\": 0.29497206703910617,\n\ \ \"acc_norm_stderr\": 0.015251931579208181\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7712418300653595,\n \"acc_stderr\": 0.024051029739912258,\n\ \ \"acc_norm\": 0.7712418300653595,\n \"acc_norm_stderr\": 0.024051029739912258\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\ \ \"acc_stderr\": 0.026003301117885135,\n \"acc_norm\": 0.7009646302250804,\n\ \ \"acc_norm_stderr\": 0.026003301117885135\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\ : 0.48936170212765956,\n \"acc_stderr\": 0.02982074719142246,\n \"\ acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.02982074719142246\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.48565840938722293,\n\ \ \"acc_stderr\": 0.01276498182952427,\n \"acc_norm\": 0.48565840938722293,\n\ \ \"acc_norm_stderr\": 0.01276498182952427\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7279411764705882,\n \"acc_stderr\": 0.02703304115168146,\n\ \ \"acc_norm\": 0.7279411764705882,\n \"acc_norm_stderr\": 0.02703304115168146\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.673202614379085,\n \"acc_stderr\": 0.018975427920507208,\n \ \ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.018975427920507208\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\": 0.7591836734693878,\n \"acc_stderr\": 0.02737294220178816,\n\ \ \"acc_norm\": 0.7591836734693878,\n \"acc_norm_stderr\": 0.02737294220178816\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\ \ \"acc_stderr\": 0.024845753212306053,\n \"acc_norm\": 0.8557213930348259,\n\ \ \"acc_norm_stderr\": 0.024845753212306053\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.03015113445777634,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.03015113445777634\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7953216374269005,\n \"acc_stderr\": 0.030944459778533193,\n\ \ \"acc_norm\": 0.7953216374269005,\n \"acc_norm_stderr\": 0.030944459778533193\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.32558139534883723,\n\ \ \"mc1_stderr\": 0.01640398946990783,\n \"mc2\": 0.47430080710659894,\n\ \ \"mc2_stderr\": 0.014677705750823734\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8334648776637726,\n \"acc_stderr\": 0.010470796496781091\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5435936315390447,\n \ \ \"acc_stderr\": 0.013720038270485327\n }\n}\n```" repo_url: https://huggingface.co/migtissera/Tess-10.7B-v1.5 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_01_27T08_16_07.104140 path: - '**/details_harness|arc:challenge|25_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-27T08-16-07.104140.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|gsm8k|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hellaswag|10_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-27T08-16-07.104140.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-management|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-27T08-16-07.104140.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|truthfulqa:mc|0_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-27T08-16-07.104140.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_27T08_16_07.104140 path: - '**/details_harness|winogrande|5_2024-01-27T08-16-07.104140.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-27T08-16-07.104140.parquet' - config_name: results data_files: - split: 2024_01_27T08_16_07.104140 path: - results_2024-01-27T08-16-07.104140.parquet - split: latest path: - results_2024-01-27T08-16-07.104140.parquet --- # Dataset Card for Evaluation run of migtissera/Tess-10.7B-v1.5  Dataset automatically created during the evaluation run of model [migtissera/Tess-10.7B-v1.5](https://huggingface.co/migtissera/Tess-10.7B-v1.5) 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_migtissera__Tess-10.7B-v1.5", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-27T08:16:07.104140](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-10.7B-v1.5/blob/main/results_2024-01-27T08-16-07.104140.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": 0.6513655424521041, "acc_stderr": 0.03165794538741113, "acc_norm": 0.6541051437423594, "acc_norm_stderr": 0.032296434557489546, "mc1": 0.32558139534883723, "mc1_stderr": 0.01640398946990783, "mc2": 0.47430080710659894, "mc2_stderr": 0.014677705750823734 }, "harness|arc:challenge|25": { "acc": 0.60580204778157, "acc_stderr": 0.014280522667467325, "acc_norm": 0.6501706484641638, "acc_norm_stderr": 0.013936809212158289 }, "harness|hellaswag|10": { "acc": 0.6490738896634136, "acc_stderr": 0.004762844770909862, "acc_norm": 0.8406691894045011, "acc_norm_stderr": 0.0036523632532895916 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.31, "acc_stderr": 0.046482319871173156, "acc_norm": 0.31, "acc_norm_stderr": 0.046482319871173156 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7631578947368421, "acc_stderr": 0.03459777606810536, "acc_norm": 0.7631578947368421, "acc_norm_stderr": 0.03459777606810536 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6981132075471698, "acc_stderr": 0.028254200344438665, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.028254200344438665 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6820809248554913, "acc_stderr": 0.035506839891655796, "acc_norm": 0.6820809248554913, "acc_norm_stderr": 0.035506839891655796 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082635, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082635 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5957446808510638, "acc_stderr": 0.032081157507886836, "acc_norm": 0.5957446808510638, "acc_norm_stderr": 0.032081157507886836 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4649122807017544, "acc_stderr": 0.046920083813689104, "acc_norm": 0.4649122807017544, "acc_norm_stderr": 0.046920083813689104 }, "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": 0.42063492063492064, "acc_stderr": 0.02542483508692399, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.02542483508692399 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4126984126984127, "acc_stderr": 0.04403438954768177, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.04403438954768177 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8, "acc_stderr": 0.02275520495954294, "acc_norm": 0.8, "acc_norm_stderr": 0.02275520495954294 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.47783251231527096, "acc_stderr": 0.03514528562175008, "acc_norm": 0.47783251231527096, "acc_norm_stderr": 0.03514528562175008 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8121212121212121, "acc_stderr": 0.03050193405942914, "acc_norm": 0.8121212121212121, "acc_norm_stderr": 0.03050193405942914 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8686868686868687, "acc_stderr": 0.024063156416822513, "acc_norm": 0.8686868686868687, "acc_norm_stderr": 0.024063156416822513 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.927461139896373, "acc_stderr": 0.01871899852067819, "acc_norm": 0.927461139896373, "acc_norm_stderr": 0.01871899852067819 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6410256410256411, "acc_stderr": 0.024321738484602357, "acc_norm": 0.6410256410256411, "acc_norm_stderr": 0.024321738484602357 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34444444444444444, "acc_stderr": 0.02897264888484427, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.02897264888484427 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7016806722689075, "acc_stderr": 0.029719142876342853, "acc_norm": 0.7016806722689075, "acc_norm_stderr": 0.029719142876342853 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2913907284768212, "acc_stderr": 0.03710185726119995, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.03710185726119995 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8495412844036697, "acc_stderr": 0.015328563932669237, "acc_norm": 0.8495412844036697, "acc_norm_stderr": 0.015328563932669237 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5787037037037037, "acc_stderr": 0.03367462138896078, "acc_norm": 0.5787037037037037, "acc_norm_stderr": 0.03367462138896078 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8284313725490197, "acc_stderr": 0.026460569561240644, "acc_norm": 0.8284313725490197, "acc_norm_stderr": 0.026460569561240644 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8481012658227848, "acc_stderr": 0.02336387809663245, "acc_norm": 0.8481012658227848, "acc_norm_stderr": 0.02336387809663245 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7130044843049327, "acc_stderr": 0.03036037971029195, "acc_norm": 0.7130044843049327, "acc_norm_stderr": 0.03036037971029195 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596915, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596915 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.03957835471980981, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.03957835471980981 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7423312883435583, "acc_stderr": 0.03436150827846917, "acc_norm": 0.7423312883435583, "acc_norm_stderr": 0.03436150827846917 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5178571428571429, "acc_stderr": 0.047427623612430116, "acc_norm": 0.5178571428571429, "acc_norm_stderr": 0.047427623612430116 }, "harness|hendrycksTest-management|5": { "acc": 0.8446601941747572, "acc_stderr": 0.03586594738573974, "acc_norm": 0.8446601941747572, "acc_norm_stderr": 0.03586594738573974 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406957, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406957 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371803, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371803 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7427745664739884, "acc_stderr": 0.023532925431044283, "acc_norm": 0.7427745664739884, "acc_norm_stderr": 0.023532925431044283 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.29497206703910617, "acc_stderr": 0.015251931579208181, "acc_norm": 0.29497206703910617, "acc_norm_stderr": 0.015251931579208181 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7712418300653595, "acc_stderr": 0.024051029739912258, "acc_norm": 0.7712418300653595, "acc_norm_stderr": 0.024051029739912258 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7009646302250804, "acc_stderr": 0.026003301117885135, "acc_norm": 0.7009646302250804, "acc_norm_stderr": 0.026003301117885135 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48936170212765956, "acc_stderr": 0.02982074719142246, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.02982074719142246 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.48565840938722293, "acc_stderr": 0.01276498182952427, "acc_norm": 0.48565840938722293, "acc_norm_stderr": 0.01276498182952427 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7279411764705882, "acc_stderr": 0.02703304115168146, "acc_norm": 0.7279411764705882, "acc_norm_stderr": 0.02703304115168146 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.673202614379085, "acc_stderr": 0.018975427920507208, "acc_norm": 0.673202614379085, "acc_norm_stderr": 0.018975427920507208 }, "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": 0.7591836734693878, "acc_stderr": 0.02737294220178816, "acc_norm": 0.7591836734693878, "acc_norm_stderr": 0.02737294220178816 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8557213930348259, "acc_stderr": 0.024845753212306053, "acc_norm": 0.8557213930348259, "acc_norm_stderr": 0.024845753212306053 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.9, "acc_stderr": 0.03015113445777634, "acc_norm": 0.9, "acc_norm_stderr": 0.03015113445777634 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7953216374269005, "acc_stderr": 0.030944459778533193, "acc_norm": 0.7953216374269005, "acc_norm_stderr": 0.030944459778533193 }, "harness|truthfulqa:mc|0": { "mc1": 0.32558139534883723, "mc1_stderr": 0.01640398946990783, "mc2": 0.47430080710659894, "mc2_stderr": 0.014677705750823734 }, "harness|winogrande|5": { "acc": 0.8334648776637726, "acc_stderr": 0.010470796496781091 }, "harness|gsm8k|5": { "acc": 0.5435936315390447, "acc_stderr": 0.013720038270485327 } } ``` ## 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 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[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]

jirong/grit_2m

--- license: apache-2.0 ---

CyberHarem/jacques_de_molay_fgo

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of jacques_de_molay/ジャック・ド・モレー/雅克·德·莫莱 (Fate/Grand Order) This is the dataset of jacques_de_molay/ジャック・ド・モレー/雅克·德·莫莱 (Fate/Grand Order), containing 122 images and their tags. The core tags of this character are `short_hair, grey_hair, glasses, breasts, large_breasts, blue_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 122 | 202.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jacques_de_molay_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 122 | 173.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jacques_de_molay_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 314 | 348.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jacques_de_molay_fgo/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/jacques_de_molay_fgo', 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 | 10 | ![](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, bare_shoulders, black_dress, horns, smile, solo, yellow_eyes, cleavage, grey_skin, looking_at_viewer, choker, collarbone, detached_sleeves, open_mouth, thighs | | 1 | 7 | ![](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) | bare_shoulders, black_dress, cleavage, collarbone, grey_skin, horns, looking_at_viewer, yellow_eyes, 1girl, solo, choker, detached_sleeves, smile, short_dress, simple_background, sword, thighs | | 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, belt, black_dress, black_jacket, cleavage, cropped_jacket, hooded_jacket, long_sleeves, looking_at_viewer, open_jacket, short_dress, smile, sheep, thighs, sword, purple_eyes | | 3 | 5 | ![](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, belt, black_dress, black_jacket, cropped_jacket, hooded_jacket, long_sleeves, looking_at_viewer, open_jacket, open_mouth, short_dress, solo, sword, thighs, cleavage, smile, yellow_eyes | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, belt, black_dress, black_jacket, cleavage, cropped_jacket, hooded_jacket, long_sleeves, looking_at_viewer, open_jacket, short_dress, solo, sword, smile, sheath | | 5 | 9 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, black_dress, black_jacket, cropped_jacket, hooded_jacket, long_sleeves, looking_at_viewer, open_jacket, solo, cleavage, smile, upper_body | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | black_dress | horns | smile | solo | yellow_eyes | cleavage | grey_skin | looking_at_viewer | choker | collarbone | detached_sleeves | open_mouth | thighs | short_dress | simple_background | sword | belt | black_jacket | cropped_jacket | hooded_jacket | long_sleeves | open_jacket | sheep | purple_eyes | sheath | upper_body | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------------|:--------|:--------|:-------|:--------------|:-----------|:------------|:--------------------|:---------|:-------------|:-------------------|:-------------|:---------|:--------------|:--------------------|:--------|:-------|:---------------|:-----------------|:----------------|:---------------|:--------------|:--------|:--------------|:---------|:-------------| | 0 | 10 | ![](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 | | | | | | | | | | | | | | | 1 | 7 | ![](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 | | | | | | | | | | | | 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 | | | | 3 | 5 | ![](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 | X | X | | | | | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | X | | X | X | | X | | X | | | | | | X | | X | X | X | X | X | X | X | | | X | | | 5 | 9 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | X | | X | X | | X | | X | | | | | | | | | | X | X | X | X | X | | | | X |

Multimodal-Fatima/VQAv2_test_split_8

--- dataset_info: features: - name: question_type dtype: string - name: multiple_choice_answer dtype: string - name: answers sequence: string - name: answers_original list: - name: answer dtype: string - name: answer_confidence dtype: string - name: answer_id dtype: int64 - name: id_image dtype: int64 - name: answer_type dtype: string - name: question_id dtype: int64 - name: question dtype: string - name: image dtype: image - name: id dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: blip_caption dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes list: - name: attribute dtype: string - name: box sequence: float32 - name: label dtype: string - name: location dtype: string - name: ratio dtype: float32 - name: size dtype: string - name: tag dtype: string - name: Attributes_ViT_L_14_descriptors_text_davinci_003_full sequence: string - name: clip_tags_ViT_L_14_wo_openai sequence: string - name: clip_tags_ViT_L_14_with_openai sequence: string - name: clip_tags_LAION_ViT_H_14_2B_wo_openai sequence: string - name: clip_tags_LAION_ViT_H_14_2B_with_openai sequence: string - name: clip_tags_LAION_ViT_bigG_14_2B_wo_openai sequence: string - name: clip_tags_LAION_ViT_bigG_14_2B_with_openai sequence: string - name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full sequence: string - name: Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full sequence: string - name: clip_tags_ViT_B_16_with_openai sequence: string - name: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random list: - name: attribute dtype: string - name: box sequence: float64 - name: captions_module sequence: string - name: captions_module_filter sequence: string - name: label dtype: string - name: location dtype: string - name: ratio dtype: float64 - name: size dtype: string - name: tag dtype: string splits: - name: test num_bytes: 9157441080.0 num_examples: 44779 download_size: 1848746963 dataset_size: 9157441080.0 --- # Dataset Card for "VQAv2_test_split_8" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

crumb/tiny-slimpajama-k8-00001

--- dataset_info: features: - name: text dtype: string - name: meta dtype: string - name: __index_level_0__ dtype: int64 - name: cluster dtype: int64 splits: - name: train num_bytes: 25963772232 num_examples: 5899634 download_size: 15090997326 dataset_size: 25963772232 configs: - config_name: default data_files: - split: train path: data/train-* ---

HSJuan/korquad-aug-valid

--- 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: pos_aug dtype: string - name: neg_aug dtype: string splits: - name: validation num_bytes: 9298037 num_examples: 5774 download_size: 1728913 dataset_size: 9298037 --- # Dataset Card for "korquad-aug-valid" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

tartuNLP/liv4ever

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en - liv license: - cc-by-nc-sa-4.0 multilinguality: - translation size_categories: - unknown source_datasets: - original task_categories: - text2text-generation - translation task_ids: [] pretty_name: Liv4ever language_bcp47: - en-US - liv tags: - conditional-text-generation --- # liv4ever v1 This is the Livonian 4-lingual parallel corpus. Livonian is a Uralic / Finnic language with just about 20 fluent speakers and no native speakers (as of 2021). The texts and translations in this corpus were collected from all the digital text resources that could be found by the authors; scanned and printed materials are left for future work. The corpus includes parallel data for Livonian-Latvian, Livonian-Estonian and Livonian-English; the data has been collected in 2021. After retrieval it was normalized in terms of different orthographies of Livonian and manually sentence-aligned where needed. It was collected from the following sources, with sentence counts per language pair: * Dictionary - example sentences from the Livonian-Latvian-Estonian dictionary; * liv-lv: 10'388, * liv-et: 10'378 * Stalte - the alphabet book by Kōrli Stalte, translated into Estonian and Latvian; * liv-lv: 842, * liv-et: 685 * Poetry - the poetry collection book "Ma võtan su õnge, tursk / Ma akūb sīnda vizzõ, tūrska", with Estonian translations; * liv-et: 770 * Vääri - the book by Eduard Vääri about Livonian language and culture; * liv-et: 592 * Satversme - translations of the Latvian Constitution into Livonian, Estonian and English; * liv-en: 380, * liv-lv: 414, * liv-et: 413 * Facebook - social media posts by the Livonian Institute and Livonian Days with original translations; * liv-en: 123, * liv-lv: 124, * liv-et: 7 * JEFUL - article abstracts from the Journal of Estonian and Finno-Ugric Linguistics, special issues dedicated to Livonian studies, translated into Estonian and English; * liv-en: 36, * liv-et: 49 * Trilium - the book with a collection of Livonian poetry, foreword and afterword translated into Estonian and Latvian; * liv-lv: 51, * liv-et: 53 * Songs - material crawled off lyricstranslate.com; * liv-en: 54, * liv-lv: 54, * liv-fr: 31

TuringsSolutions/PFAF-Function

--- license: mit ---

kaleemWaheed/twitter_dataset_1713138543

--- 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: 13116 num_examples: 32 download_size: 10247 dataset_size: 13116 configs: - config_name: default data_files: - split: train path: data/train-* ---