davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
Dialogue-Model-Research-Group/baike	--- license: cc ---
CyberHarem/kawashiro_mitori_touhou	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kawashiro_mitori/河城みとり (Touhou) This is the dataset of kawashiro_mitori/河城みとり (Touhou), containing 25 images and their tags. The core tags of this character are `hair_ornament, hat, short_hair, red_eyes, pink_hair, side_ponytail`, 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 \| 25 \| 21.45 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kawashiro_mitori_touhou/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 25 \| 15.45 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kawashiro_mitori_touhou/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 44 \| 26.74 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kawashiro_mitori_touhou/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 25 \| 20.40 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kawashiro_mitori_touhou/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 44 \| 31.98 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kawashiro_mitori_touhou/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/kawashiro_mitori_touhou', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------------------------------------------------\| \| 0 \| 25 \| ![](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) \| hair_bobbles, 1girl, solo, lock, layered_sleeves, blush, skirt, road_sign \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| hair_bobbles \| 1girl \| solo \| lock \| layered_sleeves \| blush \| skirt \| road_sign \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:---------------\|:--------\|:-------\|:-------\|:------------------\|:--------\|:--------\|:------------\| \| 0 \| 25 \| ![](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 \|
iElexperio/processedMorDataLLMv3	--- dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: bboxes sequence: sequence: int64 - name: ner_tags sequence: int64 - name: image dtype: image splits: - name: train num_bytes: 8868049.0 num_examples: 70 - name: test num_bytes: 3462408.0 num_examples: 28 download_size: 11436065 dataset_size: 12330457.0 --- # Dataset Card for "processedMorDataLLMv3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_beberik__TinyExperts-v0-4x1B	--- pretty_name: Evaluation run of beberik/TinyExperts-v0-4x1B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [beberik/TinyExperts-v0-4x1B](https://huggingface.co/beberik/TinyExperts-v0-4x1B)\ \ 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_beberik__TinyExperts-v0-4x1B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-20T21:54:19.124713](https://huggingface.co/datasets/open-llm-leaderboard/details_beberik__TinyExperts-v0-4x1B/blob/main/results_2023-12-20T21-54-19.124713.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\": 0.26252295554837873,\n\ \ \"acc_stderr\": 0.031072019491044735,\n \"acc_norm\": 0.2641174998312261,\n\ \ \"acc_norm_stderr\": 0.03186997959528178,\n \"mc1\": 0.24969400244798043,\n\ \ \"mc1_stderr\": 0.015152286907148128,\n \"mc2\": 0.41126558330324914,\n\ \ \"mc2_stderr\": 0.014912649441030584\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.27047781569965873,\n \"acc_stderr\": 0.012980954547659554,\n\ \ \"acc_norm\": 0.31399317406143346,\n \"acc_norm_stderr\": 0.013562691224726295\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.3906592312288389,\n\ \ \"acc_stderr\": 0.0048690101522807505,\n \"acc_norm\": 0.522903804023103,\n\ \ \"acc_norm_stderr\": 0.0049845435409323355\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.3333333333333333,\n\ \ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.3333333333333333,\n\ \ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.3223684210526316,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.3223684210526316,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.22,\n\ \ \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \ \ \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.20754716981132076,\n \"acc_stderr\": 0.024959918028911274,\n\ \ \"acc_norm\": 0.20754716981132076,\n \"acc_norm_stderr\": 0.024959918028911274\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2569444444444444,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653694,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653694\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|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-college_mathematics\|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-college_medicine\|5\": {\n \"acc\": 0.2543352601156069,\n\ \ \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.2543352601156069,\n\ \ \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.045766654032077636,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.045766654032077636\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.20425531914893616,\n \"acc_stderr\": 0.026355158413349424,\n\ \ \"acc_norm\": 0.20425531914893616,\n \"acc_norm_stderr\": 0.026355158413349424\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.24561403508771928,\n\ \ \"acc_stderr\": 0.04049339297748141,\n \"acc_norm\": 0.24561403508771928,\n\ \ \"acc_norm_stderr\": 0.04049339297748141\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.296551724137931,\n \"acc_stderr\": 0.03806142687309993,\n\ \ \"acc_norm\": 0.296551724137931,\n \"acc_norm_stderr\": 0.03806142687309993\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.2671957671957672,\n \"acc_stderr\": 0.022789673145776568,\n \"\ acc_norm\": 0.2671957671957672,\n \"acc_norm_stderr\": 0.022789673145776568\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.15873015873015872,\n\ \ \"acc_stderr\": 0.03268454013011744,\n \"acc_norm\": 0.15873015873015872,\n\ \ \"acc_norm_stderr\": 0.03268454013011744\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.25483870967741934,\n \"acc_stderr\": 0.02479011845933221,\n \"\ acc_norm\": 0.25483870967741934,\n \"acc_norm_stderr\": 0.02479011845933221\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.2955665024630542,\n \"acc_stderr\": 0.032104944337514575,\n \"\ acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.032104944337514575\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.2545454545454545,\n \"acc_stderr\": 0.03401506715249039,\n\ \ \"acc_norm\": 0.2545454545454545,\n \"acc_norm_stderr\": 0.03401506715249039\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.23737373737373738,\n \"acc_stderr\": 0.030313710538198892,\n \"\ acc_norm\": 0.23737373737373738,\n \"acc_norm_stderr\": 0.030313710538198892\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.22279792746113988,\n \"acc_stderr\": 0.03003114797764154,\n\ \ \"acc_norm\": 0.22279792746113988,\n \"acc_norm_stderr\": 0.03003114797764154\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.2282051282051282,\n \"acc_stderr\": 0.021278393863586282,\n\ \ \"acc_norm\": 0.2282051282051282,\n \"acc_norm_stderr\": 0.021278393863586282\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.26666666666666666,\n \"acc_stderr\": 0.02696242432507383,\n \ \ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.02696242432507383\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\ \ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2913907284768212,\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\"\ : 0.22385321100917432,\n \"acc_stderr\": 0.017871217767790222,\n \"\ acc_norm\": 0.22385321100917432,\n \"acc_norm_stderr\": 0.017871217767790222\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.029531221160930918,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.029531221160930918\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.03039153369274154\n \ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\"\ : 0.22784810126582278,\n \"acc_stderr\": 0.02730348459906941,\n \"\ acc_norm\": 0.22784810126582278,\n \"acc_norm_stderr\": 0.02730348459906941\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.29596412556053814,\n\ \ \"acc_stderr\": 0.030636591348699792,\n \"acc_norm\": 0.29596412556053814,\n\ \ \"acc_norm_stderr\": 0.030636591348699792\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.371900826446281,\n \"acc_stderr\": 0.044120158066245044,\n \"\ acc_norm\": 0.371900826446281,\n \"acc_norm_stderr\": 0.044120158066245044\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.21296296296296297,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.21296296296296297,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.3067484662576687,\n \"acc_stderr\": 0.036230899157241474,\n\ \ \"acc_norm\": 0.3067484662576687,\n \"acc_norm_stderr\": 0.036230899157241474\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.24107142857142858,\n\ \ \"acc_stderr\": 0.04059867246952687,\n \"acc_norm\": 0.24107142857142858,\n\ \ \"acc_norm_stderr\": 0.04059867246952687\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.20388349514563106,\n \"acc_stderr\": 0.03989139859531771,\n\ \ \"acc_norm\": 0.20388349514563106,\n \"acc_norm_stderr\": 0.03989139859531771\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.24358974358974358,\n\ \ \"acc_stderr\": 0.028120966503914397,\n \"acc_norm\": 0.24358974358974358,\n\ \ \"acc_norm_stderr\": 0.028120966503914397\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.26053639846743293,\n\ \ \"acc_stderr\": 0.0156960085638071,\n \"acc_norm\": 0.26053639846743293,\n\ \ \"acc_norm_stderr\": 0.0156960085638071\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.28901734104046245,\n \"acc_stderr\": 0.02440517393578323,\n\ \ \"acc_norm\": 0.28901734104046245,\n \"acc_norm_stderr\": 0.02440517393578323\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.24692737430167597,\n\ \ \"acc_stderr\": 0.014422292204808835,\n \"acc_norm\": 0.24692737430167597,\n\ \ \"acc_norm_stderr\": 0.014422292204808835\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.24836601307189543,\n \"acc_stderr\": 0.02473998135511359,\n\ \ \"acc_norm\": 0.24836601307189543,\n \"acc_norm_stderr\": 0.02473998135511359\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.2990353697749196,\n\ \ \"acc_stderr\": 0.026003301117885135,\n \"acc_norm\": 0.2990353697749196,\n\ \ \"acc_norm_stderr\": 0.026003301117885135\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.26851851851851855,\n \"acc_stderr\": 0.02465968518596728,\n\ \ \"acc_norm\": 0.26851851851851855,\n \"acc_norm_stderr\": 0.02465968518596728\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.2695035460992908,\n \"acc_stderr\": 0.02646903681859063,\n \ \ \"acc_norm\": 0.2695035460992908,\n \"acc_norm_stderr\": 0.02646903681859063\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.258148631029987,\n\ \ \"acc_stderr\": 0.01117692371931339,\n \"acc_norm\": 0.258148631029987,\n\ \ \"acc_norm_stderr\": 0.01117692371931339\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.23161764705882354,\n \"acc_stderr\": 0.025626533803777562,\n\ \ \"acc_norm\": 0.23161764705882354,\n \"acc_norm_stderr\": 0.025626533803777562\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.2581699346405229,\n \"acc_stderr\": 0.017704531653250078,\n \ \ \"acc_norm\": 0.2581699346405229,\n \"acc_norm_stderr\": 0.017704531653250078\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.20909090909090908,\n\ \ \"acc_stderr\": 0.038950910157241364,\n \"acc_norm\": 0.20909090909090908,\n\ \ \"acc_norm_stderr\": 0.038950910157241364\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.24489795918367346,\n \"acc_stderr\": 0.027529637440174923,\n\ \ \"acc_norm\": 0.24489795918367346,\n \"acc_norm_stderr\": 0.027529637440174923\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.23880597014925373,\n\ \ \"acc_stderr\": 0.030147775935409217,\n \"acc_norm\": 0.23880597014925373,\n\ \ \"acc_norm_stderr\": 0.030147775935409217\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.2289156626506024,\n\ \ \"acc_stderr\": 0.03270745277352477,\n \"acc_norm\": 0.2289156626506024,\n\ \ \"acc_norm_stderr\": 0.03270745277352477\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.27485380116959063,\n \"acc_stderr\": 0.034240429246915824,\n\ \ \"acc_norm\": 0.27485380116959063,\n \"acc_norm_stderr\": 0.034240429246915824\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.24969400244798043,\n\ \ \"mc1_stderr\": 0.015152286907148128,\n \"mc2\": 0.41126558330324914,\n\ \ \"mc2_stderr\": 0.014912649441030584\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.601420678768745,\n \"acc_stderr\": 0.01376035717687383\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.00530705079605762,\n \ \ \"acc_stderr\": 0.002001305720948071\n }\n}\n```" repo_url: https://huggingface.co/beberik/TinyExperts-v0-4x1B 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_12_20T21_54_19.124713 path: - '/details_harness\|arc:challenge\|25_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-20T21-54-19.124713.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|gsm8k\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hellaswag\|10_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-20T21-54-19.124713.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-20T21-54-19.124713.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '/details_harness\|winogrande\|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-20T21-54-19.124713.parquet' - config_name: results data_files: - split: 2023_12_20T21_54_19.124713 path: - results_2023-12-20T21-54-19.124713.parquet - split: latest path: - results_2023-12-20T21-54-19.124713.parquet --- # Dataset Card for Evaluation run of beberik/TinyExperts-v0-4x1B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [beberik/TinyExperts-v0-4x1B](https://huggingface.co/beberik/TinyExperts-v0-4x1B) 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_beberik__TinyExperts-v0-4x1B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-20T21:54:19.124713](https://huggingface.co/datasets/open-llm-leaderboard/details_beberik__TinyExperts-v0-4x1B/blob/main/results_2023-12-20T21-54-19.124713.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": 0.26252295554837873, "acc_stderr": 0.031072019491044735, "acc_norm": 0.2641174998312261, "acc_norm_stderr": 0.03186997959528178, "mc1": 0.24969400244798043, "mc1_stderr": 0.015152286907148128, "mc2": 0.41126558330324914, "mc2_stderr": 0.014912649441030584 }, "harness\|arc:challenge\|25": { "acc": 0.27047781569965873, "acc_stderr": 0.012980954547659554, "acc_norm": 0.31399317406143346, "acc_norm_stderr": 0.013562691224726295 }, "harness\|hellaswag\|10": { "acc": 0.3906592312288389, "acc_stderr": 0.0048690101522807505, "acc_norm": 0.522903804023103, "acc_norm_stderr": 0.0049845435409323355 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04072314811876837, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04072314811876837 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.3223684210526316, "acc_stderr": 0.03803510248351585, "acc_norm": 0.3223684210526316, "acc_norm_stderr": 0.03803510248351585 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.20754716981132076, "acc_stderr": 0.024959918028911274, "acc_norm": 0.20754716981132076, "acc_norm_stderr": 0.024959918028911274 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.18, "acc_stderr": 0.03861229196653694, "acc_norm": 0.18, "acc_norm_stderr": 0.03861229196653694 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.2543352601156069, "acc_stderr": 0.0332055644308557, "acc_norm": 0.2543352601156069, "acc_norm_stderr": 0.0332055644308557 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.30392156862745096, "acc_stderr": 0.045766654032077636, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.045766654032077636 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.20425531914893616, "acc_stderr": 0.026355158413349424, "acc_norm": 0.20425531914893616, "acc_norm_stderr": 0.026355158413349424 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.24561403508771928, "acc_stderr": 0.04049339297748141, "acc_norm": 0.24561403508771928, "acc_norm_stderr": 0.04049339297748141 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.296551724137931, "acc_stderr": 0.03806142687309993, "acc_norm": 0.296551724137931, "acc_norm_stderr": 0.03806142687309993 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2671957671957672, "acc_stderr": 0.022789673145776568, "acc_norm": 0.2671957671957672, "acc_norm_stderr": 0.022789673145776568 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.15873015873015872, "acc_stderr": 0.03268454013011744, "acc_norm": 0.15873015873015872, "acc_norm_stderr": 0.03268454013011744 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.25483870967741934, "acc_stderr": 0.02479011845933221, "acc_norm": 0.25483870967741934, "acc_norm_stderr": 0.02479011845933221 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.2955665024630542, "acc_stderr": 0.032104944337514575, "acc_norm": 0.2955665024630542, "acc_norm_stderr": 0.032104944337514575 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.2545454545454545, "acc_stderr": 0.03401506715249039, "acc_norm": 0.2545454545454545, "acc_norm_stderr": 0.03401506715249039 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.23737373737373738, "acc_stderr": 0.030313710538198892, "acc_norm": 0.23737373737373738, "acc_norm_stderr": 0.030313710538198892 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.22279792746113988, "acc_stderr": 0.03003114797764154, "acc_norm": 0.22279792746113988, "acc_norm_stderr": 0.03003114797764154 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.2282051282051282, "acc_stderr": 0.021278393863586282, "acc_norm": 0.2282051282051282, "acc_norm_stderr": 0.021278393863586282 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.26666666666666666, "acc_stderr": 0.02696242432507383, "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.02696242432507383 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.21008403361344538, "acc_stderr": 0.026461398717471874, "acc_norm": 0.21008403361344538, "acc_norm_stderr": 0.026461398717471874 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2913907284768212, "acc_stderr": 0.037101857261199946, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.037101857261199946 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.22385321100917432, "acc_stderr": 0.017871217767790222, "acc_norm": 0.22385321100917432, "acc_norm_stderr": 0.017871217767790222 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.25, "acc_stderr": 0.029531221160930918, "acc_norm": 0.25, "acc_norm_stderr": 0.029531221160930918 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.22784810126582278, "acc_stderr": 0.02730348459906941, "acc_norm": 0.22784810126582278, "acc_norm_stderr": 0.02730348459906941 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.29596412556053814, "acc_stderr": 0.030636591348699792, "acc_norm": 0.29596412556053814, "acc_norm_stderr": 0.030636591348699792 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.2595419847328244, "acc_stderr": 0.03844876139785271, "acc_norm": 0.2595419847328244, "acc_norm_stderr": 0.03844876139785271 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.371900826446281, "acc_stderr": 0.044120158066245044, "acc_norm": 0.371900826446281, "acc_norm_stderr": 0.044120158066245044 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.21296296296296297, "acc_stderr": 0.0395783547198098, "acc_norm": 0.21296296296296297, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.3067484662576687, "acc_stderr": 0.036230899157241474, "acc_norm": 0.3067484662576687, "acc_norm_stderr": 0.036230899157241474 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.24107142857142858, "acc_stderr": 0.04059867246952687, "acc_norm": 0.24107142857142858, "acc_norm_stderr": 0.04059867246952687 }, "harness\|hendrycksTest-management\|5": { "acc": 0.20388349514563106, "acc_stderr": 0.03989139859531771, "acc_norm": 0.20388349514563106, "acc_norm_stderr": 0.03989139859531771 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.24358974358974358, "acc_stderr": 0.028120966503914397, "acc_norm": 0.24358974358974358, "acc_norm_stderr": 0.028120966503914397 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.26053639846743293, "acc_stderr": 0.0156960085638071, "acc_norm": 0.26053639846743293, "acc_norm_stderr": 0.0156960085638071 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.28901734104046245, "acc_stderr": 0.02440517393578323, "acc_norm": 0.28901734104046245, "acc_norm_stderr": 0.02440517393578323 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.24692737430167597, "acc_stderr": 0.014422292204808835, "acc_norm": 0.24692737430167597, "acc_norm_stderr": 0.014422292204808835 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.24836601307189543, "acc_stderr": 0.02473998135511359, "acc_norm": 0.24836601307189543, "acc_norm_stderr": 0.02473998135511359 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.2990353697749196, "acc_stderr": 0.026003301117885135, "acc_norm": 0.2990353697749196, "acc_norm_stderr": 0.026003301117885135 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.26851851851851855, "acc_stderr": 0.02465968518596728, "acc_norm": 0.26851851851851855, "acc_norm_stderr": 0.02465968518596728 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2695035460992908, "acc_stderr": 0.02646903681859063, "acc_norm": 0.2695035460992908, "acc_norm_stderr": 0.02646903681859063 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.258148631029987, "acc_stderr": 0.01117692371931339, "acc_norm": 0.258148631029987, "acc_norm_stderr": 0.01117692371931339 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.23161764705882354, "acc_stderr": 0.025626533803777562, "acc_norm": 0.23161764705882354, "acc_norm_stderr": 0.025626533803777562 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.2581699346405229, "acc_stderr": 0.017704531653250078, "acc_norm": 0.2581699346405229, "acc_norm_stderr": 0.017704531653250078 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.20909090909090908, "acc_stderr": 0.038950910157241364, "acc_norm": 0.20909090909090908, "acc_norm_stderr": 0.038950910157241364 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.24489795918367346, "acc_stderr": 0.027529637440174923, "acc_norm": 0.24489795918367346, "acc_norm_stderr": 0.027529637440174923 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.23880597014925373, "acc_stderr": 0.030147775935409217, "acc_norm": 0.23880597014925373, "acc_norm_stderr": 0.030147775935409217 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.2289156626506024, "acc_stderr": 0.03270745277352477, "acc_norm": 0.2289156626506024, "acc_norm_stderr": 0.03270745277352477 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.27485380116959063, "acc_stderr": 0.034240429246915824, "acc_norm": 0.27485380116959063, "acc_norm_stderr": 0.034240429246915824 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.24969400244798043, "mc1_stderr": 0.015152286907148128, "mc2": 0.41126558330324914, "mc2_stderr": 0.014912649441030584 }, "harness\|winogrande\|5": { "acc": 0.601420678768745, "acc_stderr": 0.01376035717687383 }, "harness\|gsm8k\|5": { "acc": 0.00530705079605762, "acc_stderr": 0.002001305720948071 } } ``` ## 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]
CVdatasets/ImageNet15_animals_unbalanced_aug1	--- dataset_info: features: - name: labels dtype: class_label: names: '0': Italian_greyhound '1': Coyote '2': Beagle '3': Rottweiler '4': Hyena '5': Greater_Swiss_Mountain_dog '6': Triceratops '7': French_bulldog '8': Red_wolf '9': Egyptian_cat '10': Chihuahua '11': Irish_terrier '12': Tiger_cat '13': White_wolf '14': Timber_wolf - name: img dtype: image - name: is_generated dtype: bool splits: - name: validation num_bytes: 60570648.125 num_examples: 1439 - name: train num_bytes: 174270537.875 num_examples: 3705 download_size: 234762621 dataset_size: 234841186.0 --- # Dataset Card for "ImageNet15_animals_unbalanced_aug1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
umd-zhou-lab/Reflect_Wiz70_All	--- dataset_info: features: - name: data struct: - name: instruction dtype: string - name: output dtype: string splits: - name: origin num_bytes: 130900545 num_examples: 70000 - name: reflect_instruction num_bytes: 132137005 num_examples: 70000 - name: reflect_response num_bytes: 170505414 num_examples: 70000 - name: reflect_both num_bytes: 176166017 num_examples: 70000 download_size: 318571646 dataset_size: 609708981 --- # Dataset Card for "Reflect_Wiz70_All" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
KoddaDuck/41_4	--- license: mit ---
bigbio/bioinfer	--- language: - en bigbio_language: - English license: cc-by-2.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_2p0 pretty_name: BioInfer homepage: https://github.com/metalrt/ppi-dataset bigbio_pubmed: True bigbio_public: True bigbio_tasks: - RELATION_EXTRACTION - NAMED_ENTITY_RECOGNITION --- # Dataset Card for BioInfer ## Dataset Description - Homepage: https://github.com/metalrt/ppi-dataset - Pubmed: True - Public: True - Tasks: RE,NER A corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. ## Citation Information ``` @article{pyysalo2007bioinfer, title = {BioInfer: a corpus for information extraction in the biomedical domain}, author = { Pyysalo, Sampo and Ginter, Filip and Heimonen, Juho and Bj{"o}rne, Jari and Boberg, Jorma and J{"a}rvinen, Jouni and Salakoski, Tapio }, year = 2007, journal = {BMC bioinformatics}, publisher = {BioMed Central}, volume = 8, number = 1, pages = {1--24} } ```
cahya/instructions-hi	--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 47507115.72180105 num_examples: 49497 - name: test num_bytes: 1250616.639099476 num_examples: 1303 - name: validation num_bytes: 1250616.639099476 num_examples: 1303 download_size: 18697342 dataset_size: 50008349.00000001 --- # Dataset Card for "instructions-hi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
zolak/twitter_dataset_80_1713209820	--- 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: 1472271 num_examples: 3593 download_size: 720363 dataset_size: 1472271 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_Josephgflowers__3BigReasonCinder	--- pretty_name: Evaluation run of Josephgflowers/3BigReasonCinder dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Josephgflowers/3BigReasonCinder](https://huggingface.co/Josephgflowers/3BigReasonCinder)\ \ 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_Josephgflowers__3BigReasonCinder\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-09T12:31:38.090504](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__3BigReasonCinder/blob/main/results_2024-02-09T12-31-38.090504.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\": 0.44794266994933396,\n\ \ \"acc_stderr\": 0.03464503770381712,\n \"acc_norm\": 0.4507932379135084,\n\ \ \"acc_norm_stderr\": 0.03538213666564797,\n \"mc1\": 0.2876376988984088,\n\ \ \"mc1_stderr\": 0.015846315101394816,\n \"mc2\": 0.44764087589469737,\n\ \ \"mc2_stderr\": 0.014703779857331185\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.39078498293515357,\n \"acc_stderr\": 0.014258563880513778,\n\ \ \"acc_norm\": 0.41723549488054607,\n \"acc_norm_stderr\": 0.014409825518403082\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.4801832304321848,\n\ \ \"acc_stderr\": 0.004985860853427632,\n \"acc_norm\": 0.6515634335789683,\n\ \ \"acc_norm_stderr\": 0.004755013243022131\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4222222222222222,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.4222222222222222,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.4342105263157895,\n \"acc_stderr\": 0.040335656678483184,\n\ \ \"acc_norm\": 0.4342105263157895,\n \"acc_norm_stderr\": 0.040335656678483184\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\": {\n \"\ acc\": 0.5094339622641509,\n \"acc_stderr\": 0.030767394707808093,\n \ \ \"acc_norm\": 0.5094339622641509,\n \"acc_norm_stderr\": 0.030767394707808093\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4722222222222222,\n\ \ \"acc_stderr\": 0.04174752578923185,\n \"acc_norm\": 0.4722222222222222,\n\ \ \"acc_norm_stderr\": 0.04174752578923185\n },\n \"harness\|hendrycksTest-college_chemistry\|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-college_computer_science\|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_mathematics\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.3930635838150289,\n\ \ \"acc_stderr\": 0.03724249595817729,\n \"acc_norm\": 0.3930635838150289,\n\ \ \"acc_norm_stderr\": 0.03724249595817729\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.5,\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\": 0.37446808510638296,\n \"acc_stderr\": 0.03163910665367291,\n\ \ \"acc_norm\": 0.37446808510638296,\n \"acc_norm_stderr\": 0.03163910665367291\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813344,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813344\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.30158730158730157,\n \"acc_stderr\": 0.0236369759961018,\n \"\ acc_norm\": 0.30158730158730157,\n \"acc_norm_stderr\": 0.0236369759961018\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\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5096774193548387,\n\ \ \"acc_stderr\": 0.02843867799890955,\n \"acc_norm\": 0.5096774193548387,\n\ \ \"acc_norm_stderr\": 0.02843867799890955\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\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\ : 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.5454545454545454,\n \"acc_stderr\": 0.03888176921674101,\n\ \ \"acc_norm\": 0.5454545454545454,\n \"acc_norm_stderr\": 0.03888176921674101\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.5858585858585859,\n \"acc_stderr\": 0.03509438348879629,\n \"\ acc_norm\": 0.5858585858585859,\n \"acc_norm_stderr\": 0.03509438348879629\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.5699481865284974,\n \"acc_stderr\": 0.03572954333144808,\n\ \ \"acc_norm\": 0.5699481865284974,\n \"acc_norm_stderr\": 0.03572954333144808\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.3974358974358974,\n \"acc_stderr\": 0.024811920017903836,\n\ \ \"acc_norm\": 0.3974358974358974,\n \"acc_norm_stderr\": 0.024811920017903836\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.23333333333333334,\n \"acc_stderr\": 0.02578787422095931,\n \ \ \"acc_norm\": 0.23333333333333334,\n \"acc_norm_stderr\": 0.02578787422095931\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\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.6036697247706422,\n \"acc_stderr\": 0.02097146994790053,\n \"\ acc_norm\": 0.6036697247706422,\n \"acc_norm_stderr\": 0.02097146994790053\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.28703703703703703,\n \"acc_stderr\": 0.030851992993257013,\n \"\ acc_norm\": 0.28703703703703703,\n \"acc_norm_stderr\": 0.030851992993257013\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.5490196078431373,\n \"acc_stderr\": 0.03492406104163613,\n \"\ acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.03492406104163613\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.6075949367088608,\n \"acc_stderr\": 0.03178471874564729,\n \ \ \"acc_norm\": 0.6075949367088608,\n \"acc_norm_stderr\": 0.03178471874564729\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.47085201793721976,\n\ \ \"acc_stderr\": 0.03350073248773404,\n \"acc_norm\": 0.47085201793721976,\n\ \ \"acc_norm_stderr\": 0.03350073248773404\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.5648854961832062,\n \"acc_stderr\": 0.043482080516448585,\n\ \ \"acc_norm\": 0.5648854961832062,\n \"acc_norm_stderr\": 0.043482080516448585\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.628099173553719,\n \"acc_stderr\": 0.04412015806624505,\n \"acc_norm\"\ : 0.628099173553719,\n \"acc_norm_stderr\": 0.04412015806624505\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.5555555555555556,\n\ \ \"acc_stderr\": 0.04803752235190192,\n \"acc_norm\": 0.5555555555555556,\n\ \ \"acc_norm_stderr\": 0.04803752235190192\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.5276073619631901,\n \"acc_stderr\": 0.0392237829061099,\n\ \ \"acc_norm\": 0.5276073619631901,\n \"acc_norm_stderr\": 0.0392237829061099\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4107142857142857,\n\ \ \"acc_stderr\": 0.046695106638751906,\n \"acc_norm\": 0.4107142857142857,\n\ \ \"acc_norm_stderr\": 0.046695106638751906\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.5631067961165048,\n \"acc_stderr\": 0.049111471073657764,\n\ \ \"acc_norm\": 0.5631067961165048,\n \"acc_norm_stderr\": 0.049111471073657764\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.6623931623931624,\n\ \ \"acc_stderr\": 0.030980296992618558,\n \"acc_norm\": 0.6623931623931624,\n\ \ \"acc_norm_stderr\": 0.030980296992618558\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.5389527458492975,\n\ \ \"acc_stderr\": 0.017825621793239012,\n \"acc_norm\": 0.5389527458492975,\n\ \ \"acc_norm_stderr\": 0.017825621793239012\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.4884393063583815,\n \"acc_stderr\": 0.026911898686377913,\n\ \ \"acc_norm\": 0.4884393063583815,\n \"acc_norm_stderr\": 0.026911898686377913\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.28044692737430166,\n\ \ \"acc_stderr\": 0.015024083883322891,\n \"acc_norm\": 0.28044692737430166,\n\ \ \"acc_norm_stderr\": 0.015024083883322891\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.4803921568627451,\n \"acc_stderr\": 0.028607893699576066,\n\ \ \"acc_norm\": 0.4803921568627451,\n \"acc_norm_stderr\": 0.028607893699576066\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.49517684887459806,\n\ \ \"acc_stderr\": 0.028396770444111298,\n \"acc_norm\": 0.49517684887459806,\n\ \ \"acc_norm_stderr\": 0.028396770444111298\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.027648477877413327,\n\ \ \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.027648477877413327\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.3829787234042553,\n \"acc_stderr\": 0.028999080904806178,\n \ \ \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.028999080904806178\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.3272490221642764,\n\ \ \"acc_stderr\": 0.01198381980646473,\n \"acc_norm\": 0.3272490221642764,\n\ \ \"acc_norm_stderr\": 0.01198381980646473\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.33455882352941174,\n \"acc_stderr\": 0.028661996202335307,\n\ \ \"acc_norm\": 0.33455882352941174,\n \"acc_norm_stderr\": 0.028661996202335307\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.41830065359477125,\n \"acc_stderr\": 0.019955975145835542,\n \ \ \"acc_norm\": 0.41830065359477125,\n \"acc_norm_stderr\": 0.019955975145835542\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.509090909090909,\n\ \ \"acc_stderr\": 0.04788339768702861,\n \"acc_norm\": 0.509090909090909,\n\ \ \"acc_norm_stderr\": 0.04788339768702861\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.5346938775510204,\n \"acc_stderr\": 0.03193207024425314,\n\ \ \"acc_norm\": 0.5346938775510204,\n \"acc_norm_stderr\": 0.03193207024425314\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.6567164179104478,\n\ \ \"acc_stderr\": 0.03357379665433431,\n \"acc_norm\": 0.6567164179104478,\n\ \ \"acc_norm_stderr\": 0.03357379665433431\n },\n \"harness\|hendrycksTest-us_foreign_policy\|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-virology\|5\": {\n \"acc\": 0.4457831325301205,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.4457831325301205,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.5263157894736842,\n \"acc_stderr\": 0.038295098689947266,\n\ \ \"acc_norm\": 0.5263157894736842,\n \"acc_norm_stderr\": 0.038295098689947266\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.2876376988984088,\n\ \ \"mc1_stderr\": 0.015846315101394816,\n \"mc2\": 0.44764087589469737,\n\ \ \"mc2_stderr\": 0.014703779857331185\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.6495659037095501,\n \"acc_stderr\": 0.01340904767667018\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.2759666413949962,\n \ \ \"acc_stderr\": 0.012312603010427352\n }\n}\n```" repo_url: https://huggingface.co/Josephgflowers/3BigReasonCinder 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_09T12_31_38.090504 path: - '/details_harness\|arc:challenge\|25_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-09T12-31-38.090504.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|gsm8k\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hellaswag\|10_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T12-31-38.090504.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-09T12-31-38.090504.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '/details_harness\|winogrande\|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-09T12-31-38.090504.parquet' - config_name: results data_files: - split: 2024_02_09T12_31_38.090504 path: - results_2024-02-09T12-31-38.090504.parquet - split: latest path: - results_2024-02-09T12-31-38.090504.parquet --- # Dataset Card for Evaluation run of Josephgflowers/3BigReasonCinder <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Josephgflowers/3BigReasonCinder](https://huggingface.co/Josephgflowers/3BigReasonCinder) 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_Josephgflowers__3BigReasonCinder", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-09T12:31:38.090504](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__3BigReasonCinder/blob/main/results_2024-02-09T12-31-38.090504.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": 0.44794266994933396, "acc_stderr": 0.03464503770381712, "acc_norm": 0.4507932379135084, "acc_norm_stderr": 0.03538213666564797, "mc1": 0.2876376988984088, "mc1_stderr": 0.015846315101394816, "mc2": 0.44764087589469737, "mc2_stderr": 0.014703779857331185 }, "harness\|arc:challenge\|25": { "acc": 0.39078498293515357, "acc_stderr": 0.014258563880513778, "acc_norm": 0.41723549488054607, "acc_norm_stderr": 0.014409825518403082 }, "harness\|hellaswag\|10": { "acc": 0.4801832304321848, "acc_stderr": 0.004985860853427632, "acc_norm": 0.6515634335789683, "acc_norm_stderr": 0.004755013243022131 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4222222222222222, "acc_stderr": 0.04266763404099582, "acc_norm": 0.4222222222222222, "acc_norm_stderr": 0.04266763404099582 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.4342105263157895, "acc_stderr": 0.040335656678483184, "acc_norm": 0.4342105263157895, "acc_norm_stderr": 0.040335656678483184 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5094339622641509, "acc_stderr": 0.030767394707808093, "acc_norm": 0.5094339622641509, "acc_norm_stderr": 0.030767394707808093 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4722222222222222, "acc_stderr": 0.04174752578923185, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.04174752578923185 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.31, "acc_stderr": 0.046482319871173156, "acc_norm": 0.31, "acc_norm_stderr": 0.046482319871173156 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.3930635838150289, "acc_stderr": 0.03724249595817729, "acc_norm": 0.3930635838150289, "acc_norm_stderr": 0.03724249595817729 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.37446808510638296, "acc_stderr": 0.03163910665367291, "acc_norm": 0.37446808510638296, "acc_norm_stderr": 0.03163910665367291 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813344, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813344 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.4689655172413793, "acc_stderr": 0.04158632762097828, "acc_norm": 0.4689655172413793, "acc_norm_stderr": 0.04158632762097828 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.30158730158730157, "acc_stderr": 0.0236369759961018, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.0236369759961018 }, "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": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5096774193548387, "acc_stderr": 0.02843867799890955, "acc_norm": 0.5096774193548387, "acc_norm_stderr": 0.02843867799890955 }, "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": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.5454545454545454, "acc_stderr": 0.03888176921674101, "acc_norm": 0.5454545454545454, "acc_norm_stderr": 0.03888176921674101 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.5858585858585859, "acc_stderr": 0.03509438348879629, "acc_norm": 0.5858585858585859, "acc_norm_stderr": 0.03509438348879629 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.5699481865284974, "acc_stderr": 0.03572954333144808, "acc_norm": 0.5699481865284974, "acc_norm_stderr": 0.03572954333144808 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.3974358974358974, "acc_stderr": 0.024811920017903836, "acc_norm": 0.3974358974358974, "acc_norm_stderr": 0.024811920017903836 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.23333333333333334, "acc_stderr": 0.02578787422095931, "acc_norm": 0.23333333333333334, "acc_norm_stderr": 0.02578787422095931 }, "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": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.6036697247706422, "acc_stderr": 0.02097146994790053, "acc_norm": 0.6036697247706422, "acc_norm_stderr": 0.02097146994790053 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.28703703703703703, "acc_stderr": 0.030851992993257013, "acc_norm": 0.28703703703703703, "acc_norm_stderr": 0.030851992993257013 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.5490196078431373, "acc_stderr": 0.03492406104163613, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.03492406104163613 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.6075949367088608, "acc_stderr": 0.03178471874564729, "acc_norm": 0.6075949367088608, "acc_norm_stderr": 0.03178471874564729 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.47085201793721976, "acc_stderr": 0.03350073248773404, "acc_norm": 0.47085201793721976, "acc_norm_stderr": 0.03350073248773404 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5648854961832062, "acc_stderr": 0.043482080516448585, "acc_norm": 0.5648854961832062, "acc_norm_stderr": 0.043482080516448585 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.628099173553719, "acc_stderr": 0.04412015806624505, "acc_norm": 0.628099173553719, "acc_norm_stderr": 0.04412015806624505 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04803752235190192, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04803752235190192 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.5276073619631901, "acc_stderr": 0.0392237829061099, "acc_norm": 0.5276073619631901, "acc_norm_stderr": 0.0392237829061099 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4107142857142857, "acc_stderr": 0.046695106638751906, "acc_norm": 0.4107142857142857, "acc_norm_stderr": 0.046695106638751906 }, "harness\|hendrycksTest-management\|5": { "acc": 0.5631067961165048, "acc_stderr": 0.049111471073657764, "acc_norm": 0.5631067961165048, "acc_norm_stderr": 0.049111471073657764 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.6623931623931624, "acc_stderr": 0.030980296992618558, "acc_norm": 0.6623931623931624, "acc_norm_stderr": 0.030980296992618558 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.5389527458492975, "acc_stderr": 0.017825621793239012, "acc_norm": 0.5389527458492975, "acc_norm_stderr": 0.017825621793239012 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.4884393063583815, "acc_stderr": 0.026911898686377913, "acc_norm": 0.4884393063583815, "acc_norm_stderr": 0.026911898686377913 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.28044692737430166, "acc_stderr": 0.015024083883322891, "acc_norm": 0.28044692737430166, "acc_norm_stderr": 0.015024083883322891 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.4803921568627451, "acc_stderr": 0.028607893699576066, "acc_norm": 0.4803921568627451, "acc_norm_stderr": 0.028607893699576066 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.49517684887459806, "acc_stderr": 0.028396770444111298, "acc_norm": 0.49517684887459806, "acc_norm_stderr": 0.028396770444111298 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.027648477877413327, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.027648477877413327 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3829787234042553, "acc_stderr": 0.028999080904806178, "acc_norm": 0.3829787234042553, "acc_norm_stderr": 0.028999080904806178 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.3272490221642764, "acc_stderr": 0.01198381980646473, "acc_norm": 0.3272490221642764, "acc_norm_stderr": 0.01198381980646473 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.33455882352941174, "acc_stderr": 0.028661996202335307, "acc_norm": 0.33455882352941174, "acc_norm_stderr": 0.028661996202335307 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.41830065359477125, "acc_stderr": 0.019955975145835542, "acc_norm": 0.41830065359477125, "acc_norm_stderr": 0.019955975145835542 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.509090909090909, "acc_stderr": 0.04788339768702861, "acc_norm": 0.509090909090909, "acc_norm_stderr": 0.04788339768702861 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5346938775510204, "acc_stderr": 0.03193207024425314, "acc_norm": 0.5346938775510204, "acc_norm_stderr": 0.03193207024425314 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6567164179104478, "acc_stderr": 0.03357379665433431, "acc_norm": 0.6567164179104478, "acc_norm_stderr": 0.03357379665433431 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4457831325301205, "acc_stderr": 0.03869543323472101, "acc_norm": 0.4457831325301205, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.5263157894736842, "acc_stderr": 0.038295098689947266, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.038295098689947266 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.2876376988984088, "mc1_stderr": 0.015846315101394816, "mc2": 0.44764087589469737, "mc2_stderr": 0.014703779857331185 }, "harness\|winogrande\|5": { "acc": 0.6495659037095501, "acc_stderr": 0.01340904767667018 }, "harness\|gsm8k\|5": { "acc": 0.2759666413949962, "acc_stderr": 0.012312603010427352 } } ``` ## 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]
autoevaluate/autoeval-eval-lener_br-lener_br-c186f5-1776861659	--- type: predictions tags: - autotrain - evaluation datasets: - lener_br eval_info: task: entity_extraction model: Luciano/bertimbau-base-lener_br metrics: [] dataset_name: lener_br dataset_config: lener_br dataset_split: train col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: Luciano/bertimbau-base-lener_br * Dataset: lener_br * Config: lener_br * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.
HeTree/MevakerConcSen	--- license: apache-2.0 language: - he --- ## MevakerConcSen A sentence-level dataset for sentence-level conclusion extraction which provides a label of conclusion/not conclusion (1/0 respectivly) for each sentence together with indexes of the sentence and their document of origin. ### Citing If you use MevakerConcSen in your research, please cite [Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Language](https://arxiv.org/abs/2403.09719). ``` @article{shalumov2024mevaker, title={Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Language}, author={Vitaly Shalumov and Harel Haskey and Yuval Solaz}, year={2024}, eprint={2403.09719}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
karukas/arxiv-abstract-matching	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: train num_bytes: 7119340064 num_examples: 203037 - name: validation num_bytes: 216202656 num_examples: 6436 - name: test num_bytes: 216585242 num_examples: 6440 download_size: 3635681697 dataset_size: 7552127962 --- # Dataset Card for "arxiv-abstract-matching" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DazMashaly/test_fake_labels	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: image dtype: image splits: - name: test num_bytes: 365263950.94 num_examples: 5108 download_size: 354753479 dataset_size: 365263950.94 --- # Dataset Card for "test_fake_labels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dukkkk/test	--- annotations_creators: [] language_creators: [] language: - zh license: - apache-2.0 multilinguality: - monolingual pretty_name: Wenetspeech4TTS source_datasets: [] task_categories: - automatic-speech-recognition - text-to-speech - text-to-audio extra_gated_prompt: >- We do not own the copyright of the audio files. For researchers and educators who wish to use the audio files for non-commercial research and/or educational purposes, we can provide access through the Hub under certain conditions and terms. Terms of Access: The Researcher has requested permission to use the WenetSpeech4TTS database. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. The authors make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the authors of WenetSpeech4TTS, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database. 4.Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. The authors reserve the right to terminate Researcher's access to the Database at any time. 6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. extra_gated_fields: Name: text Email: text Organization: text Address: text I hereby confirm that I have requested access via the Google Form provided above: checkbox I accept the terms of access: checkbox size_categories: - 1M<n<10M --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## 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]
nespc/cnn_dailymail_prompts	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1354728397 num_examples: 287113 - name: test num_bytes: 53648492 num_examples: 11490 download_size: 781011544 dataset_size: 1408376889 --- # Dataset Card for "cnn_dailymail_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Verah/JParaCrawl-Filtered-English-Japanese-Parallel-Corpus	--- license: other license_name: ntt-research license_link: https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/ task_categories: - translation language: - en - ja size_categories: - 1M<n<10M --- # Introduction This is a LLM-filtered set of the first 1M rows from ntt's JParaCrawl v3 large English-Japanese parallel corpus. The original JParaCrawl corpus was put together by automated means - aligning Japanese texts with their apparent English translations that were found in-the-wild, on the internet. Whilst manually browsing the original data, I noticed that there were obvious quality issues that made me anxious about using the dataset at all. Poorly aligned translations, incomplete translations, etc. The goal of this dataset is to split the entire original dataset into its good and bad parts to: - facilitate further research - make available a high quality dataset - investigate the performance of various LLMs at evaluating the dataset. The new upload includes filtering by an additional LLM, its results are "model2_accepted": https://huggingface.co/Verah/mistral-japanese-stabalelm-merge I merged mistral instruct with stability AI's new japanese LLM, and this seems to have resulted in a model with enough knowledge of english and japanese to be competent at this task. It is likely that a finetune would further improve results. This new model accepted only 260,058 rows from the 1M seen, whilst the previous model was around twice as permissive. Prompt used with the new model: ```python from inspect import cleandoc def promptgen_mistral(japanese :str, english :str) -> str: system_prompt = cleandoc("""<s>[INST]Your role is to evaluate the accuracy of the provided Japanese to English translation. - Translations with parts missing should be rejected. - Incomplete translations should be rejected. - Inaccurate translations should be rejected. - Poor grammar should be rejected. - Any kind of mistake should be rejected. - Bad spelling should be rejected. - Low quality english should be rejected. - Low quality japanese should be rejected. - high quality translations should be accepted. - Respond with only 'ACCEPT' or 'REJECT'. """) return system_prompt + f"JAPANESE: {japanese}\nENGLISH: {english}[/INST]\n" ``` # License The license is identical to the original JParaCrawl dataset: ``` Terms of Use for Bilingual Data, Monolingual Data and Trained Models Nippon Telegraph and Telephone Corporation (Hereinafter referred to as "our company".) will provide bilingual data, monolingual data and trained models (Hereinafter referred to as "this data.") subject to your acceptance of these Terms of Use. We assume that you have agreed to these Terms of Use when you start using this data (including downloads). Article 1 (Use conditions) This data can only be used for research purposes involving information analysis (Including, but not limited to, replication and distribution. Hereinafter the same in this article.). The same applies to the derived data created based on this data. However, this data is not available for commercial use, including the sale of translators trained using this data. Article 2 (Disclaimer) Our company does not warrant the quality, performance or any other aspects of this data. We shall not be liable for any direct or indirect damages caused by the use of this data. Our company shall not be liable for any damage to the system caused by the installation of this data. Article 3 (Other). This data may be changed in whole or in part, or provision of this data may be interrupted or stopped at our company’s discretion without prior notice. ========== 対訳データ，単言語データおよび学習済みモデル利用に関する利用規約日本電信電話株式会社（以下、「当社」という。）は、本利用規約に同意されることを条件として、対訳データ、単言語データおよび学習済みモデル（以下、「本データ」という。）を提供します。なお、本データの利用（ダウンロードも含む）を開始した時点で、本利用規約にご同意頂いたものとみなします。第1条（利用条件）本データは、情報解析を伴う研究開発目的にのみご利用（複製および配布を含むが、それに限らない。以下、同じ）頂けます。本データを基に作成された派生データについても同様です。ただし、本データを使って学習したデータを内蔵した翻訳機の販売等を含む商用利用目的には、ご利用頂けません。第2条（免責）当社は、本データについて、品質、性能その他一切の保証を行うものではありません。２.直接的損害、間接的損害を問わず、本データの利用によって生ずるいかなる損害についても、一切の責任を負いません。当社は、本データのインストール作業等によって発生するシステムへの影響等、損害についても、一切の責任を負いません。第3条（その他）事前通知なしに、当社の判断によって、本データを全部または一部の変更、本データの提供の中断または停止をさせて頂くことがございます。 ```
maximalmargin/katz	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1400704.0 num_examples: 26 download_size: 1402106 dataset_size: 1400704.0 --- # Dataset Card for "katz" Images from [Alex Katz](https://www.alexkatz.com/)'s Print Archive. Hand-written image descriptions. Please use responsibly.
bigbio/bioasq_2021_mesinesp	--- language: - es bigbio_language: - Spanish license: cc-by-4.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_4p0 pretty_name: MESINESP 2021 homepage: https://zenodo.org/record/5602914#.YhSXJ5PMKWt bigbio_pubmed: False bigbio_public: True bigbio_tasks: - TEXT_CLASSIFICATION --- # Dataset Card for MESINESP 2021 ## Dataset Description - Homepage: https://zenodo.org/record/5602914#.YhSXJ5PMKWt - Pubmed: False - Public: True - Tasks: TXTCLASS The main aim of MESINESP2 is to promote the development of practically relevant semantic indexing tools for biomedical content in non-English language. We have generated a manually annotated corpus, where domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts. All the documents were labeled with DeCS descriptors, which is a structured controlled vocabulary created by BIREME to index scientific publications on BvSalud, the largest database of scientific documents in Spanish, which hosts records from the databases LILACS, MEDLINE, IBECS, among others. MESINESP track at BioASQ9 explores the efficiency of systems for assigning DeCS to different types of biomedical documents. To that purpose, we have divided the task into three subtracks depending on the document type. Then, for each one we generated an annotated corpus which was provided to participating teams: - [Subtrack 1 corpus] MESINESP-L – Scientific Literature: It contains all Spanish records from LILACS and IBECS databases at the Virtual Health Library (VHL) with non-empty abstract written in Spanish. - [Subtrack 2 corpus] MESINESP-T- Clinical Trials contains records from Registro Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the structure title/abstract needed in BioASQ, for that reason we have built artificial abstracts based on the content available in the data crawled using the REEC API. - [Subtrack 3 corpus] MESINESP-P – Patents: This corpus includes patents in Spanish extracted from Google Patents which have the IPC code “A61P” and “A61K31”. In addition, we also provide a set of complementary data such as: the DeCS terminology file, a silver standard with the participants' predictions to the task background set and the entities of medications, diseases, symptoms and medical procedures extracted from the BSC NERs documents. ## Citation Information ``` @conference {396, title = {Overview of BioASQ 2021-MESINESP track. Evaluation of advance hierarchical classification techniques for scientific literature, patents and clinical trials.}, booktitle = {Proceedings of the 9th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering}, year = {2021}, url = {http://ceur-ws.org/Vol-2936/paper-11.pdf}, author = {Gasco, Luis and Nentidis, Anastasios and Krithara, Anastasia and Estrada-Zavala, Darryl and Toshiyuki Murasaki, Renato and Primo-Pe{\~n}a, Elena and Bojo-Canales, Cristina and Paliouras, Georgios and Krallinger, Martin} } ```
Iania/QA_setup	--- license: apache-2.0 ---
roa7n/patched_test_p_10_m1_predictions_v2	--- dataset_info: features: - name: id dtype: string - name: sequence_str dtype: string - name: label dtype: int64 - name: m1_preds dtype: float32 splits: - name: train num_bytes: 1566287530 num_examples: 2843834 download_size: 138365947 dataset_size: 1566287530 --- # Dataset Card for "patched_test_p_10_m1_predictions_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_dillfrescott__amadeus-v0.1	--- pretty_name: Evaluation run of dillfrescott/amadeus-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [dillfrescott/amadeus-v0.1](https://huggingface.co/dillfrescott/amadeus-v0.1)\ \ 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_dillfrescott__amadeus-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-06T01:28:19.231223](https://huggingface.co/datasets/open-llm-leaderboard/details_dillfrescott__amadeus-v0.1/blob/main/results_2024-01-06T01-28-19.231223.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\": 0.6503116864878898,\n\ \ \"acc_stderr\": 0.03211845742165151,\n \"acc_norm\": 0.6514191578913137,\n\ \ \"acc_norm_stderr\": 0.032765902396781794,\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6382334765973684,\n\ \ \"mc2_stderr\": 0.01550846970253108\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6578498293515358,\n \"acc_stderr\": 0.013864152159177278,\n\ \ \"acc_norm\": 0.689419795221843,\n \"acc_norm_stderr\": 0.013522292098053069\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6957777335192192,\n\ \ \"acc_stderr\": 0.004591369853276529,\n \"acc_norm\": 0.8698466440948018,\n\ \ \"acc_norm_stderr\": 0.0033578442491239546\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249386,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249386\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|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-college_mathematics\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952344,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952344\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.03208115750788684,\n\ \ \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.03208115750788684\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878151,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878151\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4021164021164021,\n \"acc_stderr\": 0.025253032554997695,\n \"\ acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.025253032554997695\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.48412698412698413,\n\ \ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\ \ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness\|hendrycksTest-global_facts\|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-high_school_biology\|5\": {\n \"acc\"\ : 0.8,\n \"acc_stderr\": 0.022755204959542943,\n \"acc_norm\": 0.8,\n\ \ \"acc_norm_stderr\": 0.022755204959542943\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\ \ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768766,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768766\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \ \ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.02874204090394848,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.02874204090394848\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7100840336134454,\n \"acc_stderr\": 0.029472485833136098,\n\ \ \"acc_norm\": 0.7100840336134454,\n \"acc_norm_stderr\": 0.029472485833136098\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\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\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n\ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6995515695067265,\n\ \ \"acc_stderr\": 0.03076935200822914,\n \"acc_norm\": 0.6995515695067265,\n\ \ \"acc_norm_stderr\": 0.03076935200822914\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\ \ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\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\": 0.7962962962962963,\n\ \ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\ \ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077802,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077802\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8314176245210728,\n\ \ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\ \ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545543,\n\ \ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545543\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.43687150837988825,\n\ \ \"acc_stderr\": 0.01658868086453063,\n \"acc_norm\": 0.43687150837988825,\n\ \ \"acc_norm_stderr\": 0.01658868086453063\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137904,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137904\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6977491961414791,\n\ \ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\ \ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.024383665531035454,\n\ \ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.024383665531035454\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829727,\n \ \ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829727\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4706649282920469,\n\ \ \"acc_stderr\": 0.012748238397365549,\n \"acc_norm\": 0.4706649282920469,\n\ \ \"acc_norm_stderr\": 0.012748238397365549\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.027778298701545443,\n\ \ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.027778298701545443\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6503267973856209,\n \"acc_stderr\": 0.01929196189506638,\n \ \ \"acc_norm\": 0.6503267973856209,\n \"acc_norm_stderr\": 0.01929196189506638\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\ \ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.025196929874827075,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.025196929874827075\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\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\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6382334765973684,\n\ \ \"mc2_stderr\": 0.01550846970253108\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7995264404104183,\n \"acc_stderr\": 0.01125195828120508\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6413949962092494,\n \ \ \"acc_stderr\": 0.013210317364134031\n }\n}\n```" repo_url: https://huggingface.co/dillfrescott/amadeus-v0.1 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_06T01_28_19.231223 path: - '/details_harness\|arc:challenge\|25_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-06T01-28-19.231223.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|gsm8k\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hellaswag\|10_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T01-28-19.231223.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-06T01-28-19.231223.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '/details_harness\|winogrande\|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-06T01-28-19.231223.parquet' - config_name: results data_files: - split: 2024_01_06T01_28_19.231223 path: - results_2024-01-06T01-28-19.231223.parquet - split: latest path: - results_2024-01-06T01-28-19.231223.parquet --- # Dataset Card for Evaluation run of dillfrescott/amadeus-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [dillfrescott/amadeus-v0.1](https://huggingface.co/dillfrescott/amadeus-v0.1) 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_dillfrescott__amadeus-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-06T01:28:19.231223](https://huggingface.co/datasets/open-llm-leaderboard/details_dillfrescott__amadeus-v0.1/blob/main/results_2024-01-06T01-28-19.231223.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": 0.6503116864878898, "acc_stderr": 0.03211845742165151, "acc_norm": 0.6514191578913137, "acc_norm_stderr": 0.032765902396781794, "mc1": 0.46266829865361075, "mc1_stderr": 0.017454645150970588, "mc2": 0.6382334765973684, "mc2_stderr": 0.01550846970253108 }, "harness\|arc:challenge\|25": { "acc": 0.6578498293515358, "acc_stderr": 0.013864152159177278, "acc_norm": 0.689419795221843, "acc_norm_stderr": 0.013522292098053069 }, "harness\|hellaswag\|10": { "acc": 0.6957777335192192, "acc_stderr": 0.004591369853276529, "acc_norm": 0.8698466440948018, "acc_norm_stderr": 0.0033578442491239546 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7018867924528301, "acc_stderr": 0.02815283794249386, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.02815283794249386 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.047609522856952344, "acc_norm": 0.34, "acc_norm_stderr": 0.047609522856952344 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5957446808510638, "acc_stderr": 0.03208115750788684, "acc_norm": 0.5957446808510638, "acc_norm_stderr": 0.03208115750788684 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878151, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878151 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4021164021164021, "acc_stderr": 0.025253032554997695, "acc_norm": 0.4021164021164021, "acc_norm_stderr": 0.025253032554997695 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8, "acc_stderr": 0.022755204959542943, "acc_norm": 0.8, "acc_norm_stderr": 0.022755204959542943 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7828282828282829, "acc_stderr": 0.02937661648494563, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.02937661648494563 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768766, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768766 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6743589743589744, "acc_stderr": 0.02375966576741229, "acc_norm": 0.6743589743589744, "acc_norm_stderr": 0.02375966576741229 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.02874204090394848, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.02874204090394848 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7100840336134454, "acc_stderr": 0.029472485833136098, "acc_norm": 0.7100840336134454, "acc_norm_stderr": 0.029472485833136098 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5277777777777778, "acc_stderr": 0.0340470532865388, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.0340470532865388 }, "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": 0.810126582278481, "acc_stderr": 0.02553010046023349, "acc_norm": 0.810126582278481, "acc_norm_stderr": 0.02553010046023349 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6995515695067265, "acc_stderr": 0.03076935200822914, "acc_norm": 0.6995515695067265, "acc_norm_stderr": 0.03076935200822914 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7633587786259542, "acc_stderr": 0.03727673575596913, "acc_norm": 0.7633587786259542, "acc_norm_stderr": 0.03727673575596913 }, "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": 0.7962962962962963, "acc_stderr": 0.03893542518824847, "acc_norm": 0.7962962962962963, "acc_norm_stderr": 0.03893542518824847 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7852760736196319, "acc_stderr": 0.032262193772867744, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.032262193772867744 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077802, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077802 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7456647398843931, "acc_stderr": 0.023445826276545543, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.023445826276545543 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.43687150837988825, "acc_stderr": 0.01658868086453063, "acc_norm": 0.43687150837988825, "acc_norm_stderr": 0.01658868086453063 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137904, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137904 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6977491961414791, "acc_stderr": 0.02608270069539966, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.02608270069539966 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.024383665531035454, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.024383665531035454 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.475177304964539, "acc_stderr": 0.029790719243829727, "acc_norm": 0.475177304964539, "acc_norm_stderr": 0.029790719243829727 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4706649282920469, "acc_stderr": 0.012748238397365549, "acc_norm": 0.4706649282920469, "acc_norm_stderr": 0.012748238397365549 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7022058823529411, "acc_stderr": 0.027778298701545443, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.027778298701545443 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6503267973856209, "acc_stderr": 0.01929196189506638, "acc_norm": 0.6503267973856209, "acc_norm_stderr": 0.01929196189506638 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7224489795918367, "acc_stderr": 0.028666857790274648, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274648 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8507462686567164, "acc_stderr": 0.025196929874827075, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.025196929874827075 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "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": 0.46266829865361075, "mc1_stderr": 0.017454645150970588, "mc2": 0.6382334765973684, "mc2_stderr": 0.01550846970253108 }, "harness\|winogrande\|5": { "acc": 0.7995264404104183, "acc_stderr": 0.01125195828120508 }, "harness\|gsm8k\|5": { "acc": 0.6413949962092494, "acc_stderr": 0.013210317364134031 } } ``` ## 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]
ctu-aic/qacg-sum	--- dataset_info: - config_name: balanced features: - name: claim dtype: string - name: label dtype: string - name: evidence sequence: string - name: lang dtype: string - name: orig_idx dtype: int64 splits: - name: train num_bytes: 130783710 num_examples: 1180836 - name: validation num_bytes: 13391571 num_examples: 120348 - name: test num_bytes: 12599211 num_examples: 113760 download_size: 114959179 dataset_size: 156774492 - config_name: balanced_shuf features: - name: claim dtype: string - name: label dtype: string - name: evidence sequence: string - name: lang dtype: string - name: orig_idx dtype: int64 splits: - name: train num_bytes: 81658504 num_examples: 741542 - name: validation num_bytes: 8349339 num_examples: 75573 - name: test num_bytes: 7871047 num_examples: 71607 download_size: 71188503 dataset_size: 97878890 configs: - config_name: balanced data_files: - split: train path: balanced/train-* - split: validation path: balanced/validation-* - split: test path: balanced/test-* - config_name: balanced_shuf data_files: - split: train path: balanced_shuf/train-* - split: validation path: balanced_shuf/validation-* - split: test path: balanced_shuf/test-* ---
Nexdata/Mandarin_Speech_by_Mobile_Phone	--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging --- # Dataset Card for Nexdata/Mandarin_Speech_by_Mobile_Phone ## 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) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://www.nexdata.ai/datasets/35?source=Huggingface - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary It collects 6,278 speakers' dat from 33 provinces of China. 2,980 males and 3,298 females. The recording contents are commonly used colloquial sentences. It is recorded in both quiet and noisy environment. Annotated texts are transcribed and proofread by professional annotators. The accuracy is not less than 98%. For more details, please refer to the link: https://www.nexdata.ai/datasets/35?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Mandarin ## 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 Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing ### Citation Information [More Information Needed] ### Contributions
CyberHarem/ayase_arisa_lovelive	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of ayase_arisa/絢瀬亜里沙 (Love Live!) This is the dataset of ayase_arisa/絢瀬亜里沙 (Love Live!), containing 163 images and their tags. The core tags of this character are `blonde_hair, blue_eyes, long_hair, hair_ornament, hairclip`, 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 \| 163 \| 120.32 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ayase_arisa_lovelive/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 163 \| 95.27 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ayase_arisa_lovelive/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 334 \| 174.14 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ayase_arisa_lovelive/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 163 \| 115.17 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ayase_arisa_lovelive/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 334 \| 204.05 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ayase_arisa_lovelive/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/ayase_arisa_lovelive', 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, blush, looking_at_viewer, open_mouth, serafuku, skirt, solo, simple_background, white_background, smile \| \| 1 \| 9 \| ![](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) \| 2girls, blush, open_mouth, skirt, serafuku, :d, solo_focus \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| looking_at_viewer \| open_mouth \| serafuku \| skirt \| solo \| simple_background \| white_background \| smile \| 2girls \| :d \| solo_focus \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:--------------------\|:-------------\|:-----------\|:--------\|:-------\|:--------------------\|:-------------------\|:--------\|:---------\|:-----\|:-------------\| \| 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 \| \| \| \| \| 1 \| 9 \| ![](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 \|
nidnoiewoifehw/yocleash	--- license: gpl-3.0 ---
nilekhet/Spectrum-Dataset	--- license: wtfpl --- Dataset Card: Spectrum-Dataset 🌈 🌐 Source: [nilekhet/Spectrum · Hugging Face](https://huggingface.co/nilekhet/Spectrum) 📁 Supplementary Dataset: Spectrum-Dataset 🌟 🔗 Associated Model: Spectrum Model 🧬 ## 🔍 bengin_generator.py 👨‍💻 * 📂 Recursively walks through folders * 🚫 Skips unallowed items * 🔄 Copies .exe files to destination folder ## 🔍 malfamily.py 👩‍💻 * 🌐 Scrapes malware family links * 📥 Downloads and organizes malware samples * 🗂️ Saves data as a .csv file ## 🔍 Rust code for image generation 🎨 * 🌐 GitHub: https://github.com/nileshkhetrapal/spectrum * 🖼️ Generates images from the code ## 🎯 Intended Use of the Model 🌟 * 💻🔧 Classify malware based on input images * 🛡️💻 Improve computer and network security * 🌐 Help with malware detection and prevention # 📊 Number of Classes: 1️⃣1️⃣9️⃣ * 🦠 Includes benign class
joey234/mmlu-conceptual_physics-neg-prepend-verbal	--- configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: ori_prompt dtype: string - name: fewshot_context_neg dtype: string - name: fewshot_context_ori dtype: string - name: neg_prompt dtype: string splits: - name: dev num_bytes: 5977 num_examples: 5 - name: test num_bytes: 1347765 num_examples: 235 download_size: 155122 dataset_size: 1353742 --- # Dataset Card for "mmlu-conceptual_physics-neg-prepend-verbal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rajendrabaskota/hc3-wiki-cleaned-text-for-domain-classification-roberta-tokenized-max-len-512	--- dataset_info: features: - name: prompt dtype: string - name: text dtype: string - name: source dtype: int64 - name: human/ai dtype: int64 - name: perplexity dtype: float64 - name: cleaned_text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 845606936 num_examples: 330345 - name: test num_bytes: 44570090 num_examples: 17387 download_size: 499405861 dataset_size: 890177026 --- # Dataset Card for "hc3-wiki-cleaned-text-for-domain-classification-roberta-tokenized-max-len-512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Isamu136/chess-gpt-data	--- license: apache-2.0 ---
dsupa/hack5-IQ-HP	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' splits: - name: train num_bytes: 2171810.0 num_examples: 647 download_size: 1814705 dataset_size: 2171810.0 --- # Dataset Card for "hack5-IQ-HP" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BitTranslate/chatgpt-prompts-Ukrainian	--- license: cc0-1.0 language: - uk tags: - ChatGPT ---
Joe02/quinn_refs	--- license: other ---
liuyanchen1015/VALUE_stsb_dey_it	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 13139 num_examples: 69 - name: test num_bytes: 6243 num_examples: 48 - name: train num_bytes: 7725 num_examples: 40 download_size: 27352 dataset_size: 27107 --- # Dataset Card for "VALUE_stsb_dey_it" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
adamjweintraut/bart-finetuned-eli5_lfqa_best_slice-256_2023-12-10_run	--- dataset_info: features: - name: index dtype: int64 - name: q_id dtype: string - name: question dtype: string - name: context dtype: string - name: target dtype: string - name: predicted dtype: string - name: label dtype: string - name: rougeL_P dtype: float64 - name: rougeL_R dtype: float64 - name: rougeL_F dtype: float64 - name: Cosine_Sim dtype: float64 - name: nli-roberta_label dtype: string - name: nli-roberta_plot_vals dtype: int64 - name: nli-roberta-max-score dtype: float64 - name: sent_sim dtype: float32 - name: context_answer_sim dtype: float32 - name: rougeL_min_precision dtype: float64 - name: rougeL_min_recall dtype: float64 - name: rougeL_min_fmeasure dtype: float64 - name: rougeL_median_precision dtype: float64 - name: rougeL_median_recall dtype: float64 - name: rougeL_median_fmeasure dtype: float64 - name: rougeL_max_precision dtype: float64 - name: rougeL_max_recall dtype: float64 - name: rougeL_max_fmeasure dtype: float64 - name: context_predicted_sim dtype: float32 - name: context_label_sim dtype: float32 - name: predicted_label_sim dtype: float32 - name: nli_context_predicted_label dtype: string - name: nli_context_predicted_plots dtype: int64 splits: - name: train num_bytes: 8354389 num_examples: 1250 download_size: 5113622 dataset_size: 8354389 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_jan-hq__trinity-v1	--- pretty_name: Evaluation run of jan-hq/trinity-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jan-hq/trinity-v1](https://huggingface.co/jan-hq/trinity-v1) 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_jan-hq__trinity-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-16T19:24:08.553660](https://huggingface.co/datasets/open-llm-leaderboard/details_jan-hq__trinity-v1/blob/main/results_2023-12-16T19-24-08.553660.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\": 0.6575877329335247,\n\ \ \"acc_stderr\": 0.031985421208388404,\n \"acc_norm\": 0.6571647268300141,\n\ \ \"acc_norm_stderr\": 0.032648337921958155,\n \"mc1\": 0.5507955936352509,\n\ \ \"mc1_stderr\": 0.01741294198611529,\n \"mc2\": 0.6931209356367747,\n\ \ \"mc2_stderr\": 0.015031530031665238\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6988054607508533,\n \"acc_stderr\": 0.013406741767847632,\n\ \ \"acc_norm\": 0.7226962457337884,\n \"acc_norm_stderr\": 0.013082095839059376\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.711113324039036,\n\ \ \"acc_stderr\": 0.004523188431142894,\n \"acc_norm\": 0.8835889265086636,\n\ \ \"acc_norm_stderr\": 0.0032006176493464752\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6592592592592592,\n\ \ \"acc_stderr\": 0.040943762699967926,\n \"acc_norm\": 0.6592592592592592,\n\ \ \"acc_norm_stderr\": 0.040943762699967926\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\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\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\"\ : 0.56,\n \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6994219653179191,\n\ \ \"acc_stderr\": 0.03496101481191179,\n \"acc_norm\": 0.6994219653179191,\n\ \ \"acc_norm_stderr\": 0.03496101481191179\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.04959859966384181,\n\ \ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.04959859966384181\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\ \ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\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\": 0.33,\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\": 0.7806451612903226,\n\ \ \"acc_stderr\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n\ \ \"acc_norm_stderr\": 0.023540799358723295\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.03510766597959215,\n\ \ \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.03510766597959215\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\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\": 0.029126522834586815,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131154,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131154\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \ \ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\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\": 0.01517314184512625,\n \"\ acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.01517314184512625\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290916,\n \ \ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290916\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.031024411740572213,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.031024411740572213\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.033368203384760736,\n\ \ \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.033368203384760736\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742179,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742179\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.046840993210771065\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\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\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8352490421455939,\n\ \ \"acc_stderr\": 0.013265346261323797,\n \"acc_norm\": 0.8352490421455939,\n\ \ \"acc_norm_stderr\": 0.013265346261323797\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069356,\n\ \ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069356\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4759776536312849,\n\ \ \"acc_stderr\": 0.016703190189300186,\n \"acc_norm\": 0.4759776536312849,\n\ \ \"acc_norm_stderr\": 0.016703190189300186\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\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\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \ \ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.47392438070404175,\n\ \ \"acc_stderr\": 0.012752858346533131,\n \"acc_norm\": 0.47392438070404175,\n\ \ \"acc_norm_stderr\": 0.012752858346533131\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\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \ \ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\ \ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.025870646766169136\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\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5507955936352509,\n\ \ \"mc1_stderr\": 0.01741294198611529,\n \"mc2\": 0.6931209356367747,\n\ \ \"mc2_stderr\": 0.015031530031665238\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8200473559589582,\n \"acc_stderr\": 0.01079646868806868\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.7164518574677786,\n \ \ \"acc_stderr\": 0.012415070917508124\n }\n}\n```" repo_url: https://huggingface.co/jan-hq/trinity-v1 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_12_16T19_24_08.553660 path: - '/details_harness\|arc:challenge\|25_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-16T19-24-08.553660.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|gsm8k\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hellaswag\|10_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T19-24-08.553660.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-16T19-24-08.553660.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '/details_harness\|winogrande\|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-16T19-24-08.553660.parquet' - config_name: results data_files: - split: 2023_12_16T19_24_08.553660 path: - results_2023-12-16T19-24-08.553660.parquet - split: latest path: - results_2023-12-16T19-24-08.553660.parquet --- # Dataset Card for Evaluation run of jan-hq/trinity-v1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [jan-hq/trinity-v1](https://huggingface.co/jan-hq/trinity-v1) 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_jan-hq__trinity-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-16T19:24:08.553660](https://huggingface.co/datasets/open-llm-leaderboard/details_jan-hq__trinity-v1/blob/main/results_2023-12-16T19-24-08.553660.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": 0.6575877329335247, "acc_stderr": 0.031985421208388404, "acc_norm": 0.6571647268300141, "acc_norm_stderr": 0.032648337921958155, "mc1": 0.5507955936352509, "mc1_stderr": 0.01741294198611529, "mc2": 0.6931209356367747, "mc2_stderr": 0.015031530031665238 }, "harness\|arc:challenge\|25": { "acc": 0.6988054607508533, "acc_stderr": 0.013406741767847632, "acc_norm": 0.7226962457337884, "acc_norm_stderr": 0.013082095839059376 }, "harness\|hellaswag\|10": { "acc": 0.711113324039036, "acc_stderr": 0.004523188431142894, "acc_norm": 0.8835889265086636, "acc_norm_stderr": 0.0032006176493464752 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6592592592592592, "acc_stderr": 0.040943762699967926, "acc_norm": 0.6592592592592592, "acc_norm_stderr": 0.040943762699967926 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6994219653179191, "acc_stderr": 0.03496101481191179, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.03496101481191179 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.46078431372549017, "acc_stderr": 0.04959859966384181, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.04959859966384181 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "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": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723295, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723295 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5320197044334976, "acc_stderr": 0.03510766597959215, "acc_norm": 0.5320197044334976, "acc_norm_stderr": 0.03510766597959215 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "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": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9015544041450777, "acc_stderr": 0.02150024957603348, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.02150024957603348 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402538, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402538 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131154, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131154 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6722689075630253, "acc_stderr": 0.03048991141767323, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.03048991141767323 }, "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": 0.01517314184512625, "acc_norm": 0.8532110091743119, "acc_norm_stderr": 0.01517314184512625 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8059071729957806, "acc_stderr": 0.025744902532290916, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.025744902532290916 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6905829596412556, "acc_stderr": 0.031024411740572213, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.031024411740572213 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8244274809160306, "acc_stderr": 0.033368203384760736, "acc_norm": 0.8244274809160306, "acc_norm_stderr": 0.033368203384760736 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990947, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990947 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.040191074725573483, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.040191074725573483 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742179, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742179 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.41964285714285715, "acc_stderr": 0.046840993210771065, "acc_norm": 0.41964285714285715, "acc_norm_stderr": 0.046840993210771065 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "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": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8352490421455939, "acc_stderr": 0.013265346261323797, "acc_norm": 0.8352490421455939, "acc_norm_stderr": 0.013265346261323797 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069356, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069356 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4759776536312849, "acc_stderr": 0.016703190189300186, "acc_norm": 0.4759776536312849, "acc_norm_stderr": 0.016703190189300186 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137897, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137897 }, "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": 0.7469135802469136, "acc_stderr": 0.024191808600712995, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600712995 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.48936170212765956, "acc_stderr": 0.029820747191422473, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.029820747191422473 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.47392438070404175, "acc_stderr": 0.012752858346533131, "acc_norm": 0.47392438070404175, "acc_norm_stderr": 0.012752858346533131 }, "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": 0.6715686274509803, "acc_stderr": 0.018999707383162673, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.018999707383162673 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7224489795918367, "acc_stderr": 0.028666857790274648, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274648 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.025870646766169136, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.025870646766169136 }, "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": 0.8245614035087719, "acc_stderr": 0.029170885500727665, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727665 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5507955936352509, "mc1_stderr": 0.01741294198611529, "mc2": 0.6931209356367747, "mc2_stderr": 0.015031530031665238 }, "harness\|winogrande\|5": { "acc": 0.8200473559589582, "acc_stderr": 0.01079646868806868 }, "harness\|gsm8k\|5": { "acc": 0.7164518574677786, "acc_stderr": 0.012415070917508124 } } ``` ## 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]
MexIvanov/CodeExercise-Python-27k-ru	--- license: cc-by-nc-sa-4.0 language: - ru tags: - Python - code --- A machine translated version of the codefuse-ai/CodeExercise-Python-27k dataset. Consists of synthetically generated code with code-related data and natural language instructions. Released under the same license as the original dataset, provided as is with research intent, use/read at your own risk.
distilabel-internal-testing/airoboros-3.2-writing-oai-style-tiny	--- dataset_info: features: - name: id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 56595.216657287565 num_examples: 10 download_size: 37556 dataset_size: 56595.216657287565 configs: - config_name: default data_files: - split: train path: data/train-* ---
FlyingFishzzz/source_test	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: image_seg dtype: image - name: landmarks dtype: string - name: spiga sequence: sequence: float64 - name: spiga_seg dtype: image - name: image_caption dtype: string splits: - name: train num_bytes: 488488715.0 num_examples: 1588 download_size: 487390223 dataset_size: 488488715.0 --- # Dataset Card for "source_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
thrisha15/Content_Generation_dataset	--- language: - en ---
HydraLM/partitioned_v2_standardized_014	--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_conversation_id dtype: string splits: - name: train num_bytes: 64103762.64755713 num_examples: 125409 download_size: 18947842 dataset_size: 64103762.64755713 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v2_standardized_014" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AdapterOcean/Open_Platypus_standardized_cluster_1_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 7549475 num_examples: 7230 download_size: 0 dataset_size: 7549475 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Open_Platypus_standardized_cluster_1_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lachieandmitch/hugging	--- license: apache-2.0 ---
Someman/hindi-summarization	--- license: mit task_categories: - summarization language: hi original_source: >- https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus dataset_info: features: - name: headline dtype: string - name: summary dtype: string - name: article dtype: string splits: - name: train num_bytes: 410722079.5542422 num_examples: 55226 - name: test num_bytes: 102684238.44575782 num_examples: 13807 - name: valid num_bytes: 128376473 num_examples: 17265 download_size: 150571314 dataset_size: 641782791 pretty_name: hindi summarization size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus?select=test.csv ### Dataset Summary Hindi Text Short and Large Summarization Corpus is a collection of ~180k articles with their headlines and summary collected from Hindi News Websites. This is a first of its kind Dataset in Hindi which can be used to benchmark models for Text summarization in Hindi. This does not contain articles contained in Hindi Text Short Summarization Corpus which is being released parallely with this Dataset. The dataset retains original punctuation, numbers etc in the articles. ### Languages The language is Hindi. ### Licensing Information MIT ### Citation Information https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus?select=test.csv ### Contributions
4eJIoBek/Old-audios-11k	--- license: unknown --- unsorted audios in mod, wav or other old audio formats
PurCL/marinda-type-inference-debuginfo-only-O1-shuffle	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: metadata struct: - name: binary_name dtype: string - name: function_addr dtype: int64 - name: function_name dtype: string - name: project_name dtype: string - name: code_w_type dtype: string - name: code dtype: string - name: data_dep dtype: string splits: - name: train num_bytes: 201535867.70075417 num_examples: 37113 - name: test num_bytes: 22394684.299245823 num_examples: 4124 download_size: 52386440 dataset_size: 223930552.0 --- # Dataset Card for "marinda-type-inference-debuginfo-only-O1-shuffle" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_ajibawa-2023__Code-Mistral-7B	--- pretty_name: Evaluation run of ajibawa-2023/Code-Mistral-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ajibawa-2023/Code-Mistral-7B](https://huggingface.co/ajibawa-2023/Code-Mistral-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 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the 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_ajibawa-2023__Code-Mistral-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-25T07:53:45.933606](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Code-Mistral-7B/blob/main/results_2024-03-25T07-53-45.933606.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\": 0.6527492220016492,\n\ \ \"acc_stderr\": 0.031870603059274874,\n \"acc_norm\": 0.6533709217123561,\n\ \ \"acc_norm_stderr\": 0.0325259522142822,\n \"mc1\": 0.3684210526315789,\n\ \ \"mc1_stderr\": 0.016886551261046046,\n \"mc2\": 0.5463721037747236,\n\ \ \"mc2_stderr\": 0.015046435516843176\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6083617747440273,\n \"acc_stderr\": 0.014264122124938213,\n\ \ \"acc_norm\": 0.6459044368600683,\n \"acc_norm_stderr\": 0.01397545412275656\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6560446126269668,\n\ \ \"acc_stderr\": 0.0047405557821421735,\n \"acc_norm\": 0.8529177454690301,\n\ \ \"acc_norm_stderr\": 0.0035346403488166773\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7236842105263158,\n \"acc_stderr\": 0.03639057569952929,\n\ \ \"acc_norm\": 0.7236842105263158,\n \"acc_norm_stderr\": 0.03639057569952929\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\": 0.7245283018867924,\n \"acc_stderr\": 0.02749566368372406,\n\ \ \"acc_norm\": 0.7245283018867924,\n \"acc_norm_stderr\": 0.02749566368372406\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\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\ : 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6878612716763006,\n\ \ \"acc_stderr\": 0.03533133389323657,\n \"acc_norm\": 0.6878612716763006,\n\ \ \"acc_norm_stderr\": 0.03533133389323657\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\ \ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5158730158730159,\n\ \ \"acc_stderr\": 0.044698818540726076,\n \"acc_norm\": 0.5158730158730159,\n\ \ \"acc_norm_stderr\": 0.044698818540726076\n },\n \"harness\|hendrycksTest-global_facts\|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-high_school_biology\|5\": {\n \"acc\"\ : 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723295,\n \"\ acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723295\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218967,\n \"\ acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218967\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n\ \ \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \ \ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \ \ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.0302839955258844,\n \ \ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.0302839955258844\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\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\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.034076320938540516,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.034076320938540516\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931796,\n \"\ acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931796\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8185654008438819,\n \"acc_stderr\": 0.02508596114457966,\n \ \ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.02508596114457966\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7219730941704036,\n\ \ \"acc_stderr\": 0.030069584874494043,\n \"acc_norm\": 0.7219730941704036,\n\ \ \"acc_norm_stderr\": 0.030069584874494043\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7962962962962963,\n\ \ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\ \ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.034624199316156234,\n\ \ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.034624199316156234\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9017094017094017,\n\ \ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\ \ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8352490421455939,\n\ \ \"acc_stderr\": 0.013265346261323786,\n \"acc_norm\": 0.8352490421455939,\n\ \ \"acc_norm_stderr\": 0.013265346261323786\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044287,\n\ \ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044287\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3195530726256983,\n\ \ \"acc_stderr\": 0.01559552029414741,\n \"acc_norm\": 0.3195530726256983,\n\ \ \"acc_norm_stderr\": 0.01559552029414741\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818733,\n\ \ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818733\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7138263665594855,\n\ \ \"acc_stderr\": 0.02567025924218893,\n \"acc_norm\": 0.7138263665594855,\n\ \ \"acc_norm_stderr\": 0.02567025924218893\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\"\ : 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness\|hendrycksTest-professional_law\|5\"\ : {\n \"acc\": 0.47783572359843546,\n \"acc_stderr\": 0.012757683047716175,\n\ \ \"acc_norm\": 0.47783572359843546,\n \"acc_norm_stderr\": 0.012757683047716175\n\ \ },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\"\ : 0.6948529411764706,\n \"acc_stderr\": 0.027971541370170598,\n \"\ acc_norm\": 0.6948529411764706,\n \"acc_norm_stderr\": 0.027971541370170598\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.684640522875817,\n \"acc_stderr\": 0.01879808628488689,\n \ \ \"acc_norm\": 0.684640522875817,\n \"acc_norm_stderr\": 0.01879808628488689\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\": 0.726530612244898,\n \"acc_stderr\": 0.02853556033712844,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.02853556033712844\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.02519692987482707,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.02519692987482707\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\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\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160893,\n\ \ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160893\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3684210526315789,\n\ \ \"mc1_stderr\": 0.016886551261046046,\n \"mc2\": 0.5463721037747236,\n\ \ \"mc2_stderr\": 0.015046435516843176\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8224151539068666,\n \"acc_stderr\": 0.010740676861359226\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6808188021228203,\n \ \ \"acc_stderr\": 0.012840345676251648\n }\n}\n```" repo_url: https://huggingface.co/ajibawa-2023/Code-Mistral-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_25T05_45_49.471582 path: - '/details_harness\|arc:challenge\|25_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|arc:challenge\|25_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-25T07-53-45.933606.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|gsm8k\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|gsm8k\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hellaswag\|10_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hellaswag\|10_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-25T05-45-49.471582.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-25T07-53-45.933606.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-25T07-53-45.933606.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '/details_harness\|winogrande\|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '/details_harness\|winogrande\|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-25T07-53-45.933606.parquet' - config_name: results data_files: - split: 2024_03_25T05_45_49.471582 path: - results_2024-03-25T05-45-49.471582.parquet - split: 2024_03_25T07_53_45.933606 path: - results_2024-03-25T07-53-45.933606.parquet - split: latest path: - results_2024-03-25T07-53-45.933606.parquet --- # Dataset Card for Evaluation run of ajibawa-2023/Code-Mistral-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [ajibawa-2023/Code-Mistral-7B](https://huggingface.co/ajibawa-2023/Code-Mistral-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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_ajibawa-2023__Code-Mistral-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-25T07:53:45.933606](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Code-Mistral-7B/blob/main/results_2024-03-25T07-53-45.933606.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": 0.6527492220016492, "acc_stderr": 0.031870603059274874, "acc_norm": 0.6533709217123561, "acc_norm_stderr": 0.0325259522142822, "mc1": 0.3684210526315789, "mc1_stderr": 0.016886551261046046, "mc2": 0.5463721037747236, "mc2_stderr": 0.015046435516843176 }, "harness\|arc:challenge\|25": { "acc": 0.6083617747440273, "acc_stderr": 0.014264122124938213, "acc_norm": 0.6459044368600683, "acc_norm_stderr": 0.01397545412275656 }, "harness\|hellaswag\|10": { "acc": 0.6560446126269668, "acc_stderr": 0.0047405557821421735, "acc_norm": 0.8529177454690301, "acc_norm_stderr": 0.0035346403488166773 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7236842105263158, "acc_stderr": 0.03639057569952929, "acc_norm": 0.7236842105263158, "acc_norm_stderr": 0.03639057569952929 }, "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": 0.7245283018867924, "acc_stderr": 0.02749566368372406, "acc_norm": 0.7245283018867924, "acc_norm_stderr": 0.02749566368372406 }, "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": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6878612716763006, "acc_stderr": 0.03533133389323657, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.03533133389323657 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.39215686274509803, "acc_stderr": 0.04858083574266345, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.04858083574266345 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.02530590624159063, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.02530590624159063 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5158730158730159, "acc_stderr": 0.044698818540726076, "acc_norm": 0.5158730158730159, "acc_norm_stderr": 0.044698818540726076 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723295, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723295 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8131313131313131, "acc_stderr": 0.027772533334218967, "acc_norm": 0.8131313131313131, "acc_norm_stderr": 0.027772533334218967 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.917098445595855, "acc_stderr": 0.01989934131572178, "acc_norm": 0.917098445595855, "acc_norm_stderr": 0.01989934131572178 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6743589743589744, "acc_stderr": 0.02375966576741229, "acc_norm": 0.6743589743589744, "acc_norm_stderr": 0.02375966576741229 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.680672268907563, "acc_stderr": 0.0302839955258844, "acc_norm": 0.680672268907563, "acc_norm_stderr": 0.0302839955258844 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "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": 0.5185185185185185, "acc_stderr": 0.034076320938540516, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.034076320938540516 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931796, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931796 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8185654008438819, "acc_stderr": 0.02508596114457966, "acc_norm": 0.8185654008438819, "acc_norm_stderr": 0.02508596114457966 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7219730941704036, "acc_stderr": 0.030069584874494043, "acc_norm": 0.7219730941704036, "acc_norm_stderr": 0.030069584874494043 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8015267175572519, "acc_stderr": 0.03498149385462472, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.03498149385462472 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7962962962962963, "acc_stderr": 0.03893542518824847, "acc_norm": 0.7962962962962963, "acc_norm_stderr": 0.03893542518824847 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7361963190184049, "acc_stderr": 0.034624199316156234, "acc_norm": 0.7361963190184049, "acc_norm_stderr": 0.034624199316156234 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8352490421455939, "acc_stderr": 0.013265346261323786, "acc_norm": 0.8352490421455939, "acc_norm_stderr": 0.013265346261323786 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7427745664739884, "acc_stderr": 0.023532925431044287, "acc_norm": 0.7427745664739884, "acc_norm_stderr": 0.023532925431044287 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3195530726256983, "acc_stderr": 0.01559552029414741, "acc_norm": 0.3195530726256983, "acc_norm_stderr": 0.01559552029414741 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7189542483660131, "acc_stderr": 0.025738854797818733, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.025738854797818733 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7138263665594855, "acc_stderr": 0.02567025924218893, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.02567025924218893 }, "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": 0.5, "acc_stderr": 0.029827499313594685, "acc_norm": 0.5, "acc_norm_stderr": 0.029827499313594685 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.47783572359843546, "acc_stderr": 0.012757683047716175, "acc_norm": 0.47783572359843546, "acc_norm_stderr": 0.012757683047716175 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6948529411764706, "acc_stderr": 0.027971541370170598, "acc_norm": 0.6948529411764706, "acc_norm_stderr": 0.027971541370170598 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.684640522875817, "acc_stderr": 0.01879808628488689, "acc_norm": 0.684640522875817, "acc_norm_stderr": 0.01879808628488689 }, "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": 0.726530612244898, "acc_stderr": 0.02853556033712844, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.02853556033712844 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482707, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482707 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197769, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197769 }, "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": 0.8421052631578947, "acc_stderr": 0.027966785859160893, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.027966785859160893 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3684210526315789, "mc1_stderr": 0.016886551261046046, "mc2": 0.5463721037747236, "mc2_stderr": 0.015046435516843176 }, "harness\|winogrande\|5": { "acc": 0.8224151539068666, "acc_stderr": 0.010740676861359226 }, "harness\|gsm8k\|5": { "acc": 0.6808188021228203, "acc_stderr": 0.012840345676251648 } } ``` ## 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]
KETI-AIR/kor_ropes	--- pretty_name: ROPES language: - ko license: - cc-by-4.0 size_categories: - 10K<n<100K task_categories: - question-answering task_ids: - extractive-qa dataset_info: features: - name: data_index_by_user dtype: int32 - name: background dtype: string - name: situation dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string splits: - name: train num_bytes: 13608462 num_examples: 10924 - name: validation num_bytes: 1864822 num_examples: 1688 - name: test num_bytes: 2158508 num_examples: 1710 download_size: 1465973 dataset_size: 17631792 --- # Dataset Card for ROPES ## Licensing Information The data is distributed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. ## Source Data Citation INformation ``` @inproceedings{Lin2019ReasoningOP, title={Reasoning Over Paragraph Effects in Situations}, author={Kevin Lin and Oyvind Tafjord and Peter Clark and Matt Gardner}, booktitle={MRQA@EMNLP}, year={2019} }
aditijha/instruct_v1_1k_and_lima	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: source dtype: string splits: - name: train num_bytes: 3698244 num_examples: 2000 download_size: 2042056 dataset_size: 3698244 --- # Dataset Card for "instruct_v1_1k_and_lima" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arunrajuamrutha3/martin_valen_dataset	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 82739.0 num_examples: 10 download_size: 82646 dataset_size: 82739.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
gowitheflow/wiki-span	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: train num_bytes: 14498836027 num_examples: 6458670 download_size: 8956015300 dataset_size: 14498836027 --- # Dataset Card for "wiki-span" This dataset is constructed by sampling 25%-50% of each wikipedia record twice, as positive pairs. It can be used to train unsupervised sentence representation models.
Seanxh/twitter_dataset_1713212801	--- 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: 176063 num_examples: 412 download_size: 62997 dataset_size: 176063 configs: - config_name: default data_files: - split: train path: data/train-* ---
nlpso/m2m3_fine_tuning_ocr_ptrn_cmbert_iob2	--- language: - fr multilinguality: - monolingual task_categories: - token-classification --- # m2m3_fine_tuning_ocr_ptrn_cmbert_iob2 ## Introduction This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for nested NER task using Independant NER layers approach [M1]. It contains Paris trade directories entries from the 19th century. ## Dataset parameters * Approachrd : M2 and M3 * Dataset type : noisy (Pero OCR) * Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) * Tagging format : IOB2 * Counts : * Train : 6084 * Dev : 676 * Test : 1685 * Associated fine-tuned models : * M2 : [nlpso/m2_joint_label_ocr_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ocr_ptrn_cmbert_iob2) * M3 : [nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_iob2) ## Entity types Abbreviation\|Entity group (level)\|Description -\|-\|- O \|1 & 2\|Outside of a named entity PER \|1\|Person or company name ACT \|1 & 2\|Person or company professional activity TITREH \|2\|Military or civil distinction DESC \|1\|Entry full description TITREP \|2\|Professionnal reward SPAT \|1\|Address LOC \|2\|Street name CARDINAL \|2\|Street number FT \|2\|Geographical feature ## How to use this dataset ```python from datasets import load_dataset train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_ptrn_cmbert_iob2")
Fsoft-AIC/the-vault-function	--- language: - code - en multilinguality: - multiprogramming languages task_categories: - text-generation license: mit dataset_info: features: - name: identifier dtype: string - name: return_type dtype: string - name: repo dtype: string - name: path dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens dtype: string - name: original_docstring dtype: string - name: comment dtype: string - name: docstring_tokens dtype: string - name: docstring dtype: string - name: original_string dtype: string pretty_name: The Vault Function viewer: true --- ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Statistics](#dataset-statistics) - [Usage](#usage) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: [FSoft-AI4Code/TheVault](https://github.com/FSoft-AI4Code/TheVault) - Paper: [The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation](https://arxiv.org/abs/2305.06156) - Contact: support.ailab@fpt.com - Website: https://www.fpt-aicenter.com/ai-residency/ <p align="center"> <img src="https://raw.githubusercontent.com/FSoft-AI4Code/TheVault/main/assets/the-vault-4-logo-png.png" width="300px" alt="logo"> </p> <div align="center"> # The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation </div> ## Dataset Summary The Vault dataset is a comprehensive, large-scale, multilingual parallel dataset that features high-quality code-text pairs derived from The Stack, the largest permissively-licensed source code dataset. We provide The Vault which contains code snippets from 10 popular programming languages such as Java, JavaScript, Python, Ruby, Rust, Golang, C#, C++, C, and PHP. This dataset provides multiple code-snippet levels, metadata, and 11 docstring styles for enhanced usability and versatility. ## Supported Tasks The Vault can be used for pretraining LLMs or downstream code-text interaction tasks. A number of tasks related to code understanding and geneartion can be constructed using The Vault such as code summarization, text-to-code generation and code search. ## Languages The natural language text (docstring) is in English. 10 programming languages are supported in The Vault: `Python`, `Java`, `JavaScript`, `PHP`, `C`, `C#`, `C++`, `Go`, `Ruby`, `Rust` ## Dataset Structure ### Data Instances ``` { "hexsha": "5c47f0b4c173a8fd03e4e633d9b3dd8211e67ad0", "repo": "neumanna94/beepboop", "path": "js/scripts.js", "license": [ "MIT" ], "language": "JavaScript", "identifier": "beepBoopSelector", "return_type": "<not_specific>", "original_string": "function beepBoopSelector(inputString, bbFunction){\n if(bbFunction==1){\n return beepBoop(inputString);\n } else if(bbFunction==2){\n return beepBoop2(inputString);\n } else if(bbFunction==3){\n return beepBoop3(inputString);\n } else {\n }\n}", "original_docstring": "//Determines what beepBoop function to use", "docstring": "Determines what beepBoop function to use", "docstring_tokens": [ "Determines", "what", "beepBoop", "function", "to", "use" ], "code": "function beepBoopSelector(inputString, bbFunction){\n if(bbFunction==1){\n return beepBoop(inputString);\n } else if(bbFunction==2){\n return beepBoop2(inputString);\n } else if(bbFunction==3){\n return beepBoop3(inputString);\n } else {\n }\n}", "code_tokens": [ "function", "beepBoopSelector", "(", "inputString", ",", "bbFunction", ")", "{", "if", "(", "bbFunction", "==", "1", ")", "{", "return", "beepBoop", "(", "inputString", ")", ";", "}", "else", "if", "(", "bbFunction", "==", "2", ")", "{", "return", "beepBoop2", "(", "inputString", ")", ";", "}", "else", "if", "(", "bbFunction", "==", "3", ")", "{", "return", "beepBoop3", "(", "inputString", ")", ";", "}", "else", "{", "}", "}" ], "short_docstring": "Determines what beepBoop function to use", "short_docstring_tokens": [ "Determines", "what", "beepBoop", "function", "to", "use" ], "comment": [], "parameters": [ { "param": "inputString", "type": null }, { "param": "bbFunction", "type": null } ], "docstring_params": { "returns": [], "raises": [], "params": [ { "identifier": "inputString", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "bbFunction", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] } } ``` ### Data Fields Data fields for function level: - hexsha (string): the unique git hash of file - repo (string): the owner/repo - path (string): the full path to the original file - license (list): licenses in the repo - language (string): the programming language - identifier (string): the function or method name - return_type (string): the type returned by the function - original_string (string): original version of function/class node - original_docstring (string): the raw string before tokenization or parsing - code (string): the part of the original that is code - code_tokens (list): tokenized version of `code` - short_docstring (string): short, brief summarization (first line of the docstring) - short_docstring_tokens (list): tokenized version of `short_docstring - docstring (string): the top-level comment or docstring (docstring version without param’s doc, return, exception fields, etc) - docstring_tokens (list): tokenized version of docstring - comment (list): list of comments (line) inside the function/class - parameters (list): List of parameters and its type (type can be None) - docstring_params (dict): Dictionary of the parsed information from docstring See [here](https://github.com/FSoft-AI4Code/TheVault/blob/main/data/README.md) for more details and examples. ### Data Splits In this repo, The Vault is divided into 5 subsets, where three training versions are split based on size of the full training set, and the remains are validation set and test set (approximate 20,000 samples in each). The statistic for languages in each split set is illustrated in the following section. Before split, the dataset is deduplicated. There are 3 versions of training set that are small (5%), medium (20%) and large (100%). ## Dataset Statistics - Compare to other benchmarks \| Dataset \| #Language \| #Code-text pair \| \|:--------------------------\|----------:\|-----------------:\| \| PyMT5 \| 1 \| ≈ 7,700,000 \| \| CoDesc \| 1 \| 4,211,516 \| \| CodeSearchNet \| 6 \| 2,326,976 \| \| CodeSearchNet (CodeXGLUE) \| 6 \| 1,005,474 \| \| Deepcom \| 1 \| 424,028 \| \| CONCODE \| 1 \| 2,184,310 \| \| Funcom \| 1 \| 2,149,121 \| \| CodeT5 \| 8 \| 3,158,313 \| \| The Vault \| 10 \| 34,098,775 \| - Statistic for split sets \| \| train/small \| train/medium \| train/full \| validation \| test \| total \| \|:-----------\|------------:\|-------------:\|-----------:\|-----------:\|-------:\|--------------:\| \|Python \| 370,657 \| 1,952,110 \| 7,772,647 \| 30,992 \| 21,652 \| 7,825,291 \| \|Java \| 351,213 \| 1,612,366 \| 6,629,193 \| 22,677 \| 15,552 \| 6,667,422 \| \|JavaScript \| 82,931 \| 404,729 \| 1,640,416 \| 22,044 \| 21,108 \| 1,683,568 \| \|PHP \| 236,638 \| 1,155,476 \| 4,656,371 \| 21,375 \| 19,010 \| 4,696,756 \| \|C \| 105,978 \| 381,207 \| 1,639,319 \| 27,525 \| 19,122 \| 1,685,966 \| \|C# \| 141,090 \| 783,166 \| 3,305,891 \| 24,787 \| 19,638 \| 3,350,316 \| \|C++ \| 87,420 \| 410,907 \| 1,671,268 \| 20,011 \| 18,169 \| 1,709,448 \| \|Go \| 267,535 \| 1,319,547 \| 5,109,020 \| 19,102 \| 25,314 \| 5,153,436 \| \|Ruby \| 23,921 \| 112,574 \| 424,339 \| 17,338 \| 19,908 \| 461,585 \| \|Rust \| 35,367 \| 224,015 \| 825,130 \| 16,716 \| 23,141 \| 864,987 \| \|TOTAL \| 1,702,750 \| 8,356,097 \|33,673,594 \|222,567 \|202,614 \|34,098,775 \| ## Usage You can load The Vault dataset using datasets library: ```pip install datasets``` ```python from datasets import load_dataset # Load full function level dataset (34M samples) dataset = load_dataset("Fsoft-AIC/the-vault-function") # Load function level train/validation/test set dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"]) # Load "small" (or "medium", "full") version of function level training set dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train/small"]) # specific language (e.g. Python) dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"], languages=['Python']) # dataset streaming data = load_dataset("Fsoft-AIC/the-vault-function", split_set= ["train"], streaming= True) for sample in iter(data['train']): print(sample) ``` A back up dataset can be downloaded in azure storage. See [Download The Vault from Azure blob storage](https://github.com/FSoft-AI4Code/TheVault#download-via-link). ## Additional information ### Licensing Information MIT License ### Citation Information ``` @article{manh2023vault, title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation}, author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ}, journal={arXiv preprint arXiv:2305.06156}, year={2023} } ``` ### Contributions This dataset is developed by [FSOFT AI4Code team](https://github.com/FSoft-AI4Code).
ahishamm/isic_binary_augmented	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': benign '1': malignant splits: - name: train num_bytes: 97831304.566 num_examples: 17214 - name: test num_bytes: 44333792.176 num_examples: 7804 download_size: 152665521 dataset_size: 142165096.74199998 --- # Dataset Card for "isic_binary_augmented" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pkufool/libriheavy	--- license: apache-2.0 --- # Libriheavy: a 50,000 hours ASR corpus with punctuation casing and context Libriheavy is a labeled version of [Librilight](https://github.com/facebookresearch/libri-light), read our [paper](https://arxiv.org/abs/2309.08105) for more details. See https://github.com/k2-fsa/libriheavy for more details. ## Citation ``` @misc{kang2023libriheavy, title={Libriheavy: a 50,000 hours ASR corpus with punctuation casing and context}, author={Wei Kang and Xiaoyu Yang and Zengwei Yao and Fangjun Kuang and Yifan Yang and Liyong Guo and Long Lin and Daniel Povey}, year={2023}, eprint={2309.08105}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```
arthurneuron/USDC-WETH-Uniswap-V3-2021-to-2023	--- license: mit ---
qgiaohc/twitter_dataset_1713183929	--- 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: 24781 num_examples: 55 download_size: 13092 dataset_size: 24781 configs: - config_name: default data_files: - split: train path: data/train-* ---
dim/SlimOrcaRU	--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: value_ru dtype: string - name: weight dtype: float64 - name: key dtype: int64 splits: - name: train num_bytes: 183635644 num_examples: 47536 download_size: 83293621 dataset_size: 183635644 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "SlimOrcaRU" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BitTranslate/indonesiantest	--- license: cc0-1.0 ---
ricardo-lsantos/my_cool_dataset	--- license: mit language: - pt pretty_name: My Cool Dataset ---
Atipico1/nq_test_adversary	--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: gpt_answer_sentence dtype: string - name: gpt_adv_sentence dtype: string - name: is_valid_sentence dtype: bool - name: gpt_adv_passage dtype: string - name: is_valid_passage dtype: bool splits: - name: train num_bytes: 14371495 num_examples: 3610 download_size: 8513525 dataset_size: 14371495 configs: - config_name: default data_files: - split: train path: data/train-* ---
alvations/c4p0-v1-de-en	--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 18411738 num_examples: 15146 download_size: 7768565 dataset_size: 18411738 configs: - config_name: default data_files: - split: train path: data/train-* ---
LNTANOooo/tulu_v3	--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: role dtype: string splits: - name: science.scierc_ner num_bytes: 634623.0 num_examples: 349 - name: sharegpt num_bytes: 776319873.4338813 num_examples: 72413 - name: science.scifact_json num_bytes: 2350372.0 num_examples: 919 - name: lima num_bytes: 2815967.0 num_examples: 1012 - name: gpt4_alpaca num_bytes: 16091564.0 num_examples: 19834 - name: science.evidence_inference num_bytes: 6620099.0 num_examples: 1673 - name: oasst1 num_bytes: 11027612.499452954 num_examples: 7046 - name: science.scitldr_aic num_bytes: 13392412.0 num_examples: 1957 - name: science.scierc_relation num_bytes: 735295.0 num_examples: 349 - name: science.qasper_truncated_4000 num_bytes: 34952831.0 num_examples: 2204 - name: hard_coded num_bytes: 44940.0 num_examples: 90 - name: code_alpaca num_bytes: 7102581.0 num_examples: 19992 - name: cot num_bytes: 56091350.817187 num_examples: 49709 - name: wizardlm num_bytes: 69442958.16317087 num_examples: 29597 - name: open_orca num_bytes: 52677835.20356853 num_examples: 29581 - name: flan_v2 num_bytes: 105654005.53780366 num_examples: 49108 download_size: 518036574 dataset_size: 1155954319.6550643 configs: - config_name: default data_files: - split: science.scierc_ner path: data/science.scierc_ner-* - split: sharegpt path: data/sharegpt-* - split: science.scifact_json path: data/science.scifact_json-* - split: lima path: data/lima-* - split: gpt4_alpaca path: data/gpt4_alpaca-* - split: science.evidence_inference path: data/science.evidence_inference-* - split: oasst1 path: data/oasst1-* - split: science.scitldr_aic path: data/science.scitldr_aic-* - split: science.scierc_relation path: data/science.scierc_relation-* - split: science.qasper_truncated_4000 path: data/science.qasper_truncated_4000-* - split: hard_coded path: data/hard_coded-* - split: code_alpaca path: data/code_alpaca-* - split: cot path: data/cot-* - split: wizardlm path: data/wizardlm-* - split: open_orca path: data/open_orca-* - split: flan_v2 path: data/flan_v2-* ---
mozci/tinysketch	--- license: cc-by-nc-sa-4.0 language: - en language_creators: - machine-generated multilinguality: - monolingual pretty_name: 'Sketch Scene Descriptions' size_categories: - n<10K source_datasets: - FS-COCO tags: [] task_categories: - text-to-image task_ids: [] --- # Dataset Card for Sketch Scene Descriptions _Dataset used to train [Sketch Scene text to image model]()_ We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description. For each row, the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided. ## Citation If you use this dataset, please cite it as: ``` @inproceedings{fscoco, title={FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context.} author={Chowdhury, Pinaki Nath and Sain, Aneeshan and Bhunia, Ayan Kumar and Xiang, Tao and Gryaditskaya, Yulia and Song, Yi-Zhe}, booktitle={ECCV}, year={2022} } ```
mugithi/ubuntu_question_answer_jsonl	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 2073677 num_examples: 12100 - name: test num_bytes: 882250 num_examples: 5186 download_size: 0 dataset_size: 2955927 --- # Dataset Card for "ubuntu_question_answer_jsonl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
multi_re_qa	--- annotations_creators: - expert-generated - found language_creators: - expert-generated - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - 1M<n<10M source_datasets: - extended\|other-BioASQ - extended\|other-DuoRC - extended\|other-HotpotQA - extended\|other-Natural-Questions - extended\|other-Relation-Extraction - extended\|other-SQuAD - extended\|other-SearchQA - extended\|other-TextbookQA - extended\|other-TriviaQA task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: multireqa pretty_name: MultiReQA dataset_info: - config_name: SearchQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 183902877 num_examples: 3163801 - name: validation num_bytes: 26439174 num_examples: 454836 download_size: 36991959 dataset_size: 210342051 - config_name: TriviaQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 107326326 num_examples: 1893674 - name: validation num_bytes: 13508062 num_examples: 238339 download_size: 21750402 dataset_size: 120834388 - config_name: HotpotQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 29516866 num_examples: 508879 - name: validation num_bytes: 3027229 num_examples: 52191 download_size: 6343389 dataset_size: 32544095 - config_name: SQuAD features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 16828974 num_examples: 95659 - name: validation num_bytes: 2012997 num_examples: 10642 download_size: 3003646 dataset_size: 18841971 - config_name: NaturalQuestions features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 28732767 num_examples: 448355 - name: validation num_bytes: 1418124 num_examples: 22118 download_size: 6124487 dataset_size: 30150891 - config_name: BioASQ features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 766190 num_examples: 14158 download_size: 156649 dataset_size: 766190 - config_name: RelationExtraction features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 217870 num_examples: 3301 download_size: 73019 dataset_size: 217870 - config_name: TextbookQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 4182675 num_examples: 71147 download_size: 704602 dataset_size: 4182675 - config_name: DuoRC features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 1483518 num_examples: 5525 download_size: 97625 dataset_size: 1483518 config_names: - BioASQ - DuoRC - HotpotQA - NaturalQuestions - RelationExtraction - SQuAD - SearchQA - TextbookQA - TriviaQA --- # Dataset Card for MultiReQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://github.com/google-research-datasets/MultiReQA - Repository: https://github.com/google-research-datasets/MultiReQA - Paper: https://arxiv.org/pdf/2005.02507.pdf - Leaderboard: - Point of Contact: ### Dataset Summary MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data (also includes DuoRC but not specified in the official documentation) ### Supported Tasks and Leaderboards - Question answering (QA) - Retrieval question answering (ReQA) ### Languages Sentence boundary annotation for SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, TextbookQA and DuoRC ## Dataset Structure ### Data Instances The general format is: ` { "candidate_id": <candidate_id>, "response_start": <response_start>, "response_end": <response_end> } ... ` An example from SearchQA: `{'candidate_id': 'SearchQA_000077f3912049dfb4511db271697bad/_0_1', 'response_end': 306, 'response_start': 243} ` ### Data Fields ` { "candidate_id": <STRING>, "response_start": <INT>, "response_end": <INT> } ... ` - candidate_id: The candidate id of the candidate sentence. It consists of the original qid from the MRQA shared task. - response_start: The start index of the sentence with respect to its original context. - response_end: The end index of the sentence with respect to its original context ### Data Splits Train and Dev splits are available only for the following datasets, - SearchQA - TriviaQA - HotpotQA - SQuAD - NaturalQuestions Test splits are available only for the following datasets, - BioASQ - RelationExtraction - TextbookQA The number of candidate sentences for each dataset in the table below. \| \| MultiReQA \| \| \|--------------------\|-----------\|---------\| \| \| train \| test \| \| SearchQA \| 629,160 \| 454,836 \| \| TriviaQA \| 335,659 \| 238,339 \| \| HotpotQA \| 104,973 \| 52,191 \| \| SQuAD \| 87,133 \| 10,642 \| \| NaturalQuestions \| 106,521 \| 22,118 \| \| BioASQ \| - \| 14,158 \| \| RelationExtraction \| - \| 3,301 \| \| TextbookQA \| - \| 3,701 \| ## Dataset Creation ### Curation Rationale MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the [MRQA shared task](https://mrqa.github.io/). The dataset was curated by converting existing QA datasets from [MRQA shared task](https://mrqa.github.io/) to the format of MultiReQA benchmark. ### Source Data #### Initial Data Collection and Normalization The Initial data collection was performed by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository ### 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 The annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{m2020multireqa, title={MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models}, author={Mandy Guo and Yinfei Yang and Daniel Cer and Qinlan Shen and Noah Constant}, year={2020}, eprint={2005.02507}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@Karthik-Bhaskar](https://github.com/Karthik-Bhaskar) for adding this dataset.
open-llm-leaderboard/details_alnrg2arg__test_wanda_240109	--- pretty_name: Evaluation run of alnrg2arg/test_wanda_240109 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [alnrg2arg/test_wanda_240109](https://huggingface.co/alnrg2arg/test_wanda_240109)\ \ 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 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the 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_alnrg2arg__test_wanda_240109\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-13T17:19:19.094893](https://huggingface.co/datasets/open-llm-leaderboard/details_alnrg2arg__test_wanda_240109/blob/main/results_2024-01-13T17-19-19.094893.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\": 0.23401038489636644,\n\ \ \"acc_stderr\": 0.029968361313724278,\n \"acc_norm\": 0.23351347966222002,\n\ \ \"acc_norm_stderr\": 0.0307471687800331,\n \"mc1\": 1.0,\n \ \ \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n \ \ },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.22525597269624573,\n\ \ \"acc_stderr\": 0.012207839995407305,\n \"acc_norm\": 0.2295221843003413,\n\ \ \"acc_norm_stderr\": 0.012288926760890797\n },\n \"harness\|hellaswag\|10\"\ : {\n \"acc\": 0.25542720573590916,\n \"acc_stderr\": 0.004352098082984431,\n\ \ \"acc_norm\": 0.2526389165504879,\n \"acc_norm_stderr\": 0.004336375492801798\n\ \ },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.22,\n\ \ \"acc_stderr\": 0.04163331998932268,\n \"acc_norm\": 0.22,\n \ \ \"acc_norm_stderr\": 0.04163331998932268\n },\n \"harness\|hendrycksTest-anatomy\|5\"\ : {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.03355677216313142,\n\ \ \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.03355677216313142\n\ \ },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.29605263157894735,\n\ \ \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.29605263157894735,\n\ \ \"acc_norm_stderr\": 0.03715062154998904\n },\n \"harness\|hendrycksTest-business_ethics\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768077,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768077\n \ \ },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.21509433962264152,\n\ \ \"acc_stderr\": 0.02528839450289137,\n \"acc_norm\": 0.21509433962264152,\n\ \ \"acc_norm_stderr\": 0.02528839450289137\n },\n \"harness\|hendrycksTest-college_biology\|5\"\ : {\n \"acc\": 0.2569444444444444,\n \"acc_stderr\": 0.03653946969442099,\n\ \ \"acc_norm\": 0.2569444444444444,\n \"acc_norm_stderr\": 0.03653946969442099\n\ \ },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\":\ \ 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n\ \ \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness\|hendrycksTest-college_computer_science\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\ \ \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.18,\n\ \ \"acc_stderr\": 0.038612291966536934,\n \"acc_norm\": 0.18,\n \ \ \"acc_norm_stderr\": 0.038612291966536934\n },\n \"harness\|hendrycksTest-college_medicine\|5\"\ : {\n \"acc\": 0.20809248554913296,\n \"acc_stderr\": 0.030952890217749874,\n\ \ \"acc_norm\": 0.20809248554913296,\n \"acc_norm_stderr\": 0.030952890217749874\n\ \ },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.21568627450980393,\n\ \ \"acc_stderr\": 0.04092563958237654,\n \"acc_norm\": 0.21568627450980393,\n\ \ \"acc_norm_stderr\": 0.04092563958237654\n },\n \"harness\|hendrycksTest-computer_security\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\":\ \ 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n \"\ acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\ acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\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\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"\ acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.15763546798029557,\n \"acc_stderr\": 0.025639014131172404,\n \"\ acc_norm\": 0.15763546798029557,\n \"acc_norm_stderr\": 0.025639014131172404\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.17,\n \"acc_stderr\": 0.037752516806863715,\n \"acc_norm\"\ : 0.17,\n \"acc_norm_stderr\": 0.037752516806863715\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\ acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n\ \ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.20256410256410257,\n \"acc_stderr\": 0.020377660970371372,\n\ \ \"acc_norm\": 0.20256410256410257,\n \"acc_norm_stderr\": 0.020377660970371372\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \ \ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.24789915966386555,\n \"acc_stderr\": 0.028047967224176892,\n\ \ \"acc_norm\": 0.24789915966386555,\n \"acc_norm_stderr\": 0.028047967224176892\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"\ acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"\ acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"\ acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\ \ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.31390134529147984,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.2900763358778626,\n \"acc_stderr\": 0.03980066246467765,\n\ \ \"acc_norm\": 0.2900763358778626,\n \"acc_norm_stderr\": 0.03980066246467765\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.2892561983471074,\n \"acc_stderr\": 0.041391127276354626,\n \"\ acc_norm\": 0.2892561983471074,\n \"acc_norm_stderr\": 0.041391127276354626\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.2962962962962963,\n\ \ \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.2962962962962963,\n\ \ \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.3125,\n\ \ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\ \ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\ \ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.2905982905982906,\n\ \ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\ \ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.22349936143039592,\n\ \ \"acc_stderr\": 0.014897235229450707,\n \"acc_norm\": 0.22349936143039592,\n\ \ \"acc_norm_stderr\": 0.014897235229450707\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.26878612716763006,\n \"acc_stderr\": 0.023868003262500114,\n\ \ \"acc_norm\": 0.26878612716763006,\n \"acc_norm_stderr\": 0.023868003262500114\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2435754189944134,\n\ \ \"acc_stderr\": 0.014355911964767864,\n \"acc_norm\": 0.2435754189944134,\n\ \ \"acc_norm_stderr\": 0.014355911964767864\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.2508038585209003,\n\ \ \"acc_stderr\": 0.024619771956697165,\n \"acc_norm\": 0.2508038585209003,\n\ \ \"acc_norm_stderr\": 0.024619771956697165\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\ \ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \ \ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\ \ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness\|hendrycksTest-public_relations\|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n\ \ },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.18775510204081633,\n\ \ \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n\ \ \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness\|hendrycksTest-sociology\|5\"\ : {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n\ \ \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n\ \ },\n \"harness\|hendrycksTest-us_foreign_policy\|5\": {\n \"acc\":\ \ 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-virology\|5\"\ : {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n\ \ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n\ \ },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.3216374269005848,\n\ \ \"acc_stderr\": 0.03582529442573122,\n \"acc_norm\": 0.3216374269005848,\n\ \ \"acc_norm_stderr\": 0.03582529442573122\n },\n \"harness\|truthfulqa:mc\|0\"\ : {\n \"mc1\": 1.0,\n \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n\ \ \"mc2_stderr\": NaN\n },\n \"harness\|winogrande\|5\": {\n \"\ acc\": 0.4988161010260458,\n \"acc_stderr\": 0.014052446290529019\n },\n\ \ \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n\ \ }\n}\n```" repo_url: https://huggingface.co/alnrg2arg/test_wanda_240109 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_13T17_14_43.764095 path: - '/details_harness\|arc:challenge\|25_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|arc:challenge\|25_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-13T17-19-19.094893.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|gsm8k\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|gsm8k\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hellaswag\|10_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hellaswag\|10_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T17-14-43.764095.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T17-19-19.094893.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-13T17-19-19.094893.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '/details_harness\|winogrande\|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '/details_harness\|winogrande\|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-13T17-19-19.094893.parquet' - config_name: results data_files: - split: 2024_01_13T17_14_43.764095 path: - results_2024-01-13T17-14-43.764095.parquet - split: 2024_01_13T17_19_19.094893 path: - results_2024-01-13T17-19-19.094893.parquet - split: latest path: - results_2024-01-13T17-19-19.094893.parquet --- # Dataset Card for Evaluation run of alnrg2arg/test_wanda_240109 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [alnrg2arg/test_wanda_240109](https://huggingface.co/alnrg2arg/test_wanda_240109) 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_alnrg2arg__test_wanda_240109", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T17:19:19.094893](https://huggingface.co/datasets/open-llm-leaderboard/details_alnrg2arg__test_wanda_240109/blob/main/results_2024-01-13T17-19-19.094893.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": 0.23401038489636644, "acc_stderr": 0.029968361313724278, "acc_norm": 0.23351347966222002, "acc_norm_stderr": 0.0307471687800331, "mc1": 1.0, "mc1_stderr": 0.0, "mc2": NaN, "mc2_stderr": NaN }, "harness\|arc:challenge\|25": { "acc": 0.22525597269624573, "acc_stderr": 0.012207839995407305, "acc_norm": 0.2295221843003413, "acc_norm_stderr": 0.012288926760890797 }, "harness\|hellaswag\|10": { "acc": 0.25542720573590916, "acc_stderr": 0.004352098082984431, "acc_norm": 0.2526389165504879, "acc_norm_stderr": 0.004336375492801798 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.22, "acc_stderr": 0.04163331998932268, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932268 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.18518518518518517, "acc_stderr": 0.03355677216313142, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.03355677216313142 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.29605263157894735, "acc_stderr": 0.03715062154998904, "acc_norm": 0.29605263157894735, "acc_norm_stderr": 0.03715062154998904 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768077, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768077 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.21509433962264152, "acc_stderr": 0.02528839450289137, "acc_norm": 0.21509433962264152, "acc_norm_stderr": 0.02528839450289137 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.2, "acc_stderr": 0.04020151261036845, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036845 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.20809248554913296, "acc_stderr": 0.030952890217749874, "acc_norm": 0.20809248554913296, "acc_norm_stderr": 0.030952890217749874 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.26382978723404255, "acc_stderr": 0.028809989854102973, "acc_norm": 0.26382978723404255, "acc_norm_stderr": 0.028809989854102973 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813365, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813365 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.20899470899470898, "acc_stderr": 0.02094048156533486, "acc_norm": 0.20899470899470898, "acc_norm_stderr": 0.02094048156533486 }, "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": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.1774193548387097, "acc_stderr": 0.02173254068932927, "acc_norm": 0.1774193548387097, "acc_norm_stderr": 0.02173254068932927 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.15763546798029557, "acc_stderr": 0.025639014131172404, "acc_norm": 0.15763546798029557, "acc_norm_stderr": 0.025639014131172404 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.17, "acc_stderr": 0.037752516806863715, "acc_norm": 0.17, "acc_norm_stderr": 0.037752516806863715 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.17676767676767677, "acc_stderr": 0.027178752639044915, "acc_norm": 0.17676767676767677, "acc_norm_stderr": 0.027178752639044915 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.19689119170984457, "acc_stderr": 0.028697873971860664, "acc_norm": 0.19689119170984457, "acc_norm_stderr": 0.028697873971860664 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.20256410256410257, "acc_stderr": 0.020377660970371372, "acc_norm": 0.20256410256410257, "acc_norm_stderr": 0.020377660970371372 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2111111111111111, "acc_stderr": 0.024882116857655075, "acc_norm": 0.2111111111111111, "acc_norm_stderr": 0.024882116857655075 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.24789915966386555, "acc_stderr": 0.028047967224176892, "acc_norm": 0.24789915966386555, "acc_norm_stderr": 0.028047967224176892 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.1986754966887417, "acc_stderr": 0.03257847384436776, "acc_norm": 0.1986754966887417, "acc_norm_stderr": 0.03257847384436776 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.1926605504587156, "acc_stderr": 0.016909276884936094, "acc_norm": 0.1926605504587156, "acc_norm_stderr": 0.016909276884936094 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.1527777777777778, "acc_stderr": 0.024536326026134224, "acc_norm": 0.1527777777777778, "acc_norm_stderr": 0.024536326026134224 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.270042194092827, "acc_stderr": 0.028900721906293426, "acc_norm": 0.270042194092827, "acc_norm_stderr": 0.028900721906293426 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.31390134529147984, "acc_stderr": 0.031146796482972465, "acc_norm": 0.31390134529147984, "acc_norm_stderr": 0.031146796482972465 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.2900763358778626, "acc_stderr": 0.03980066246467765, "acc_norm": 0.2900763358778626, "acc_norm_stderr": 0.03980066246467765 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2892561983471074, "acc_stderr": 0.041391127276354626, "acc_norm": 0.2892561983471074, "acc_norm_stderr": 0.041391127276354626 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.2962962962962963, "acc_stderr": 0.044143436668549335, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.044143436668549335 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.22085889570552147, "acc_stderr": 0.032591773927421776, "acc_norm": 0.22085889570552147, "acc_norm_stderr": 0.032591773927421776 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.3125, "acc_stderr": 0.043994650575715215, "acc_norm": 0.3125, "acc_norm_stderr": 0.043994650575715215 }, "harness\|hendrycksTest-management\|5": { "acc": 0.17475728155339806, "acc_stderr": 0.037601780060266224, "acc_norm": 0.17475728155339806, "acc_norm_stderr": 0.037601780060266224 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.2905982905982906, "acc_stderr": 0.02974504857267404, "acc_norm": 0.2905982905982906, "acc_norm_stderr": 0.02974504857267404 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.22349936143039592, "acc_stderr": 0.014897235229450707, "acc_norm": 0.22349936143039592, "acc_norm_stderr": 0.014897235229450707 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.26878612716763006, "acc_stderr": 0.023868003262500114, "acc_norm": 0.26878612716763006, "acc_norm_stderr": 0.023868003262500114 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2435754189944134, "acc_stderr": 0.014355911964767864, "acc_norm": 0.2435754189944134, "acc_norm_stderr": 0.014355911964767864 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.22549019607843138, "acc_stderr": 0.023929155517351284, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.023929155517351284 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.2508038585209003, "acc_stderr": 0.024619771956697165, "acc_norm": 0.2508038585209003, "acc_norm_stderr": 0.024619771956697165 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.21604938271604937, "acc_stderr": 0.022899162918445806, "acc_norm": 0.21604938271604937, "acc_norm_stderr": 0.022899162918445806 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.23404255319148937, "acc_stderr": 0.025257861359432417, "acc_norm": 0.23404255319148937, "acc_norm_stderr": 0.025257861359432417 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.18382352941176472, "acc_stderr": 0.023529242185193106, "acc_norm": 0.18382352941176472, "acc_norm_stderr": 0.023529242185193106 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.25, "acc_stderr": 0.01751781884501444, "acc_norm": 0.25, "acc_norm_stderr": 0.01751781884501444 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03955932861795833, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03955932861795833 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.18775510204081633, "acc_stderr": 0.02500025603954621, "acc_norm": 0.18775510204081633, "acc_norm_stderr": 0.02500025603954621 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.24378109452736318, "acc_stderr": 0.03036049015401465, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.28313253012048195, "acc_stderr": 0.03507295431370518, "acc_norm": 0.28313253012048195, "acc_norm_stderr": 0.03507295431370518 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.3216374269005848, "acc_stderr": 0.03582529442573122, "acc_norm": 0.3216374269005848, "acc_norm_stderr": 0.03582529442573122 }, "harness\|truthfulqa:mc\|0": { "mc1": 1.0, "mc1_stderr": 0.0, "mc2": NaN, "mc2_stderr": NaN }, "harness\|winogrande\|5": { "acc": 0.4988161010260458, "acc_stderr": 0.014052446290529019 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
aditijha/instruct_v3_10k	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: source dtype: string splits: - name: train num_bytes: 39309622.55416882 num_examples: 10000 download_size: 23617961 dataset_size: 39309622.55416882 --- # Dataset Card for "instruct_v3_10k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nlpso/m0_qualitative_analysis_ref_ptrn_cmbert_io	--- language: - fr multilinguality: - monolingual task_categories: - token-classification --- # m0_qualitative_analysis_ref_ptrn_cmbert_io ## Introduction This dataset was used to perform qualitative analysis of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on flat NER task using Flat NER approach [M0]. It contains 19th-century Paris trade directories' entries. ## Dataset parameters * Approach : M0 * Dataset type : ground-truth * Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) * Tagging format : IO * Counts : * Train : 6084 * Dev : 676 * Test : 1685 * Associated fine-tuned model : [nlpso/m0_flat_ner_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ref_ptrn_cmbert_io) ## Entity types Abbreviation\|Description -\|- O \|Outside of a named entity PER \|Person or company name ACT \|Person or company professional activity TITRE \|Distinction LOC \|Street name CARDINAL \|Street number FT \|Geographical feature ## How to use this dataset ```python from datasets import load_dataset train_dev_test = load_dataset("nlpso/m0_qualitative_analysis_ref_ptrn_cmbert_io")
Maeda-miyazaki/dataset_750	--- license: cc-by-nc-3.0 ---
chats-bug/red-pyjama-sample-1T-max-chunk-16k	--- dataset_info: features: - name: text dtype: string - name: meta dtype: string splits: - name: train num_bytes: 5266104478.188356 num_examples: 924172 - name: test num_bytes: 53198269.811643824 num_examples: 9336 download_size: 3092233105 dataset_size: 5319302748.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
CyberHarem/marina_akizuki_onichichi	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Marina Akizuki This is the dataset of Marina Akizuki, containing 300 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 \| 300 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 588 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 730 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 300 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 300 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 300 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 588 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 588 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 502 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 730 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 730 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|
bengisucam/tr_dataset_combined	--- license: apache-2.0 dataset_info: features: - name: Text dtype: string splits: - name: train num_bytes: 167603259 num_examples: 824809 download_size: 106342453 dataset_size: 167603259 configs: - config_name: default data_files: - split: train path: data/train-* language: - tr --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> This dataset is the combination of the datasets listed below: - BDas/Turkish-Dataset - turkish_product_reviews - winvoker/turkish-sentiment-analysis-dataset - 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]
arthurmluz/xlsum_data-xlsum_gptextsum_results	--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: summary dtype: string - name: text dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 splits: - name: validation num_bytes: 26244213 num_examples: 7175 download_size: 15951725 dataset_size: 26244213 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "wikilingua_data-xlsum_gptextsum_results" rouge= {'rouge1': 0.3230756314331615, 'rouge2': 0.12295752023585965, 'rougeL': 0.23099240967982115, 'rougeLsum': 0.23099240967982115} bert= {'precision': 0.7382304361929877, 'recall': 0.7454116297765061, 'f1': 0.7414375136205958}
lilacai/lilac-Capybara	--- tags: - Lilac --- # lilac/Capybara This dataset is a [Lilac](http://lilacml.com) processed dataset. Original dataset: [https://huggingface.co/datasets/LDJnr/Capybara](https://huggingface.co/datasets/LDJnr/Capybara) To download the dataset to a local directory: ```bash lilac download lilacai/lilac-Capybara ``` or from python with: ```py ll.download("lilacai/lilac-Capybara") ```
one-sec-cv12/chunk_105	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 18310590720.0 num_examples: 190640 download_size: 16453314083 dataset_size: 18310590720.0 --- # Dataset Card for "chunk_105" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jaja7744/dolly-15k-cn	--- license: apache-2.0 task_categories: - text-generation language: - zh pretty_name: d size_categories: - 10K<n<100K ---
irds/lotte_recreation_test_search	--- pretty_name: '`lotte/recreation/test/search`' viewer: false source_datasets: ['irds/lotte_recreation_test'] task_categories: - text-retrieval --- # Dataset Card for `lotte/recreation/test/search` The `lotte/recreation/test/search` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/recreation/test/search). # Data This dataset provides: - `queries` (i.e., topics); count=924 - `qrels`: (relevance assessments); count=1,991 - For `docs`, use [`irds/lotte_recreation_test`](https://huggingface.co/datasets/irds/lotte_recreation_test) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/lotte_recreation_test_search', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/lotte_recreation_test_search', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Santhanam2021ColBERTv2, title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction", author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia", journal= "arXiv preprint arXiv:2112.01488", year = "2021", url = "https://arxiv.org/abs/2112.01488" } ```
AliEdalat/Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model	--- license: apache-2.0 task_categories: - text-generation - conversational language: - fa size_categories: - 1K<n<10K --- # Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model Persian ChatBot dataset, fine-tune LLaMa on instructed data (preprocessed alpaca dataset). [GitHub](https://github.com/AliEdalat/ChatBot_for_persian_LLaMA_fine_tune.git) - we use [preprocessed alpaca dataset](https://github.com/thisserand/alpaca-lora-finetune-language.git) as a dataset. we translate no_translate data to persian with [mt5](https://huggingface.co/persiannlp/mt5-large-parsinlu-translation_en_fa). ([train dataset](https://huggingface.co/datasets/AliEdalat/Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model/tree/main) and [test data](https://huggingface.co/datasets/AliEdalat/Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model/tree/main) with 2k example is ready) - we use LLaMA as a generative model for creating a chatbot model. we fine-tune the model with our Persian dataset and test it. - for improving ChatBot performance, replace "برای اینکه این کار را بکنم" with ""
Defetya/eval_open_llama_ru	--- license: apache-2.0 ---
SS3830/image-search-sa	--- license: apache-2.0 dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 28874455.946 num_examples: 2378 download_size: 24014632 dataset_size: 28874455.946 configs: - config_name: default data_files: - split: train path: data/train-* ---
zolak/twitter_dataset_80_1713214788	--- 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: 1420630 num_examples: 3501 download_size: 719400 dataset_size: 1420630 configs: - config_name: default data_files: - split: train path: data/train-* ---
ikuldeep1/vehicle-damage-fraud-image-balanced	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 1716555477.626 num_examples: 12729 download_size: 1433374572 dataset_size: 1716555477.626 configs: - config_name: default data_files: - split: train path: data/train-* ---
autoevaluate/autoeval-eval-futin__feed-sen_vi-894567-2175669982	--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: facebook/opt-2.7b metrics: [] dataset_name: futin/feed dataset_config: sen_vi dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-2.7b * Dataset: futin/feed * Config: sen_vi * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.
chathuranga-jayanath/context-5-rhino-finmath-times4j-html-mavendoxia-wro4j-guava-supercsv-len-10000-prompt-1	--- dataset_info: features: - name: id dtype: int64 - name: filepath dtype: string - name: start_bug_line dtype: int64 - name: end_bug_line dtype: int64 - name: bug dtype: string - name: fix dtype: string - name: ctx dtype: string splits: - name: train num_bytes: 28232907 num_examples: 45517 - name: validation num_bytes: 3535186 num_examples: 5689 - name: test num_bytes: 3535341 num_examples: 5689 download_size: 14548547 dataset_size: 35303434 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
TecnicaLLM/dolly-15k	--- license: cc-by-sa-3.0 ---
projectbaraat/kannada-Mathematical	--- dataset_info: features: - name: input dtype: string - name: response dtype: string splits: - name: train num_bytes: 519631610 num_examples: 335690 download_size: 169797917 dataset_size: 519631610 configs: - config_name: default data_files: - split: train path: data/train-* ---
Yevhenii1234/test	--- license: apache-2.0 ---
presencesw/dataset_2000_complexquestion_3	--- dataset_info: features: - name: entities sequence: 'null' - name: triplets sequence: 'null' - name: answer dtype: string - name: complex_question dtype: string splits: - name: train num_bytes: 17911 num_examples: 200 download_size: 0 dataset_size: 17911 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dataset_2000_complexquestion_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Bluepelt/Idkwhattodomate	--- license: mit ---
connorhoehn/trading_card_display_classification_1_5k_v3	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': grid '1': solo '2': spread '3': stack splits: - name: train num_bytes: 1127230775.103 num_examples: 1249 - name: test num_bytes: 155934991.0 num_examples: 307 download_size: 1317201819 dataset_size: 1283165766.103 --- # Dataset Card for "trading_card_display_classification_1_5k_v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
freshpearYoon/v3_train_free_concat_20	--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 3842535816 num_examples: 2500 download_size: 1826001643 dataset_size: 3842535816 configs: - config_name: default data_files: - split: train path: data/train-* ---
Falah/arabic_islamic_fashion_prompts_SDXL	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 239684574 num_examples: 1000000 download_size: 27020301 dataset_size: 239684574 --- # Dataset Card for "arabic_islamic_fashion_prompts_SDXL" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Indic-Benchmark/nepali-arc-c-2.5k	--- dataset_info: features: - name: id dtype: string - name: question struct: - name: choices list: - name: label dtype: string - name: text dtype: string - name: stem dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 1841172 num_examples: 2584 download_size: 706909 dataset_size: 1841172 configs: - config_name: default data_files: - split: train path: data/train-* ---
dot-ammar/AR-dotted-mediumPlus	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: clean dtype: string splits: - name: train num_bytes: 387187864 num_examples: 1625508 download_size: 214233397 dataset_size: 387187864 --- # Dataset Card for "AR-dotted-mediumPlus-arrow" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Thermostatic/parallel_corpus_webcrawl_english_spanish_1	--- license: cc-by-4.0 task_categories: - translation language: - en - es tags: - English - Spanish - Parallel corpus size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This parallel corpus dataset contains about 21k rows of parallel English and Spanish texts obtained by crawling different websites. It has been filtered strictly. ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> This is a parallel corpus of bilingual texts crawled from multilingual websites, which contains 21, 005 TUs. A strict validation process has been followed, which resulted in discarding: - TUs from crawled websites that do not comply to the PSI directive, - TUs with more than 99% of mispelled tokens, - TUs identified during the manual validation process and all the TUs from websites which error rate in the sample extracted for manual validation is strictly above the following thresholds: 50% of TUs with language identification errors, 50% of TUs with alignment errors, 50% of TUs with tokenization errors, 20% of TUs identified as machine translated content, 50% of TUs with translation errors. - Period of crawling: 15/11/2016 - 23/01/2017 (DD/MM/YY). - Curated by: Directorate-General for Communications Networks, Content and Technology. - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): English & Spanish - License: cc-by-4.0 ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: http://data.europa.eu/88u/dataset/elrc_339 - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> This dataset is perfect for training Machine Translation algorithms. ### 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]
autoevaluate/autoeval-staging-eval-project-0b0f26eb-7664951	--- type: predictions tags: - autotrain - evaluation datasets: - lener_br eval_info: task: entity_extraction model: Luciano/bertimbau-large-lener_br metrics: [] dataset_name: lener_br dataset_config: lener_br dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: Luciano/bertimbau-large-lener_br * Dataset: lener_br To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
tasksource/sherliic	--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: string splits: - name: train num_bytes: 106784 num_examples: 996 - name: test num_bytes: 322932 num_examples: 2989 download_size: 146406 dataset_size: 429716 language: - en --- # Dataset Card for "sherliic" https://github.com/mnschmit/SherLIiC ``` @inproceedings{schmitt2019sherliic, title = "{S}her{LI}i{C}: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference", author = {Schmitt, Martin and Sch{\"u}tze, Hinrich}, booktitle = "Proceedings of the 57th Conference of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1086", pages = "902--914" } ```

Dialogue-Model-Research-Group/baike

--- license: cc ---

CyberHarem/kawashiro_mitori_touhou

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of kawashiro_mitori/河城みとり (Touhou) This is the dataset of kawashiro_mitori/河城みとり (Touhou), containing 25 images and their tags. The core tags of this character are `hair_ornament, hat, short_hair, red_eyes, pink_hair, side_ponytail`, 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 | 25 | 21.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawashiro_mitori_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 25 | 15.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawashiro_mitori_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 44 | 26.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawashiro_mitori_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 25 | 20.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawashiro_mitori_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 44 | 31.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawashiro_mitori_touhou/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/kawashiro_mitori_touhou', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------| | 0 | 25 | ![](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) | hair_bobbles, 1girl, solo, lock, layered_sleeves, blush, skirt, road_sign | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | hair_bobbles | 1girl | solo | lock | layered_sleeves | blush | skirt | road_sign | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------|:--------|:-------|:-------|:------------------|:--------|:--------|:------------| | 0 | 25 | ![](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 |

iElexperio/processedMorDataLLMv3

--- dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: bboxes sequence: sequence: int64 - name: ner_tags sequence: int64 - name: image dtype: image splits: - name: train num_bytes: 8868049.0 num_examples: 70 - name: test num_bytes: 3462408.0 num_examples: 28 download_size: 11436065 dataset_size: 12330457.0 --- # Dataset Card for "processedMorDataLLMv3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_beberik__TinyExperts-v0-4x1B

--- pretty_name: Evaluation run of beberik/TinyExperts-v0-4x1B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [beberik/TinyExperts-v0-4x1B](https://huggingface.co/beberik/TinyExperts-v0-4x1B)\ \ 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_beberik__TinyExperts-v0-4x1B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-20T21:54:19.124713](https://huggingface.co/datasets/open-llm-leaderboard/details_beberik__TinyExperts-v0-4x1B/blob/main/results_2023-12-20T21-54-19.124713.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\": 0.26252295554837873,\n\ \ \"acc_stderr\": 0.031072019491044735,\n \"acc_norm\": 0.2641174998312261,\n\ \ \"acc_norm_stderr\": 0.03186997959528178,\n \"mc1\": 0.24969400244798043,\n\ \ \"mc1_stderr\": 0.015152286907148128,\n \"mc2\": 0.41126558330324914,\n\ \ \"mc2_stderr\": 0.014912649441030584\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.27047781569965873,\n \"acc_stderr\": 0.012980954547659554,\n\ \ \"acc_norm\": 0.31399317406143346,\n \"acc_norm_stderr\": 0.013562691224726295\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.3906592312288389,\n\ \ \"acc_stderr\": 0.0048690101522807505,\n \"acc_norm\": 0.522903804023103,\n\ \ \"acc_norm_stderr\": 0.0049845435409323355\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3333333333333333,\n\ \ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.3333333333333333,\n\ \ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.3223684210526316,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.3223684210526316,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.22,\n\ \ \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \ \ \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.20754716981132076,\n \"acc_stderr\": 0.024959918028911274,\n\ \ \"acc_norm\": 0.20754716981132076,\n \"acc_norm_stderr\": 0.024959918028911274\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\ \ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\ \ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653694,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653694\n \ \ },\n \"harness|hendrycksTest-college_computer_science|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-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.2543352601156069,\n\ \ \"acc_stderr\": 0.0332055644308557,\n \"acc_norm\": 0.2543352601156069,\n\ \ \"acc_norm_stderr\": 0.0332055644308557\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.045766654032077636,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.045766654032077636\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.20425531914893616,\n \"acc_stderr\": 0.026355158413349424,\n\ \ \"acc_norm\": 0.20425531914893616,\n \"acc_norm_stderr\": 0.026355158413349424\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n\ \ \"acc_stderr\": 0.04049339297748141,\n \"acc_norm\": 0.24561403508771928,\n\ \ \"acc_norm_stderr\": 0.04049339297748141\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.296551724137931,\n \"acc_stderr\": 0.03806142687309993,\n\ \ \"acc_norm\": 0.296551724137931,\n \"acc_norm_stderr\": 0.03806142687309993\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.2671957671957672,\n \"acc_stderr\": 0.022789673145776568,\n \"\ acc_norm\": 0.2671957671957672,\n \"acc_norm_stderr\": 0.022789673145776568\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15873015873015872,\n\ \ \"acc_stderr\": 0.03268454013011744,\n \"acc_norm\": 0.15873015873015872,\n\ \ \"acc_norm_stderr\": 0.03268454013011744\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.25483870967741934,\n \"acc_stderr\": 0.02479011845933221,\n \"\ acc_norm\": 0.25483870967741934,\n \"acc_norm_stderr\": 0.02479011845933221\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.2955665024630542,\n \"acc_stderr\": 0.032104944337514575,\n \"\ acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.032104944337514575\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.2545454545454545,\n \"acc_stderr\": 0.03401506715249039,\n\ \ \"acc_norm\": 0.2545454545454545,\n \"acc_norm_stderr\": 0.03401506715249039\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.23737373737373738,\n \"acc_stderr\": 0.030313710538198892,\n \"\ acc_norm\": 0.23737373737373738,\n \"acc_norm_stderr\": 0.030313710538198892\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.22279792746113988,\n \"acc_stderr\": 0.03003114797764154,\n\ \ \"acc_norm\": 0.22279792746113988,\n \"acc_norm_stderr\": 0.03003114797764154\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2282051282051282,\n \"acc_stderr\": 0.021278393863586282,\n\ \ \"acc_norm\": 0.2282051282051282,\n \"acc_norm_stderr\": 0.021278393863586282\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.26666666666666666,\n \"acc_stderr\": 0.02696242432507383,\n \ \ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.02696242432507383\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\ \ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2913907284768212,\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\"\ : 0.22385321100917432,\n \"acc_stderr\": 0.017871217767790222,\n \"\ acc_norm\": 0.22385321100917432,\n \"acc_norm_stderr\": 0.017871217767790222\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.029531221160930918,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.029531221160930918\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.03039153369274154\n \ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\"\ : 0.22784810126582278,\n \"acc_stderr\": 0.02730348459906941,\n \"\ acc_norm\": 0.22784810126582278,\n \"acc_norm_stderr\": 0.02730348459906941\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.29596412556053814,\n\ \ \"acc_stderr\": 0.030636591348699792,\n \"acc_norm\": 0.29596412556053814,\n\ \ \"acc_norm_stderr\": 0.030636591348699792\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.371900826446281,\n \"acc_stderr\": 0.044120158066245044,\n \"\ acc_norm\": 0.371900826446281,\n \"acc_norm_stderr\": 0.044120158066245044\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.21296296296296297,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.3067484662576687,\n \"acc_stderr\": 0.036230899157241474,\n\ \ \"acc_norm\": 0.3067484662576687,\n \"acc_norm_stderr\": 0.036230899157241474\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.24107142857142858,\n\ \ \"acc_stderr\": 0.04059867246952687,\n \"acc_norm\": 0.24107142857142858,\n\ \ \"acc_norm_stderr\": 0.04059867246952687\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.20388349514563106,\n \"acc_stderr\": 0.03989139859531771,\n\ \ \"acc_norm\": 0.20388349514563106,\n \"acc_norm_stderr\": 0.03989139859531771\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.24358974358974358,\n\ \ \"acc_stderr\": 0.028120966503914397,\n \"acc_norm\": 0.24358974358974358,\n\ \ \"acc_norm_stderr\": 0.028120966503914397\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.26053639846743293,\n\ \ \"acc_stderr\": 0.0156960085638071,\n \"acc_norm\": 0.26053639846743293,\n\ \ \"acc_norm_stderr\": 0.0156960085638071\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.28901734104046245,\n \"acc_stderr\": 0.02440517393578323,\n\ \ \"acc_norm\": 0.28901734104046245,\n \"acc_norm_stderr\": 0.02440517393578323\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n\ \ \"acc_stderr\": 0.014422292204808835,\n \"acc_norm\": 0.24692737430167597,\n\ \ \"acc_norm_stderr\": 0.014422292204808835\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.24836601307189543,\n \"acc_stderr\": 0.02473998135511359,\n\ \ \"acc_norm\": 0.24836601307189543,\n \"acc_norm_stderr\": 0.02473998135511359\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2990353697749196,\n\ \ \"acc_stderr\": 0.026003301117885135,\n \"acc_norm\": 0.2990353697749196,\n\ \ \"acc_norm_stderr\": 0.026003301117885135\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.26851851851851855,\n \"acc_stderr\": 0.02465968518596728,\n\ \ \"acc_norm\": 0.26851851851851855,\n \"acc_norm_stderr\": 0.02465968518596728\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2695035460992908,\n \"acc_stderr\": 0.02646903681859063,\n \ \ \"acc_norm\": 0.2695035460992908,\n \"acc_norm_stderr\": 0.02646903681859063\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.258148631029987,\n\ \ \"acc_stderr\": 0.01117692371931339,\n \"acc_norm\": 0.258148631029987,\n\ \ \"acc_norm_stderr\": 0.01117692371931339\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.23161764705882354,\n \"acc_stderr\": 0.025626533803777562,\n\ \ \"acc_norm\": 0.23161764705882354,\n \"acc_norm_stderr\": 0.025626533803777562\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.2581699346405229,\n \"acc_stderr\": 0.017704531653250078,\n \ \ \"acc_norm\": 0.2581699346405229,\n \"acc_norm_stderr\": 0.017704531653250078\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.20909090909090908,\n\ \ \"acc_stderr\": 0.038950910157241364,\n \"acc_norm\": 0.20909090909090908,\n\ \ \"acc_norm_stderr\": 0.038950910157241364\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.24489795918367346,\n \"acc_stderr\": 0.027529637440174923,\n\ \ \"acc_norm\": 0.24489795918367346,\n \"acc_norm_stderr\": 0.027529637440174923\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23880597014925373,\n\ \ \"acc_stderr\": 0.030147775935409217,\n \"acc_norm\": 0.23880597014925373,\n\ \ \"acc_norm_stderr\": 0.030147775935409217\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \ \ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.2289156626506024,\n\ \ \"acc_stderr\": 0.03270745277352477,\n \"acc_norm\": 0.2289156626506024,\n\ \ \"acc_norm_stderr\": 0.03270745277352477\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.27485380116959063,\n \"acc_stderr\": 0.034240429246915824,\n\ \ \"acc_norm\": 0.27485380116959063,\n \"acc_norm_stderr\": 0.034240429246915824\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.24969400244798043,\n\ \ \"mc1_stderr\": 0.015152286907148128,\n \"mc2\": 0.41126558330324914,\n\ \ \"mc2_stderr\": 0.014912649441030584\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.601420678768745,\n \"acc_stderr\": 0.01376035717687383\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.00530705079605762,\n \ \ \"acc_stderr\": 0.002001305720948071\n }\n}\n```" repo_url: https://huggingface.co/beberik/TinyExperts-v0-4x1B 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_12_20T21_54_19.124713 path: - '**/details_harness|arc:challenge|25_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-20T21-54-19.124713.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|gsm8k|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hellaswag|10_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-20T21-54-19.124713.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-management|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-20T21-54-19.124713.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|truthfulqa:mc|0_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-20T21-54-19.124713.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_20T21_54_19.124713 path: - '**/details_harness|winogrande|5_2023-12-20T21-54-19.124713.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-20T21-54-19.124713.parquet' - config_name: results data_files: - split: 2023_12_20T21_54_19.124713 path: - results_2023-12-20T21-54-19.124713.parquet - split: latest path: - results_2023-12-20T21-54-19.124713.parquet --- # Dataset Card for Evaluation run of beberik/TinyExperts-v0-4x1B  Dataset automatically created during the evaluation run of model [beberik/TinyExperts-v0-4x1B](https://huggingface.co/beberik/TinyExperts-v0-4x1B) 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_beberik__TinyExperts-v0-4x1B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-20T21:54:19.124713](https://huggingface.co/datasets/open-llm-leaderboard/details_beberik__TinyExperts-v0-4x1B/blob/main/results_2023-12-20T21-54-19.124713.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": 0.26252295554837873, "acc_stderr": 0.031072019491044735, "acc_norm": 0.2641174998312261, "acc_norm_stderr": 0.03186997959528178, "mc1": 0.24969400244798043, "mc1_stderr": 0.015152286907148128, "mc2": 0.41126558330324914, "mc2_stderr": 0.014912649441030584 }, "harness|arc:challenge|25": { "acc": 0.27047781569965873, "acc_stderr": 0.012980954547659554, "acc_norm": 0.31399317406143346, "acc_norm_stderr": 0.013562691224726295 }, "harness|hellaswag|10": { "acc": 0.3906592312288389, "acc_stderr": 0.0048690101522807505, "acc_norm": 0.522903804023103, "acc_norm_stderr": 0.0049845435409323355 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04072314811876837, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04072314811876837 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.3223684210526316, "acc_stderr": 0.03803510248351585, "acc_norm": 0.3223684210526316, "acc_norm_stderr": 0.03803510248351585 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.20754716981132076, "acc_stderr": 0.024959918028911274, "acc_norm": 0.20754716981132076, "acc_norm_stderr": 0.024959918028911274 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.18, "acc_stderr": 0.03861229196653694, "acc_norm": 0.18, "acc_norm_stderr": 0.03861229196653694 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2543352601156069, "acc_stderr": 0.0332055644308557, "acc_norm": 0.2543352601156069, "acc_norm_stderr": 0.0332055644308557 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.30392156862745096, "acc_stderr": 0.045766654032077636, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.045766654032077636 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.20425531914893616, "acc_stderr": 0.026355158413349424, "acc_norm": 0.20425531914893616, "acc_norm_stderr": 0.026355158413349424 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.24561403508771928, "acc_stderr": 0.04049339297748141, "acc_norm": 0.24561403508771928, "acc_norm_stderr": 0.04049339297748141 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.296551724137931, "acc_stderr": 0.03806142687309993, "acc_norm": 0.296551724137931, "acc_norm_stderr": 0.03806142687309993 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2671957671957672, "acc_stderr": 0.022789673145776568, "acc_norm": 0.2671957671957672, "acc_norm_stderr": 0.022789673145776568 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.15873015873015872, "acc_stderr": 0.03268454013011744, "acc_norm": 0.15873015873015872, "acc_norm_stderr": 0.03268454013011744 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.25483870967741934, "acc_stderr": 0.02479011845933221, "acc_norm": 0.25483870967741934, "acc_norm_stderr": 0.02479011845933221 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2955665024630542, "acc_stderr": 0.032104944337514575, "acc_norm": 0.2955665024630542, "acc_norm_stderr": 0.032104944337514575 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.2545454545454545, "acc_stderr": 0.03401506715249039, "acc_norm": 0.2545454545454545, "acc_norm_stderr": 0.03401506715249039 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.23737373737373738, "acc_stderr": 0.030313710538198892, "acc_norm": 0.23737373737373738, "acc_norm_stderr": 0.030313710538198892 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.22279792746113988, "acc_stderr": 0.03003114797764154, "acc_norm": 0.22279792746113988, "acc_norm_stderr": 0.03003114797764154 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2282051282051282, "acc_stderr": 0.021278393863586282, "acc_norm": 0.2282051282051282, "acc_norm_stderr": 0.021278393863586282 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.26666666666666666, "acc_stderr": 0.02696242432507383, "acc_norm": 0.26666666666666666, "acc_norm_stderr": 0.02696242432507383 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.21008403361344538, "acc_stderr": 0.026461398717471874, "acc_norm": 0.21008403361344538, "acc_norm_stderr": 0.026461398717471874 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2913907284768212, "acc_stderr": 0.037101857261199946, "acc_norm": 0.2913907284768212, "acc_norm_stderr": 0.037101857261199946 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.22385321100917432, "acc_stderr": 0.017871217767790222, "acc_norm": 0.22385321100917432, "acc_norm_stderr": 0.017871217767790222 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.25, "acc_stderr": 0.029531221160930918, "acc_norm": 0.25, "acc_norm_stderr": 0.029531221160930918 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.22784810126582278, "acc_stderr": 0.02730348459906941, "acc_norm": 0.22784810126582278, "acc_norm_stderr": 0.02730348459906941 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.29596412556053814, "acc_stderr": 0.030636591348699792, "acc_norm": 0.29596412556053814, "acc_norm_stderr": 0.030636591348699792 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2595419847328244, "acc_stderr": 0.03844876139785271, "acc_norm": 0.2595419847328244, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.371900826446281, "acc_stderr": 0.044120158066245044, "acc_norm": 0.371900826446281, "acc_norm_stderr": 0.044120158066245044 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.21296296296296297, "acc_stderr": 0.0395783547198098, "acc_norm": 0.21296296296296297, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.3067484662576687, "acc_stderr": 0.036230899157241474, "acc_norm": 0.3067484662576687, "acc_norm_stderr": 0.036230899157241474 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.24107142857142858, "acc_stderr": 0.04059867246952687, "acc_norm": 0.24107142857142858, "acc_norm_stderr": 0.04059867246952687 }, "harness|hendrycksTest-management|5": { "acc": 0.20388349514563106, "acc_stderr": 0.03989139859531771, "acc_norm": 0.20388349514563106, "acc_norm_stderr": 0.03989139859531771 }, "harness|hendrycksTest-marketing|5": { "acc": 0.24358974358974358, "acc_stderr": 0.028120966503914397, "acc_norm": 0.24358974358974358, "acc_norm_stderr": 0.028120966503914397 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.26053639846743293, "acc_stderr": 0.0156960085638071, "acc_norm": 0.26053639846743293, "acc_norm_stderr": 0.0156960085638071 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.28901734104046245, "acc_stderr": 0.02440517393578323, "acc_norm": 0.28901734104046245, "acc_norm_stderr": 0.02440517393578323 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24692737430167597, "acc_stderr": 0.014422292204808835, "acc_norm": 0.24692737430167597, "acc_norm_stderr": 0.014422292204808835 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.24836601307189543, "acc_stderr": 0.02473998135511359, "acc_norm": 0.24836601307189543, "acc_norm_stderr": 0.02473998135511359 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2990353697749196, "acc_stderr": 0.026003301117885135, "acc_norm": 0.2990353697749196, "acc_norm_stderr": 0.026003301117885135 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.26851851851851855, "acc_stderr": 0.02465968518596728, "acc_norm": 0.26851851851851855, "acc_norm_stderr": 0.02465968518596728 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2695035460992908, "acc_stderr": 0.02646903681859063, "acc_norm": 0.2695035460992908, "acc_norm_stderr": 0.02646903681859063 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.258148631029987, "acc_stderr": 0.01117692371931339, "acc_norm": 0.258148631029987, "acc_norm_stderr": 0.01117692371931339 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.23161764705882354, "acc_stderr": 0.025626533803777562, "acc_norm": 0.23161764705882354, "acc_norm_stderr": 0.025626533803777562 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2581699346405229, "acc_stderr": 0.017704531653250078, "acc_norm": 0.2581699346405229, "acc_norm_stderr": 0.017704531653250078 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.20909090909090908, "acc_stderr": 0.038950910157241364, "acc_norm": 0.20909090909090908, "acc_norm_stderr": 0.038950910157241364 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.24489795918367346, "acc_stderr": 0.027529637440174923, "acc_norm": 0.24489795918367346, "acc_norm_stderr": 0.027529637440174923 }, "harness|hendrycksTest-sociology|5": { "acc": 0.23880597014925373, "acc_stderr": 0.030147775935409217, "acc_norm": 0.23880597014925373, "acc_norm_stderr": 0.030147775935409217 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.23, "acc_stderr": 0.04229525846816505, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816505 }, "harness|hendrycksTest-virology|5": { "acc": 0.2289156626506024, "acc_stderr": 0.03270745277352477, "acc_norm": 0.2289156626506024, "acc_norm_stderr": 0.03270745277352477 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.27485380116959063, "acc_stderr": 0.034240429246915824, "acc_norm": 0.27485380116959063, "acc_norm_stderr": 0.034240429246915824 }, "harness|truthfulqa:mc|0": { "mc1": 0.24969400244798043, "mc1_stderr": 0.015152286907148128, "mc2": 0.41126558330324914, "mc2_stderr": 0.014912649441030584 }, "harness|winogrande|5": { "acc": 0.601420678768745, "acc_stderr": 0.01376035717687383 }, "harness|gsm8k|5": { "acc": 0.00530705079605762, "acc_stderr": 0.002001305720948071 } } ``` ## 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]

CVdatasets/ImageNet15_animals_unbalanced_aug1

--- dataset_info: features: - name: labels dtype: class_label: names: '0': Italian_greyhound '1': Coyote '2': Beagle '3': Rottweiler '4': Hyena '5': Greater_Swiss_Mountain_dog '6': Triceratops '7': French_bulldog '8': Red_wolf '9': Egyptian_cat '10': Chihuahua '11': Irish_terrier '12': Tiger_cat '13': White_wolf '14': Timber_wolf - name: img dtype: image - name: is_generated dtype: bool splits: - name: validation num_bytes: 60570648.125 num_examples: 1439 - name: train num_bytes: 174270537.875 num_examples: 3705 download_size: 234762621 dataset_size: 234841186.0 --- # Dataset Card for "ImageNet15_animals_unbalanced_aug1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

umd-zhou-lab/Reflect_Wiz70_All

--- dataset_info: features: - name: data struct: - name: instruction dtype: string - name: output dtype: string splits: - name: origin num_bytes: 130900545 num_examples: 70000 - name: reflect_instruction num_bytes: 132137005 num_examples: 70000 - name: reflect_response num_bytes: 170505414 num_examples: 70000 - name: reflect_both num_bytes: 176166017 num_examples: 70000 download_size: 318571646 dataset_size: 609708981 --- # Dataset Card for "Reflect_Wiz70_All" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

KoddaDuck/41_4

--- license: mit ---

bigbio/bioinfer

--- language: - en bigbio_language: - English license: cc-by-2.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_2p0 pretty_name: BioInfer homepage: https://github.com/metalrt/ppi-dataset bigbio_pubmed: True bigbio_public: True bigbio_tasks: - RELATION_EXTRACTION - NAMED_ENTITY_RECOGNITION --- # Dataset Card for BioInfer ## Dataset Description - **Homepage:** https://github.com/metalrt/ppi-dataset - **Pubmed:** True - **Public:** True - **Tasks:** RE,NER A corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. ## Citation Information ``` @article{pyysalo2007bioinfer, title = {BioInfer: a corpus for information extraction in the biomedical domain}, author = { Pyysalo, Sampo and Ginter, Filip and Heimonen, Juho and Bj{"o}rne, Jari and Boberg, Jorma and J{"a}rvinen, Jouni and Salakoski, Tapio }, year = 2007, journal = {BMC bioinformatics}, publisher = {BioMed Central}, volume = 8, number = 1, pages = {1--24} } ```

cahya/instructions-hi

--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 47507115.72180105 num_examples: 49497 - name: test num_bytes: 1250616.639099476 num_examples: 1303 - name: validation num_bytes: 1250616.639099476 num_examples: 1303 download_size: 18697342 dataset_size: 50008349.00000001 --- # Dataset Card for "instructions-hi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

zolak/twitter_dataset_80_1713209820

--- 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: 1472271 num_examples: 3593 download_size: 720363 dataset_size: 1472271 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_Josephgflowers__3BigReasonCinder

--- pretty_name: Evaluation run of Josephgflowers/3BigReasonCinder dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Josephgflowers/3BigReasonCinder](https://huggingface.co/Josephgflowers/3BigReasonCinder)\ \ 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_Josephgflowers__3BigReasonCinder\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-09T12:31:38.090504](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__3BigReasonCinder/blob/main/results_2024-02-09T12-31-38.090504.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\": 0.44794266994933396,\n\ \ \"acc_stderr\": 0.03464503770381712,\n \"acc_norm\": 0.4507932379135084,\n\ \ \"acc_norm_stderr\": 0.03538213666564797,\n \"mc1\": 0.2876376988984088,\n\ \ \"mc1_stderr\": 0.015846315101394816,\n \"mc2\": 0.44764087589469737,\n\ \ \"mc2_stderr\": 0.014703779857331185\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.39078498293515357,\n \"acc_stderr\": 0.014258563880513778,\n\ \ \"acc_norm\": 0.41723549488054607,\n \"acc_norm_stderr\": 0.014409825518403082\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4801832304321848,\n\ \ \"acc_stderr\": 0.004985860853427632,\n \"acc_norm\": 0.6515634335789683,\n\ \ \"acc_norm_stderr\": 0.004755013243022131\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4222222222222222,\n\ \ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.4222222222222222,\n\ \ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.4342105263157895,\n \"acc_stderr\": 0.040335656678483184,\n\ \ \"acc_norm\": 0.4342105263157895,\n \"acc_norm_stderr\": 0.040335656678483184\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"\ acc\": 0.5094339622641509,\n \"acc_stderr\": 0.030767394707808093,\n \ \ \"acc_norm\": 0.5094339622641509,\n \"acc_norm_stderr\": 0.030767394707808093\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4722222222222222,\n\ \ \"acc_stderr\": 0.04174752578923185,\n \"acc_norm\": 0.4722222222222222,\n\ \ \"acc_norm_stderr\": 0.04174752578923185\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|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_mathematics|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3930635838150289,\n\ \ \"acc_stderr\": 0.03724249595817729,\n \"acc_norm\": 0.3930635838150289,\n\ \ \"acc_norm_stderr\": 0.03724249595817729\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\ \ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.5,\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\": 0.37446808510638296,\n \"acc_stderr\": 0.03163910665367291,\n\ \ \"acc_norm\": 0.37446808510638296,\n \"acc_norm_stderr\": 0.03163910665367291\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813344,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813344\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.30158730158730157,\n \"acc_stderr\": 0.0236369759961018,\n \"\ acc_norm\": 0.30158730158730157,\n \"acc_norm_stderr\": 0.0236369759961018\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\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5096774193548387,\n\ \ \"acc_stderr\": 0.02843867799890955,\n \"acc_norm\": 0.5096774193548387,\n\ \ \"acc_norm_stderr\": 0.02843867799890955\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\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\ : 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.5454545454545454,\n \"acc_stderr\": 0.03888176921674101,\n\ \ \"acc_norm\": 0.5454545454545454,\n \"acc_norm_stderr\": 0.03888176921674101\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.5858585858585859,\n \"acc_stderr\": 0.03509438348879629,\n \"\ acc_norm\": 0.5858585858585859,\n \"acc_norm_stderr\": 0.03509438348879629\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.5699481865284974,\n \"acc_stderr\": 0.03572954333144808,\n\ \ \"acc_norm\": 0.5699481865284974,\n \"acc_norm_stderr\": 0.03572954333144808\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.3974358974358974,\n \"acc_stderr\": 0.024811920017903836,\n\ \ \"acc_norm\": 0.3974358974358974,\n \"acc_norm_stderr\": 0.024811920017903836\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.23333333333333334,\n \"acc_stderr\": 0.02578787422095931,\n \ \ \"acc_norm\": 0.23333333333333334,\n \"acc_norm_stderr\": 0.02578787422095931\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\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.6036697247706422,\n \"acc_stderr\": 0.02097146994790053,\n \"\ acc_norm\": 0.6036697247706422,\n \"acc_norm_stderr\": 0.02097146994790053\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.28703703703703703,\n \"acc_stderr\": 0.030851992993257013,\n \"\ acc_norm\": 0.28703703703703703,\n \"acc_norm_stderr\": 0.030851992993257013\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.5490196078431373,\n \"acc_stderr\": 0.03492406104163613,\n \"\ acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.03492406104163613\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.6075949367088608,\n \"acc_stderr\": 0.03178471874564729,\n \ \ \"acc_norm\": 0.6075949367088608,\n \"acc_norm_stderr\": 0.03178471874564729\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.47085201793721976,\n\ \ \"acc_stderr\": 0.03350073248773404,\n \"acc_norm\": 0.47085201793721976,\n\ \ \"acc_norm_stderr\": 0.03350073248773404\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5648854961832062,\n \"acc_stderr\": 0.043482080516448585,\n\ \ \"acc_norm\": 0.5648854961832062,\n \"acc_norm_stderr\": 0.043482080516448585\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.628099173553719,\n \"acc_stderr\": 0.04412015806624505,\n \"acc_norm\"\ : 0.628099173553719,\n \"acc_norm_stderr\": 0.04412015806624505\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5555555555555556,\n\ \ \"acc_stderr\": 0.04803752235190192,\n \"acc_norm\": 0.5555555555555556,\n\ \ \"acc_norm_stderr\": 0.04803752235190192\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.5276073619631901,\n \"acc_stderr\": 0.0392237829061099,\n\ \ \"acc_norm\": 0.5276073619631901,\n \"acc_norm_stderr\": 0.0392237829061099\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n\ \ \"acc_stderr\": 0.046695106638751906,\n \"acc_norm\": 0.4107142857142857,\n\ \ \"acc_norm_stderr\": 0.046695106638751906\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.5631067961165048,\n \"acc_stderr\": 0.049111471073657764,\n\ \ \"acc_norm\": 0.5631067961165048,\n \"acc_norm_stderr\": 0.049111471073657764\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6623931623931624,\n\ \ \"acc_stderr\": 0.030980296992618558,\n \"acc_norm\": 0.6623931623931624,\n\ \ \"acc_norm_stderr\": 0.030980296992618558\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5389527458492975,\n\ \ \"acc_stderr\": 0.017825621793239012,\n \"acc_norm\": 0.5389527458492975,\n\ \ \"acc_norm_stderr\": 0.017825621793239012\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.4884393063583815,\n \"acc_stderr\": 0.026911898686377913,\n\ \ \"acc_norm\": 0.4884393063583815,\n \"acc_norm_stderr\": 0.026911898686377913\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.28044692737430166,\n\ \ \"acc_stderr\": 0.015024083883322891,\n \"acc_norm\": 0.28044692737430166,\n\ \ \"acc_norm_stderr\": 0.015024083883322891\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.4803921568627451,\n \"acc_stderr\": 0.028607893699576066,\n\ \ \"acc_norm\": 0.4803921568627451,\n \"acc_norm_stderr\": 0.028607893699576066\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.49517684887459806,\n\ \ \"acc_stderr\": 0.028396770444111298,\n \"acc_norm\": 0.49517684887459806,\n\ \ \"acc_norm_stderr\": 0.028396770444111298\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.027648477877413327,\n\ \ \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.027648477877413327\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3829787234042553,\n \"acc_stderr\": 0.028999080904806178,\n \ \ \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.028999080904806178\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3272490221642764,\n\ \ \"acc_stderr\": 0.01198381980646473,\n \"acc_norm\": 0.3272490221642764,\n\ \ \"acc_norm_stderr\": 0.01198381980646473\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.33455882352941174,\n \"acc_stderr\": 0.028661996202335307,\n\ \ \"acc_norm\": 0.33455882352941174,\n \"acc_norm_stderr\": 0.028661996202335307\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.41830065359477125,\n \"acc_stderr\": 0.019955975145835542,\n \ \ \"acc_norm\": 0.41830065359477125,\n \"acc_norm_stderr\": 0.019955975145835542\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.509090909090909,\n\ \ \"acc_stderr\": 0.04788339768702861,\n \"acc_norm\": 0.509090909090909,\n\ \ \"acc_norm_stderr\": 0.04788339768702861\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.5346938775510204,\n \"acc_stderr\": 0.03193207024425314,\n\ \ \"acc_norm\": 0.5346938775510204,\n \"acc_norm_stderr\": 0.03193207024425314\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6567164179104478,\n\ \ \"acc_stderr\": 0.03357379665433431,\n \"acc_norm\": 0.6567164179104478,\n\ \ \"acc_norm_stderr\": 0.03357379665433431\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.4457831325301205,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.4457831325301205,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.5263157894736842,\n \"acc_stderr\": 0.038295098689947266,\n\ \ \"acc_norm\": 0.5263157894736842,\n \"acc_norm_stderr\": 0.038295098689947266\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2876376988984088,\n\ \ \"mc1_stderr\": 0.015846315101394816,\n \"mc2\": 0.44764087589469737,\n\ \ \"mc2_stderr\": 0.014703779857331185\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6495659037095501,\n \"acc_stderr\": 0.01340904767667018\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2759666413949962,\n \ \ \"acc_stderr\": 0.012312603010427352\n }\n}\n```" repo_url: https://huggingface.co/Josephgflowers/3BigReasonCinder 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_09T12_31_38.090504 path: - '**/details_harness|arc:challenge|25_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-09T12-31-38.090504.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|gsm8k|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hellaswag|10_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-09T12-31-38.090504.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-management|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T12-31-38.090504.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|truthfulqa:mc|0_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-09T12-31-38.090504.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_09T12_31_38.090504 path: - '**/details_harness|winogrande|5_2024-02-09T12-31-38.090504.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-09T12-31-38.090504.parquet' - config_name: results data_files: - split: 2024_02_09T12_31_38.090504 path: - results_2024-02-09T12-31-38.090504.parquet - split: latest path: - results_2024-02-09T12-31-38.090504.parquet --- # Dataset Card for Evaluation run of Josephgflowers/3BigReasonCinder  Dataset automatically created during the evaluation run of model [Josephgflowers/3BigReasonCinder](https://huggingface.co/Josephgflowers/3BigReasonCinder) 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_Josephgflowers__3BigReasonCinder", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-09T12:31:38.090504](https://huggingface.co/datasets/open-llm-leaderboard/details_Josephgflowers__3BigReasonCinder/blob/main/results_2024-02-09T12-31-38.090504.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": 0.44794266994933396, "acc_stderr": 0.03464503770381712, "acc_norm": 0.4507932379135084, "acc_norm_stderr": 0.03538213666564797, "mc1": 0.2876376988984088, "mc1_stderr": 0.015846315101394816, "mc2": 0.44764087589469737, "mc2_stderr": 0.014703779857331185 }, "harness|arc:challenge|25": { "acc": 0.39078498293515357, "acc_stderr": 0.014258563880513778, "acc_norm": 0.41723549488054607, "acc_norm_stderr": 0.014409825518403082 }, "harness|hellaswag|10": { "acc": 0.4801832304321848, "acc_stderr": 0.004985860853427632, "acc_norm": 0.6515634335789683, "acc_norm_stderr": 0.004755013243022131 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4222222222222222, "acc_stderr": 0.04266763404099582, "acc_norm": 0.4222222222222222, "acc_norm_stderr": 0.04266763404099582 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.4342105263157895, "acc_stderr": 0.040335656678483184, "acc_norm": 0.4342105263157895, "acc_norm_stderr": 0.040335656678483184 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5094339622641509, "acc_stderr": 0.030767394707808093, "acc_norm": 0.5094339622641509, "acc_norm_stderr": 0.030767394707808093 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4722222222222222, "acc_stderr": 0.04174752578923185, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.04174752578923185 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.31, "acc_stderr": 0.046482319871173156, "acc_norm": 0.31, "acc_norm_stderr": 0.046482319871173156 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.3930635838150289, "acc_stderr": 0.03724249595817729, "acc_norm": 0.3930635838150289, "acc_norm_stderr": 0.03724249595817729 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.37446808510638296, "acc_stderr": 0.03163910665367291, "acc_norm": 0.37446808510638296, "acc_norm_stderr": 0.03163910665367291 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813344, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813344 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4689655172413793, "acc_stderr": 0.04158632762097828, "acc_norm": 0.4689655172413793, "acc_norm_stderr": 0.04158632762097828 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.30158730158730157, "acc_stderr": 0.0236369759961018, "acc_norm": 0.30158730158730157, "acc_norm_stderr": 0.0236369759961018 }, "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": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5096774193548387, "acc_stderr": 0.02843867799890955, "acc_norm": 0.5096774193548387, "acc_norm_stderr": 0.02843867799890955 }, "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": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5454545454545454, "acc_stderr": 0.03888176921674101, "acc_norm": 0.5454545454545454, "acc_norm_stderr": 0.03888176921674101 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5858585858585859, "acc_stderr": 0.03509438348879629, "acc_norm": 0.5858585858585859, "acc_norm_stderr": 0.03509438348879629 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.5699481865284974, "acc_stderr": 0.03572954333144808, "acc_norm": 0.5699481865284974, "acc_norm_stderr": 0.03572954333144808 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.3974358974358974, "acc_stderr": 0.024811920017903836, "acc_norm": 0.3974358974358974, "acc_norm_stderr": 0.024811920017903836 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.23333333333333334, "acc_stderr": 0.02578787422095931, "acc_norm": 0.23333333333333334, "acc_norm_stderr": 0.02578787422095931 }, "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": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.6036697247706422, "acc_stderr": 0.02097146994790053, "acc_norm": 0.6036697247706422, "acc_norm_stderr": 0.02097146994790053 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.28703703703703703, "acc_stderr": 0.030851992993257013, "acc_norm": 0.28703703703703703, "acc_norm_stderr": 0.030851992993257013 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5490196078431373, "acc_stderr": 0.03492406104163613, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.03492406104163613 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6075949367088608, "acc_stderr": 0.03178471874564729, "acc_norm": 0.6075949367088608, "acc_norm_stderr": 0.03178471874564729 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.47085201793721976, "acc_stderr": 0.03350073248773404, "acc_norm": 0.47085201793721976, "acc_norm_stderr": 0.03350073248773404 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5648854961832062, "acc_stderr": 0.043482080516448585, "acc_norm": 0.5648854961832062, "acc_norm_stderr": 0.043482080516448585 }, "harness|hendrycksTest-international_law|5": { "acc": 0.628099173553719, "acc_stderr": 0.04412015806624505, "acc_norm": 0.628099173553719, "acc_norm_stderr": 0.04412015806624505 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.5555555555555556, "acc_stderr": 0.04803752235190192, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.04803752235190192 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.5276073619631901, "acc_stderr": 0.0392237829061099, "acc_norm": 0.5276073619631901, "acc_norm_stderr": 0.0392237829061099 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4107142857142857, "acc_stderr": 0.046695106638751906, "acc_norm": 0.4107142857142857, "acc_norm_stderr": 0.046695106638751906 }, "harness|hendrycksTest-management|5": { "acc": 0.5631067961165048, "acc_stderr": 0.049111471073657764, "acc_norm": 0.5631067961165048, "acc_norm_stderr": 0.049111471073657764 }, "harness|hendrycksTest-marketing|5": { "acc": 0.6623931623931624, "acc_stderr": 0.030980296992618558, "acc_norm": 0.6623931623931624, "acc_norm_stderr": 0.030980296992618558 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.46, "acc_stderr": 0.05009082659620333, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.5389527458492975, "acc_stderr": 0.017825621793239012, "acc_norm": 0.5389527458492975, "acc_norm_stderr": 0.017825621793239012 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.4884393063583815, "acc_stderr": 0.026911898686377913, "acc_norm": 0.4884393063583815, "acc_norm_stderr": 0.026911898686377913 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.28044692737430166, "acc_stderr": 0.015024083883322891, "acc_norm": 0.28044692737430166, "acc_norm_stderr": 0.015024083883322891 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.4803921568627451, "acc_stderr": 0.028607893699576066, "acc_norm": 0.4803921568627451, "acc_norm_stderr": 0.028607893699576066 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.49517684887459806, "acc_stderr": 0.028396770444111298, "acc_norm": 0.49517684887459806, "acc_norm_stderr": 0.028396770444111298 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.4444444444444444, "acc_stderr": 0.027648477877413327, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.027648477877413327 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3829787234042553, "acc_stderr": 0.028999080904806178, "acc_norm": 0.3829787234042553, "acc_norm_stderr": 0.028999080904806178 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3272490221642764, "acc_stderr": 0.01198381980646473, "acc_norm": 0.3272490221642764, "acc_norm_stderr": 0.01198381980646473 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.33455882352941174, "acc_stderr": 0.028661996202335307, "acc_norm": 0.33455882352941174, "acc_norm_stderr": 0.028661996202335307 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.41830065359477125, "acc_stderr": 0.019955975145835542, "acc_norm": 0.41830065359477125, "acc_norm_stderr": 0.019955975145835542 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.509090909090909, "acc_stderr": 0.04788339768702861, "acc_norm": 0.509090909090909, "acc_norm_stderr": 0.04788339768702861 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.5346938775510204, "acc_stderr": 0.03193207024425314, "acc_norm": 0.5346938775510204, "acc_norm_stderr": 0.03193207024425314 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6567164179104478, "acc_stderr": 0.03357379665433431, "acc_norm": 0.6567164179104478, "acc_norm_stderr": 0.03357379665433431 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.6, "acc_stderr": 0.049236596391733084, "acc_norm": 0.6, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-virology|5": { "acc": 0.4457831325301205, "acc_stderr": 0.03869543323472101, "acc_norm": 0.4457831325301205, "acc_norm_stderr": 0.03869543323472101 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.5263157894736842, "acc_stderr": 0.038295098689947266, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.038295098689947266 }, "harness|truthfulqa:mc|0": { "mc1": 0.2876376988984088, "mc1_stderr": 0.015846315101394816, "mc2": 0.44764087589469737, "mc2_stderr": 0.014703779857331185 }, "harness|winogrande|5": { "acc": 0.6495659037095501, "acc_stderr": 0.01340904767667018 }, "harness|gsm8k|5": { "acc": 0.2759666413949962, "acc_stderr": 0.012312603010427352 } } ``` ## 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]

autoevaluate/autoeval-eval-lener_br-lener_br-c186f5-1776861659

--- type: predictions tags: - autotrain - evaluation datasets: - lener_br eval_info: task: entity_extraction model: Luciano/bertimbau-base-lener_br metrics: [] dataset_name: lener_br dataset_config: lener_br dataset_split: train col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: Luciano/bertimbau-base-lener_br * Dataset: lener_br * Config: lener_br * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model.

HeTree/MevakerConcSen

--- license: apache-2.0 language: - he --- ## MevakerConcSen A sentence-level dataset for sentence-level conclusion extraction which provides a label of conclusion/not conclusion (1/0 respectivly) for each sentence together with indexes of the sentence and their document of origin. ### Citing If you use MevakerConcSen in your research, please cite [Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Language](https://arxiv.org/abs/2403.09719). ``` @article{shalumov2024mevaker, title={Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Language}, author={Vitaly Shalumov and Harel Haskey and Yuval Solaz}, year={2024}, eprint={2403.09719}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

karukas/arxiv-abstract-matching

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: train num_bytes: 7119340064 num_examples: 203037 - name: validation num_bytes: 216202656 num_examples: 6436 - name: test num_bytes: 216585242 num_examples: 6440 download_size: 3635681697 dataset_size: 7552127962 --- # Dataset Card for "arxiv-abstract-matching" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DazMashaly/test_fake_labels

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: image dtype: image splits: - name: test num_bytes: 365263950.94 num_examples: 5108 download_size: 354753479 dataset_size: 365263950.94 --- # Dataset Card for "test_fake_labels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dukkkk/test

--- annotations_creators: [] language_creators: [] language: - zh license: - apache-2.0 multilinguality: - monolingual pretty_name: Wenetspeech4TTS source_datasets: [] task_categories: - automatic-speech-recognition - text-to-speech - text-to-audio extra_gated_prompt: >- We do not own the copyright of the audio files. For researchers and educators who wish to use the audio files for non-commercial research and/or educational purposes, we can provide access through the Hub under certain conditions and terms. Terms of Access: The Researcher has requested permission to use the WenetSpeech4TTS database. In exchange for such permission, Researcher hereby agrees to the following terms and conditions: 1. Researcher shall use the Database only for non-commercial research and educational purposes. 2. The authors make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. 3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the authors of WenetSpeech4TTS, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database. 4.Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. 5. The authors reserve the right to terminate Researcher's access to the Database at any time. 6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. extra_gated_fields: Name: text Email: text Organization: text Address: text I hereby confirm that I have requested access via the Google Form provided above: checkbox I accept the terms of access: checkbox size_categories: - 1M<n<10M --- # Dataset Card for Dataset Name  This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## 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]

nespc/cnn_dailymail_prompts

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1354728397 num_examples: 287113 - name: test num_bytes: 53648492 num_examples: 11490 download_size: 781011544 dataset_size: 1408376889 --- # Dataset Card for "cnn_dailymail_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Verah/JParaCrawl-Filtered-English-Japanese-Parallel-Corpus

--- license: other license_name: ntt-research license_link: https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/ task_categories: - translation language: - en - ja size_categories: - 1M<n<10M --- # Introduction This is a LLM-filtered set of the first 1M rows from ntt's JParaCrawl v3 large English-Japanese parallel corpus. The original JParaCrawl corpus was put together by automated means - aligning Japanese texts with their apparent English translations that were found in-the-wild, on the internet. Whilst manually browsing the original data, I noticed that there were obvious quality issues that made me anxious about using the dataset at all. Poorly aligned translations, incomplete translations, etc. The goal of this dataset is to split the entire original dataset into its good and bad parts to: - facilitate further research - make available a high quality dataset - investigate the performance of various LLMs at evaluating the dataset. The new upload includes filtering by an additional LLM, its results are "model2_accepted": https://huggingface.co/Verah/mistral-japanese-stabalelm-merge I merged mistral instruct with stability AI's new japanese LLM, and this seems to have resulted in a model with enough knowledge of english and japanese to be competent at this task. It is likely that a finetune would further improve results. This new model accepted only 260,058 rows from the 1M seen, whilst the previous model was around twice as permissive. Prompt used with the new model: ```python from inspect import cleandoc def promptgen_mistral(japanese :str, english :str) -> str: system_prompt = cleandoc("""<s>[INST]Your role is to evaluate the accuracy of the provided Japanese to English translation. - Translations with parts missing should be rejected. - Incomplete translations should be rejected. - Inaccurate translations should be rejected. - Poor grammar should be rejected. - Any kind of mistake should be rejected. - Bad spelling should be rejected. - Low quality english should be rejected. - Low quality japanese should be rejected. - high quality translations should be accepted. - Respond with only 'ACCEPT' or 'REJECT'. """) return system_prompt + f"JAPANESE: {japanese}\nENGLISH: {english}[/INST]\n" ``` # License The license is identical to the original JParaCrawl dataset: ``` Terms of Use for Bilingual Data, Monolingual Data and Trained Models Nippon Telegraph and Telephone Corporation (Hereinafter referred to as "our company".) will provide bilingual data, monolingual data and trained models (Hereinafter referred to as "this data.") subject to your acceptance of these Terms of Use. We assume that you have agreed to these Terms of Use when you start using this data (including downloads). Article 1 (Use conditions) This data can only be used for research purposes involving information analysis (Including, but not limited to, replication and distribution. Hereinafter the same in this article.). The same applies to the derived data created based on this data. However, this data is not available for commercial use, including the sale of translators trained using this data. Article 2 (Disclaimer) Our company does not warrant the quality, performance or any other aspects of this data. We shall not be liable for any direct or indirect damages caused by the use of this data. Our company shall not be liable for any damage to the system caused by the installation of this data. Article 3 (Other). This data may be changed in whole or in part, or provision of this data may be interrupted or stopped at our company’s discretion without prior notice. ========== 対訳データ，単言語データおよび学習済みモデル利用に関する利用規約日本電信電話株式会社（以下、「当社」という。）は、本利用規約に同意されることを条件として、対訳データ、単言語データおよび学習済みモデル（以下、「本データ」という。）を提供します。なお、本データの利用（ダウンロードも含む）を開始した時点で、本利用規約にご同意頂いたものとみなします。第1条（利用条件）本データは、情報解析を伴う研究開発目的にのみご利用（複製および配布を含むが、それに限らない。以下、同じ）頂けます。本データを基に作成された派生データについても同様です。ただし、本データを使って学習したデータを内蔵した翻訳機の販売等を含む商用利用目的には、ご利用頂けません。第2条（免責）当社は、本データについて、品質、性能その他一切の保証を行うものではありません。２.直接的損害、間接的損害を問わず、本データの利用によって生ずるいかなる損害についても、一切の責任を負いません。当社は、本データのインストール作業等によって発生するシステムへの影響等、損害についても、一切の責任を負いません。第3条（その他）事前通知なしに、当社の判断によって、本データを全部または一部の変更、本データの提供の中断または停止をさせて頂くことがございます。 ```

maximalmargin/katz

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1400704.0 num_examples: 26 download_size: 1402106 dataset_size: 1400704.0 --- # Dataset Card for "katz" Images from [Alex Katz](https://www.alexkatz.com/)'s Print Archive. Hand-written image descriptions. Please use responsibly.

bigbio/bioasq_2021_mesinesp

--- language: - es bigbio_language: - Spanish license: cc-by-4.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_4p0 pretty_name: MESINESP 2021 homepage: https://zenodo.org/record/5602914#.YhSXJ5PMKWt bigbio_pubmed: False bigbio_public: True bigbio_tasks: - TEXT_CLASSIFICATION --- # Dataset Card for MESINESP 2021 ## Dataset Description - **Homepage:** https://zenodo.org/record/5602914#.YhSXJ5PMKWt - **Pubmed:** False - **Public:** True - **Tasks:** TXTCLASS The main aim of MESINESP2 is to promote the development of practically relevant semantic indexing tools for biomedical content in non-English language. We have generated a manually annotated corpus, where domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts. All the documents were labeled with DeCS descriptors, which is a structured controlled vocabulary created by BIREME to index scientific publications on BvSalud, the largest database of scientific documents in Spanish, which hosts records from the databases LILACS, MEDLINE, IBECS, among others. MESINESP track at BioASQ9 explores the efficiency of systems for assigning DeCS to different types of biomedical documents. To that purpose, we have divided the task into three subtracks depending on the document type. Then, for each one we generated an annotated corpus which was provided to participating teams: - [Subtrack 1 corpus] MESINESP-L – Scientific Literature: It contains all Spanish records from LILACS and IBECS databases at the Virtual Health Library (VHL) with non-empty abstract written in Spanish. - [Subtrack 2 corpus] MESINESP-T- Clinical Trials contains records from Registro Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the structure title/abstract needed in BioASQ, for that reason we have built artificial abstracts based on the content available in the data crawled using the REEC API. - [Subtrack 3 corpus] MESINESP-P – Patents: This corpus includes patents in Spanish extracted from Google Patents which have the IPC code “A61P” and “A61K31”. In addition, we also provide a set of complementary data such as: the DeCS terminology file, a silver standard with the participants' predictions to the task background set and the entities of medications, diseases, symptoms and medical procedures extracted from the BSC NERs documents. ## Citation Information ``` @conference {396, title = {Overview of BioASQ 2021-MESINESP track. Evaluation of advance hierarchical classification techniques for scientific literature, patents and clinical trials.}, booktitle = {Proceedings of the 9th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering}, year = {2021}, url = {http://ceur-ws.org/Vol-2936/paper-11.pdf}, author = {Gasco, Luis and Nentidis, Anastasios and Krithara, Anastasia and Estrada-Zavala, Darryl and Toshiyuki Murasaki, Renato and Primo-Pe{\~n}a, Elena and Bojo-Canales, Cristina and Paliouras, Georgios and Krallinger, Martin} } ```

Iania/QA_setup

--- license: apache-2.0 ---

roa7n/patched_test_p_10_m1_predictions_v2

--- dataset_info: features: - name: id dtype: string - name: sequence_str dtype: string - name: label dtype: int64 - name: m1_preds dtype: float32 splits: - name: train num_bytes: 1566287530 num_examples: 2843834 download_size: 138365947 dataset_size: 1566287530 --- # Dataset Card for "patched_test_p_10_m1_predictions_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_dillfrescott__amadeus-v0.1

--- pretty_name: Evaluation run of dillfrescott/amadeus-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [dillfrescott/amadeus-v0.1](https://huggingface.co/dillfrescott/amadeus-v0.1)\ \ 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_dillfrescott__amadeus-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-06T01:28:19.231223](https://huggingface.co/datasets/open-llm-leaderboard/details_dillfrescott__amadeus-v0.1/blob/main/results_2024-01-06T01-28-19.231223.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\": 0.6503116864878898,\n\ \ \"acc_stderr\": 0.03211845742165151,\n \"acc_norm\": 0.6514191578913137,\n\ \ \"acc_norm_stderr\": 0.032765902396781794,\n \"mc1\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6382334765973684,\n\ \ \"mc2_stderr\": 0.01550846970253108\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6578498293515358,\n \"acc_stderr\": 0.013864152159177278,\n\ \ \"acc_norm\": 0.689419795221843,\n \"acc_norm_stderr\": 0.013522292098053069\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6957777335192192,\n\ \ \"acc_stderr\": 0.004591369853276529,\n \"acc_norm\": 0.8698466440948018,\n\ \ \"acc_norm_stderr\": 0.0033578442491239546\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\ \ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\ \ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249386,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249386\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness|hendrycksTest-college_computer_science|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-college_mathematics|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952344,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952344\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\ \ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\ \ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.03208115750788684,\n\ \ \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.03208115750788684\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878151,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878151\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4021164021164021,\n \"acc_stderr\": 0.025253032554997695,\n \"\ acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.025253032554997695\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\ \ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\ \ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\ : 0.8,\n \"acc_stderr\": 0.022755204959542943,\n \"acc_norm\": 0.8,\n\ \ \"acc_norm_stderr\": 0.022755204959542943\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\ \ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\ acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768766,\n\ \ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768766\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \ \ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.02874204090394848,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.02874204090394848\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7100840336134454,\n \"acc_stderr\": 0.029472485833136098,\n\ \ \"acc_norm\": 0.7100840336134454,\n \"acc_norm_stderr\": 0.029472485833136098\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\ acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\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\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n\ \ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n\ \ \"acc_stderr\": 0.03076935200822914,\n \"acc_norm\": 0.6995515695067265,\n\ \ \"acc_norm_stderr\": 0.03076935200822914\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\ \ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\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\": 0.7962962962962963,\n\ \ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\ \ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\ \ \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n\ \ \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077802,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077802\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8314176245210728,\n\ \ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\ \ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545543,\n\ \ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545543\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43687150837988825,\n\ \ \"acc_stderr\": 0.01658868086453063,\n \"acc_norm\": 0.43687150837988825,\n\ \ \"acc_norm_stderr\": 0.01658868086453063\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137904,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137904\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\ \ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\ \ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.024383665531035454,\n\ \ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.024383665531035454\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829727,\n \ \ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829727\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4706649282920469,\n\ \ \"acc_stderr\": 0.012748238397365549,\n \"acc_norm\": 0.4706649282920469,\n\ \ \"acc_norm_stderr\": 0.012748238397365549\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.027778298701545443,\n\ \ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.027778298701545443\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6503267973856209,\n \"acc_stderr\": 0.01929196189506638,\n \ \ \"acc_norm\": 0.6503267973856209,\n \"acc_norm_stderr\": 0.01929196189506638\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\ \ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\ \ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\ \ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.025196929874827075,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.025196929874827075\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\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\": 0.46266829865361075,\n\ \ \"mc1_stderr\": 0.017454645150970588,\n \"mc2\": 0.6382334765973684,\n\ \ \"mc2_stderr\": 0.01550846970253108\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7995264404104183,\n \"acc_stderr\": 0.01125195828120508\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6413949962092494,\n \ \ \"acc_stderr\": 0.013210317364134031\n }\n}\n```" repo_url: https://huggingface.co/dillfrescott/amadeus-v0.1 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_06T01_28_19.231223 path: - '**/details_harness|arc:challenge|25_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-06T01-28-19.231223.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|gsm8k|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hellaswag|10_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-06T01-28-19.231223.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-management|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T01-28-19.231223.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|truthfulqa:mc|0_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-06T01-28-19.231223.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_06T01_28_19.231223 path: - '**/details_harness|winogrande|5_2024-01-06T01-28-19.231223.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-06T01-28-19.231223.parquet' - config_name: results data_files: - split: 2024_01_06T01_28_19.231223 path: - results_2024-01-06T01-28-19.231223.parquet - split: latest path: - results_2024-01-06T01-28-19.231223.parquet --- # Dataset Card for Evaluation run of dillfrescott/amadeus-v0.1  Dataset automatically created during the evaluation run of model [dillfrescott/amadeus-v0.1](https://huggingface.co/dillfrescott/amadeus-v0.1) 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_dillfrescott__amadeus-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-06T01:28:19.231223](https://huggingface.co/datasets/open-llm-leaderboard/details_dillfrescott__amadeus-v0.1/blob/main/results_2024-01-06T01-28-19.231223.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": 0.6503116864878898, "acc_stderr": 0.03211845742165151, "acc_norm": 0.6514191578913137, "acc_norm_stderr": 0.032765902396781794, "mc1": 0.46266829865361075, "mc1_stderr": 0.017454645150970588, "mc2": 0.6382334765973684, "mc2_stderr": 0.01550846970253108 }, "harness|arc:challenge|25": { "acc": 0.6578498293515358, "acc_stderr": 0.013864152159177278, "acc_norm": 0.689419795221843, "acc_norm_stderr": 0.013522292098053069 }, "harness|hellaswag|10": { "acc": 0.6957777335192192, "acc_stderr": 0.004591369853276529, "acc_norm": 0.8698466440948018, "acc_norm_stderr": 0.0033578442491239546 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7018867924528301, "acc_stderr": 0.02815283794249386, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.02815283794249386 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.047609522856952344, "acc_norm": 0.34, "acc_norm_stderr": 0.047609522856952344 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5957446808510638, "acc_stderr": 0.03208115750788684, "acc_norm": 0.5957446808510638, "acc_norm_stderr": 0.03208115750788684 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5517241379310345, "acc_stderr": 0.04144311810878151, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.04144311810878151 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4021164021164021, "acc_stderr": 0.025253032554997695, "acc_norm": 0.4021164021164021, "acc_norm_stderr": 0.025253032554997695 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8, "acc_stderr": 0.022755204959542943, "acc_norm": 0.8, "acc_norm_stderr": 0.022755204959542943 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5123152709359606, "acc_stderr": 0.035169204442208966, "acc_norm": 0.5123152709359606, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.72, "acc_stderr": 0.04512608598542127, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7575757575757576, "acc_stderr": 0.03346409881055953, "acc_norm": 0.7575757575757576, "acc_norm_stderr": 0.03346409881055953 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7828282828282829, "acc_stderr": 0.02937661648494563, "acc_norm": 0.7828282828282829, "acc_norm_stderr": 0.02937661648494563 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8911917098445595, "acc_stderr": 0.022473253332768766, "acc_norm": 0.8911917098445595, "acc_norm_stderr": 0.022473253332768766 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6743589743589744, "acc_stderr": 0.02375966576741229, "acc_norm": 0.6743589743589744, "acc_norm_stderr": 0.02375966576741229 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.02874204090394848, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.02874204090394848 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7100840336134454, "acc_stderr": 0.029472485833136098, "acc_norm": 0.7100840336134454, "acc_norm_stderr": 0.029472485833136098 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.03861557546255169, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.03861557546255169 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5277777777777778, "acc_stderr": 0.0340470532865388, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 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0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8314176245210728, "acc_stderr": 0.013387895731543604, "acc_norm": 0.8314176245210728, "acc_norm_stderr": 0.013387895731543604 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7456647398843931, "acc_stderr": 0.023445826276545543, "acc_norm": 0.7456647398843931, "acc_norm_stderr": 0.023445826276545543 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.43687150837988825, "acc_stderr": 0.01658868086453063, "acc_norm": 0.43687150837988825, "acc_norm_stderr": 0.01658868086453063 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137904, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137904 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6977491961414791, "acc_stderr": 0.02608270069539966, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.02608270069539966 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7407407407407407, "acc_stderr": 0.024383665531035454, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.024383665531035454 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.475177304964539, "acc_stderr": 0.029790719243829727, "acc_norm": 0.475177304964539, "acc_norm_stderr": 0.029790719243829727 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4706649282920469, "acc_stderr": 0.012748238397365549, "acc_norm": 0.4706649282920469, "acc_norm_stderr": 0.012748238397365549 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7022058823529411, "acc_stderr": 0.027778298701545443, "acc_norm": 0.7022058823529411, "acc_norm_stderr": 0.027778298701545443 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6503267973856209, "acc_stderr": 0.01929196189506638, "acc_norm": 0.6503267973856209, "acc_norm_stderr": 0.01929196189506638 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7224489795918367, "acc_stderr": 0.028666857790274648, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274648 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8507462686567164, "acc_stderr": 0.025196929874827075, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.025196929874827075 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness|hendrycksTest-virology|5": { "acc": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "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": 0.46266829865361075, "mc1_stderr": 0.017454645150970588, "mc2": 0.6382334765973684, "mc2_stderr": 0.01550846970253108 }, "harness|winogrande|5": { "acc": 0.7995264404104183, "acc_stderr": 0.01125195828120508 }, "harness|gsm8k|5": { "acc": 0.6413949962092494, "acc_stderr": 0.013210317364134031 } } ``` ## 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]

ctu-aic/qacg-sum

--- dataset_info: - config_name: balanced features: - name: claim dtype: string - name: label dtype: string - name: evidence sequence: string - name: lang dtype: string - name: orig_idx dtype: int64 splits: - name: train num_bytes: 130783710 num_examples: 1180836 - name: validation num_bytes: 13391571 num_examples: 120348 - name: test num_bytes: 12599211 num_examples: 113760 download_size: 114959179 dataset_size: 156774492 - config_name: balanced_shuf features: - name: claim dtype: string - name: label dtype: string - name: evidence sequence: string - name: lang dtype: string - name: orig_idx dtype: int64 splits: - name: train num_bytes: 81658504 num_examples: 741542 - name: validation num_bytes: 8349339 num_examples: 75573 - name: test num_bytes: 7871047 num_examples: 71607 download_size: 71188503 dataset_size: 97878890 configs: - config_name: balanced data_files: - split: train path: balanced/train-* - split: validation path: balanced/validation-* - split: test path: balanced/test-* - config_name: balanced_shuf data_files: - split: train path: balanced_shuf/train-* - split: validation path: balanced_shuf/validation-* - split: test path: balanced_shuf/test-* ---

Nexdata/Mandarin_Speech_by_Mobile_Phone

--- YAML tags: - copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging --- # Dataset Card for Nexdata/Mandarin_Speech_by_Mobile_Phone ## 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) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://www.nexdata.ai/datasets/35?source=Huggingface - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary It collects 6,278 speakers' dat from 33 provinces of China. 2,980 males and 3,298 females. The recording contents are commonly used colloquial sentences. It is recorded in both quiet and noisy environment. Annotated texts are transcribed and proofread by professional annotators. The accuracy is not less than 98%. For more details, please refer to the link: https://www.nexdata.ai/datasets/35?source=Huggingface ### Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR). ### Languages Mandarin ## 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 Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing ### Citation Information [More Information Needed] ### Contributions

CyberHarem/ayase_arisa_lovelive

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of ayase_arisa/絢瀬亜里沙 (Love Live!) This is the dataset of ayase_arisa/絢瀬亜里沙 (Love Live!), containing 163 images and their tags. The core tags of this character are `blonde_hair, blue_eyes, long_hair, hair_ornament, hairclip`, 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 | 163 | 120.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ayase_arisa_lovelive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 163 | 95.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ayase_arisa_lovelive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 334 | 174.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ayase_arisa_lovelive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 163 | 115.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ayase_arisa_lovelive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 334 | 204.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ayase_arisa_lovelive/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/ayase_arisa_lovelive', 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, blush, looking_at_viewer, open_mouth, serafuku, skirt, solo, simple_background, white_background, smile | | 1 | 9 | ![](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) | 2girls, blush, open_mouth, skirt, serafuku, :d, solo_focus | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | open_mouth | serafuku | skirt | solo | simple_background | white_background | smile | 2girls | :d | solo_focus | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------------|:-----------|:--------|:-------|:--------------------|:-------------------|:--------|:---------|:-----|:-------------| | 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 | | | | | 1 | 9 | ![](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 |

nidnoiewoifehw/yocleash

--- license: gpl-3.0 ---

nilekhet/Spectrum-Dataset

--- license: wtfpl --- Dataset Card: Spectrum-Dataset 🌈 🌐 Source: [nilekhet/Spectrum · Hugging Face](https://huggingface.co/nilekhet/Spectrum) 📁 Supplementary Dataset: Spectrum-Dataset 🌟 🔗 Associated Model: Spectrum Model 🧬 ## 🔍 bengin_generator.py 👨‍💻 * 📂 Recursively walks through folders * 🚫 Skips unallowed items * 🔄 Copies .exe files to destination folder ## 🔍 malfamily.py 👩‍💻 * 🌐 Scrapes malware family links * 📥 Downloads and organizes malware samples * 🗂️ Saves data as a .csv file ## 🔍 Rust code for image generation 🎨 * 🌐 GitHub: https://github.com/nileshkhetrapal/spectrum * 🖼️ Generates images from the code ## 🎯 Intended Use of the Model 🌟 * 💻🔧 Classify malware based on input images * 🛡️💻 Improve computer and network security * 🌐 Help with malware detection and prevention # 📊 Number of Classes: 1️⃣1️⃣9️⃣ * 🦠 Includes benign class

joey234/mmlu-conceptual_physics-neg-prepend-verbal

--- configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: ori_prompt dtype: string - name: fewshot_context_neg dtype: string - name: fewshot_context_ori dtype: string - name: neg_prompt dtype: string splits: - name: dev num_bytes: 5977 num_examples: 5 - name: test num_bytes: 1347765 num_examples: 235 download_size: 155122 dataset_size: 1353742 --- # Dataset Card for "mmlu-conceptual_physics-neg-prepend-verbal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rajendrabaskota/hc3-wiki-cleaned-text-for-domain-classification-roberta-tokenized-max-len-512

--- dataset_info: features: - name: prompt dtype: string - name: text dtype: string - name: source dtype: int64 - name: human/ai dtype: int64 - name: perplexity dtype: float64 - name: cleaned_text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 845606936 num_examples: 330345 - name: test num_bytes: 44570090 num_examples: 17387 download_size: 499405861 dataset_size: 890177026 --- # Dataset Card for "hc3-wiki-cleaned-text-for-domain-classification-roberta-tokenized-max-len-512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Isamu136/chess-gpt-data

--- license: apache-2.0 ---

dsupa/hack5-IQ-HP

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' '2': '2' '3': '3' '4': '4' '5': '5' '6': '6' splits: - name: train num_bytes: 2171810.0 num_examples: 647 download_size: 1814705 dataset_size: 2171810.0 --- # Dataset Card for "hack5-IQ-HP" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

BitTranslate/chatgpt-prompts-Ukrainian

--- license: cc0-1.0 language: - uk tags: - ChatGPT ---

Joe02/quinn_refs

--- license: other ---

liuyanchen1015/VALUE_stsb_dey_it

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 13139 num_examples: 69 - name: test num_bytes: 6243 num_examples: 48 - name: train num_bytes: 7725 num_examples: 40 download_size: 27352 dataset_size: 27107 --- # Dataset Card for "VALUE_stsb_dey_it" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

adamjweintraut/bart-finetuned-eli5_lfqa_best_slice-256_2023-12-10_run

--- dataset_info: features: - name: index dtype: int64 - name: q_id dtype: string - name: question dtype: string - name: context dtype: string - name: target dtype: string - name: predicted dtype: string - name: label dtype: string - name: rougeL_P dtype: float64 - name: rougeL_R dtype: float64 - name: rougeL_F dtype: float64 - name: Cosine_Sim dtype: float64 - name: nli-roberta_label dtype: string - name: nli-roberta_plot_vals dtype: int64 - name: nli-roberta-max-score dtype: float64 - name: sent_sim dtype: float32 - name: context_answer_sim dtype: float32 - name: rougeL_min_precision dtype: float64 - name: rougeL_min_recall dtype: float64 - name: rougeL_min_fmeasure dtype: float64 - name: rougeL_median_precision dtype: float64 - name: rougeL_median_recall dtype: float64 - name: rougeL_median_fmeasure dtype: float64 - name: rougeL_max_precision dtype: float64 - name: rougeL_max_recall dtype: float64 - name: rougeL_max_fmeasure dtype: float64 - name: context_predicted_sim dtype: float32 - name: context_label_sim dtype: float32 - name: predicted_label_sim dtype: float32 - name: nli_context_predicted_label dtype: string - name: nli_context_predicted_plots dtype: int64 splits: - name: train num_bytes: 8354389 num_examples: 1250 download_size: 5113622 dataset_size: 8354389 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_jan-hq__trinity-v1

--- pretty_name: Evaluation run of jan-hq/trinity-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jan-hq/trinity-v1](https://huggingface.co/jan-hq/trinity-v1) 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_jan-hq__trinity-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-16T19:24:08.553660](https://huggingface.co/datasets/open-llm-leaderboard/details_jan-hq__trinity-v1/blob/main/results_2023-12-16T19-24-08.553660.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\": 0.6575877329335247,\n\ \ \"acc_stderr\": 0.031985421208388404,\n \"acc_norm\": 0.6571647268300141,\n\ \ \"acc_norm_stderr\": 0.032648337921958155,\n \"mc1\": 0.5507955936352509,\n\ \ \"mc1_stderr\": 0.01741294198611529,\n \"mc2\": 0.6931209356367747,\n\ \ \"mc2_stderr\": 0.015031530031665238\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6988054607508533,\n \"acc_stderr\": 0.013406741767847632,\n\ \ \"acc_norm\": 0.7226962457337884,\n \"acc_norm_stderr\": 0.013082095839059376\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.711113324039036,\n\ \ \"acc_stderr\": 0.004523188431142894,\n \"acc_norm\": 0.8835889265086636,\n\ \ \"acc_norm_stderr\": 0.0032006176493464752\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n\ \ \"acc_stderr\": 0.040943762699967926,\n \"acc_norm\": 0.6592592592592592,\n\ \ \"acc_norm_stderr\": 0.040943762699967926\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\ \ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\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\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\"\ : 0.56,\n \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6994219653179191,\n\ \ \"acc_stderr\": 0.03496101481191179,\n \"acc_norm\": 0.6994219653179191,\n\ \ \"acc_norm_stderr\": 0.03496101481191179\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.04959859966384181,\n\ \ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.04959859966384181\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\ \ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\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\": 0.33,\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\": 0.7806451612903226,\n\ \ \"acc_stderr\": 0.023540799358723295,\n \"acc_norm\": 0.7806451612903226,\n\ \ \"acc_norm_stderr\": 0.023540799358723295\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.03510766597959215,\n\ \ \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.03510766597959215\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\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\": 0.029126522834586815,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131154,\n \ \ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131154\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \ \ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\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\": 0.01517314184512625,\n \"\ acc_norm\": 0.8532110091743119,\n \"acc_norm_stderr\": 0.01517314184512625\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\ acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\ acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290916,\n \ \ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290916\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.031024411740572213,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.031024411740572213\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.033368203384760736,\n\ \ \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.033368203384760736\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\ acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742179,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742179\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\ \ \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n\ \ \"acc_norm_stderr\": 0.046840993210771065\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\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\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8352490421455939,\n\ \ \"acc_stderr\": 0.013265346261323797,\n \"acc_norm\": 0.8352490421455939,\n\ \ \"acc_norm_stderr\": 0.013265346261323797\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069356,\n\ \ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069356\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4759776536312849,\n\ \ \"acc_stderr\": 0.016703190189300186,\n \"acc_norm\": 0.4759776536312849,\n\ \ \"acc_norm_stderr\": 0.016703190189300186\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\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\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \ \ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47392438070404175,\n\ \ \"acc_stderr\": 0.012752858346533131,\n \"acc_norm\": 0.47392438070404175,\n\ \ \"acc_norm_stderr\": 0.012752858346533131\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\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \ \ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\ \ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.025870646766169136,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.025870646766169136\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\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\ \ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5507955936352509,\n\ \ \"mc1_stderr\": 0.01741294198611529,\n \"mc2\": 0.6931209356367747,\n\ \ \"mc2_stderr\": 0.015031530031665238\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8200473559589582,\n \"acc_stderr\": 0.01079646868806868\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7164518574677786,\n \ \ \"acc_stderr\": 0.012415070917508124\n }\n}\n```" repo_url: https://huggingface.co/jan-hq/trinity-v1 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_12_16T19_24_08.553660 path: - '**/details_harness|arc:challenge|25_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-16T19-24-08.553660.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|gsm8k|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hellaswag|10_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-16T19-24-08.553660.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T19-24-08.553660.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-16T19-24-08.553660.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_16T19_24_08.553660 path: - '**/details_harness|winogrande|5_2023-12-16T19-24-08.553660.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-16T19-24-08.553660.parquet' - config_name: results data_files: - split: 2023_12_16T19_24_08.553660 path: - results_2023-12-16T19-24-08.553660.parquet - split: latest path: - results_2023-12-16T19-24-08.553660.parquet --- # Dataset Card for Evaluation run of jan-hq/trinity-v1  Dataset automatically created during the evaluation run of model [jan-hq/trinity-v1](https://huggingface.co/jan-hq/trinity-v1) 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_jan-hq__trinity-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-16T19:24:08.553660](https://huggingface.co/datasets/open-llm-leaderboard/details_jan-hq__trinity-v1/blob/main/results_2023-12-16T19-24-08.553660.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": 0.6575877329335247, "acc_stderr": 0.031985421208388404, "acc_norm": 0.6571647268300141, "acc_norm_stderr": 0.032648337921958155, "mc1": 0.5507955936352509, "mc1_stderr": 0.01741294198611529, "mc2": 0.6931209356367747, "mc2_stderr": 0.015031530031665238 }, "harness|arc:challenge|25": { "acc": 0.6988054607508533, "acc_stderr": 0.013406741767847632, "acc_norm": 0.7226962457337884, "acc_norm_stderr": 0.013082095839059376 }, "harness|hellaswag|10": { "acc": 0.711113324039036, "acc_stderr": 0.004523188431142894, "acc_norm": 0.8835889265086636, "acc_norm_stderr": 0.0032006176493464752 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6592592592592592, "acc_stderr": 0.040943762699967926, "acc_norm": 0.6592592592592592, "acc_norm_stderr": 0.040943762699967926 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.62, "acc_stderr": 0.048783173121456316, "acc_norm": 0.62, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.720754716981132, "acc_stderr": 0.027611163402399715, "acc_norm": 0.720754716981132, "acc_norm_stderr": 0.027611163402399715 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6994219653179191, "acc_stderr": 0.03496101481191179, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.03496101481191179 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.46078431372549017, "acc_stderr": 0.04959859966384181, "acc_norm": 0.46078431372549017, "acc_norm_stderr": 0.04959859966384181 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.042295258468165065, "acc_norm": 0.77, "acc_norm_stderr": 0.042295258468165065 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "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": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723295, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723295 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5320197044334976, "acc_stderr": 0.03510766597959215, "acc_norm": 0.5320197044334976, "acc_norm_stderr": 0.03510766597959215 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "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": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.02150024957603348, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.02150024957603348 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402538, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402538 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131154, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131154 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6722689075630253, "acc_stderr": 0.03048991141767323, "acc_norm": 0.6722689075630253, "acc_norm_stderr": 0.03048991141767323 }, "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": 0.01517314184512625, "acc_norm": 0.8532110091743119, "acc_norm_stderr": 0.01517314184512625 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5416666666666666, "acc_stderr": 0.03398110890294636, "acc_norm": 0.5416666666666666, "acc_norm_stderr": 0.03398110890294636 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8333333333333334, "acc_stderr": 0.026156867523931045, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.026156867523931045 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8059071729957806, "acc_stderr": 0.025744902532290916, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.025744902532290916 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6905829596412556, "acc_stderr": 0.031024411740572213, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.031024411740572213 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8244274809160306, "acc_stderr": 0.033368203384760736, "acc_norm": 0.8244274809160306, "acc_norm_stderr": 0.033368203384760736 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8016528925619835, "acc_stderr": 0.03640118271990947, "acc_norm": 0.8016528925619835, "acc_norm_stderr": 0.03640118271990947 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7777777777777778, "acc_stderr": 0.040191074725573483, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.040191074725573483 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742179, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742179 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.41964285714285715, "acc_stderr": 0.046840993210771065, "acc_norm": 0.41964285714285715, "acc_norm_stderr": 0.046840993210771065 }, "harness|hendrycksTest-management|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "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": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8352490421455939, "acc_stderr": 0.013265346261323797, "acc_norm": 0.8352490421455939, "acc_norm_stderr": 0.013265346261323797 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069356, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069356 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4759776536312849, "acc_stderr": 0.016703190189300186, "acc_norm": 0.4759776536312849, "acc_norm_stderr": 0.016703190189300186 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137897, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137897 }, "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": 0.7469135802469136, "acc_stderr": 0.024191808600712995, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600712995 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.48936170212765956, "acc_stderr": 0.029820747191422473, "acc_norm": 0.48936170212765956, "acc_norm_stderr": 0.029820747191422473 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.47392438070404175, "acc_stderr": 0.012752858346533131, "acc_norm": 0.47392438070404175, "acc_norm_stderr": 0.012752858346533131 }, "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": 0.6715686274509803, "acc_stderr": 0.018999707383162673, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.018999707383162673 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7224489795918367, "acc_stderr": 0.028666857790274648, "acc_norm": 0.7224489795918367, "acc_norm_stderr": 0.028666857790274648 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.025870646766169136, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.025870646766169136 }, "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": 0.8245614035087719, "acc_stderr": 0.029170885500727665, "acc_norm": 0.8245614035087719, "acc_norm_stderr": 0.029170885500727665 }, "harness|truthfulqa:mc|0": { "mc1": 0.5507955936352509, "mc1_stderr": 0.01741294198611529, "mc2": 0.6931209356367747, "mc2_stderr": 0.015031530031665238 }, "harness|winogrande|5": { "acc": 0.8200473559589582, "acc_stderr": 0.01079646868806868 }, "harness|gsm8k|5": { "acc": 0.7164518574677786, "acc_stderr": 0.012415070917508124 } } ``` ## 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]

MexIvanov/CodeExercise-Python-27k-ru

--- license: cc-by-nc-sa-4.0 language: - ru tags: - Python - code --- A machine translated version of the codefuse-ai/CodeExercise-Python-27k dataset. Consists of synthetically generated code with code-related data and natural language instructions. Released under the same license as the original dataset, provided as is with research intent, use/read at your own risk.

distilabel-internal-testing/airoboros-3.2-writing-oai-style-tiny

--- dataset_info: features: - name: id dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 56595.216657287565 num_examples: 10 download_size: 37556 dataset_size: 56595.216657287565 configs: - config_name: default data_files: - split: train path: data/train-* ---

FlyingFishzzz/source_test

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image dtype: image - name: image_seg dtype: image - name: landmarks dtype: string - name: spiga sequence: sequence: float64 - name: spiga_seg dtype: image - name: image_caption dtype: string splits: - name: train num_bytes: 488488715.0 num_examples: 1588 download_size: 487390223 dataset_size: 488488715.0 --- # Dataset Card for "source_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

thrisha15/Content_Generation_dataset

--- language: - en ---

HydraLM/partitioned_v2_standardized_014

--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: dataset_id dtype: string - name: unique_conversation_id dtype: string splits: - name: train num_bytes: 64103762.64755713 num_examples: 125409 download_size: 18947842 dataset_size: 64103762.64755713 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "partitioned_v2_standardized_014" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

AdapterOcean/Open_Platypus_standardized_cluster_1_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 7549475 num_examples: 7230 download_size: 0 dataset_size: 7549475 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Open_Platypus_standardized_cluster_1_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

lachieandmitch/hugging

--- license: apache-2.0 ---

Someman/hindi-summarization

--- license: mit task_categories: - summarization language: hi original_source: >- https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus dataset_info: features: - name: headline dtype: string - name: summary dtype: string - name: article dtype: string splits: - name: train num_bytes: 410722079.5542422 num_examples: 55226 - name: test num_bytes: 102684238.44575782 num_examples: 13807 - name: valid num_bytes: 128376473 num_examples: 17265 download_size: 150571314 dataset_size: 641782791 pretty_name: hindi summarization size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus?select=test.csv ### Dataset Summary Hindi Text Short and Large Summarization Corpus is a collection of ~180k articles with their headlines and summary collected from Hindi News Websites. This is a first of its kind Dataset in Hindi which can be used to benchmark models for Text summarization in Hindi. This does not contain articles contained in Hindi Text Short Summarization Corpus which is being released parallely with this Dataset. The dataset retains original punctuation, numbers etc in the articles. ### Languages The language is Hindi. ### Licensing Information MIT ### Citation Information https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus?select=test.csv ### Contributions

4eJIoBek/Old-audios-11k

--- license: unknown --- unsorted audios in mod, wav or other old audio formats

PurCL/marinda-type-inference-debuginfo-only-O1-shuffle

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: metadata struct: - name: binary_name dtype: string - name: function_addr dtype: int64 - name: function_name dtype: string - name: project_name dtype: string - name: code_w_type dtype: string - name: code dtype: string - name: data_dep dtype: string splits: - name: train num_bytes: 201535867.70075417 num_examples: 37113 - name: test num_bytes: 22394684.299245823 num_examples: 4124 download_size: 52386440 dataset_size: 223930552.0 --- # Dataset Card for "marinda-type-inference-debuginfo-only-O1-shuffle" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_ajibawa-2023__Code-Mistral-7B

--- pretty_name: Evaluation run of ajibawa-2023/Code-Mistral-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ajibawa-2023/Code-Mistral-7B](https://huggingface.co/ajibawa-2023/Code-Mistral-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 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the 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_ajibawa-2023__Code-Mistral-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-25T07:53:45.933606](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Code-Mistral-7B/blob/main/results_2024-03-25T07-53-45.933606.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\": 0.6527492220016492,\n\ \ \"acc_stderr\": 0.031870603059274874,\n \"acc_norm\": 0.6533709217123561,\n\ \ \"acc_norm_stderr\": 0.0325259522142822,\n \"mc1\": 0.3684210526315789,\n\ \ \"mc1_stderr\": 0.016886551261046046,\n \"mc2\": 0.5463721037747236,\n\ \ \"mc2_stderr\": 0.015046435516843176\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6083617747440273,\n \"acc_stderr\": 0.014264122124938213,\n\ \ \"acc_norm\": 0.6459044368600683,\n \"acc_norm_stderr\": 0.01397545412275656\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6560446126269668,\n\ \ \"acc_stderr\": 0.0047405557821421735,\n \"acc_norm\": 0.8529177454690301,\n\ \ \"acc_norm_stderr\": 0.0035346403488166773\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6666666666666666,\n\ \ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\ \ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7236842105263158,\n \"acc_stderr\": 0.03639057569952929,\n\ \ \"acc_norm\": 0.7236842105263158,\n \"acc_norm_stderr\": 0.03639057569952929\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\": 0.7245283018867924,\n \"acc_stderr\": 0.02749566368372406,\n\ \ \"acc_norm\": 0.7245283018867924,\n \"acc_norm_stderr\": 0.02749566368372406\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\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\ : 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \ \ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6878612716763006,\n\ \ \"acc_stderr\": 0.03533133389323657,\n \"acc_norm\": 0.6878612716763006,\n\ \ \"acc_norm_stderr\": 0.03533133389323657\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n\ \ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\ \ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\ \ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5158730158730159,\n\ \ \"acc_stderr\": 0.044698818540726076,\n \"acc_norm\": 0.5158730158730159,\n\ \ \"acc_norm_stderr\": 0.044698818540726076\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\ : 0.7806451612903226,\n \"acc_stderr\": 0.023540799358723295,\n \"\ acc_norm\": 0.7806451612903226,\n \"acc_norm_stderr\": 0.023540799358723295\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\ acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\"\ : 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218967,\n \"\ acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218967\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n\ \ \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \ \ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \ \ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.0302839955258844,\n \ \ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.0302839955258844\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\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\"\ : 0.5185185185185185,\n \"acc_stderr\": 0.034076320938540516,\n \"\ acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.034076320938540516\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931796,\n \"\ acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931796\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8185654008438819,\n \"acc_stderr\": 0.02508596114457966,\n \ \ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.02508596114457966\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7219730941704036,\n\ \ \"acc_stderr\": 0.030069584874494043,\n \"acc_norm\": 0.7219730941704036,\n\ \ \"acc_norm_stderr\": 0.030069584874494043\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\ \ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\ \ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.034624199316156234,\n\ \ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.034624199316156234\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9017094017094017,\n\ \ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\ \ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8352490421455939,\n\ \ \"acc_stderr\": 0.013265346261323786,\n \"acc_norm\": 0.8352490421455939,\n\ \ \"acc_norm_stderr\": 0.013265346261323786\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044287,\n\ \ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044287\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3195530726256983,\n\ \ \"acc_stderr\": 0.01559552029414741,\n \"acc_norm\": 0.3195530726256983,\n\ \ \"acc_norm_stderr\": 0.01559552029414741\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818733,\n\ \ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818733\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\ \ \"acc_stderr\": 0.02567025924218893,\n \"acc_norm\": 0.7138263665594855,\n\ \ \"acc_norm_stderr\": 0.02567025924218893\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\"\ : 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\ : {\n \"acc\": 0.47783572359843546,\n \"acc_stderr\": 0.012757683047716175,\n\ \ \"acc_norm\": 0.47783572359843546,\n \"acc_norm_stderr\": 0.012757683047716175\n\ \ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\ : 0.6948529411764706,\n \"acc_stderr\": 0.027971541370170598,\n \"\ acc_norm\": 0.6948529411764706,\n \"acc_norm_stderr\": 0.027971541370170598\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.684640522875817,\n \"acc_stderr\": 0.01879808628488689,\n \ \ \"acc_norm\": 0.684640522875817,\n \"acc_norm_stderr\": 0.01879808628488689\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\": 0.726530612244898,\n \"acc_stderr\": 0.02853556033712844,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.02853556033712844\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.02519692987482707,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.02519692987482707\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\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\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160893,\n\ \ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160893\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3684210526315789,\n\ \ \"mc1_stderr\": 0.016886551261046046,\n \"mc2\": 0.5463721037747236,\n\ \ \"mc2_stderr\": 0.015046435516843176\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8224151539068666,\n \"acc_stderr\": 0.010740676861359226\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6808188021228203,\n \ \ \"acc_stderr\": 0.012840345676251648\n }\n}\n```" repo_url: https://huggingface.co/ajibawa-2023/Code-Mistral-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_25T05_45_49.471582 path: - '**/details_harness|arc:challenge|25_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|arc:challenge|25_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-25T07-53-45.933606.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|gsm8k|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|gsm8k|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hellaswag|10_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hellaswag|10_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-25T05-45-49.471582.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-25T07-53-45.933606.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-management|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-management|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-25T07-53-45.933606.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|truthfulqa:mc|0_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|truthfulqa:mc|0_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-25T07-53-45.933606.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_25T05_45_49.471582 path: - '**/details_harness|winogrande|5_2024-03-25T05-45-49.471582.parquet' - split: 2024_03_25T07_53_45.933606 path: - '**/details_harness|winogrande|5_2024-03-25T07-53-45.933606.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-25T07-53-45.933606.parquet' - config_name: results data_files: - split: 2024_03_25T05_45_49.471582 path: - results_2024-03-25T05-45-49.471582.parquet - split: 2024_03_25T07_53_45.933606 path: - results_2024-03-25T07-53-45.933606.parquet - split: latest path: - results_2024-03-25T07-53-45.933606.parquet --- # Dataset Card for Evaluation run of ajibawa-2023/Code-Mistral-7B  Dataset automatically created during the evaluation run of model [ajibawa-2023/Code-Mistral-7B](https://huggingface.co/ajibawa-2023/Code-Mistral-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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_ajibawa-2023__Code-Mistral-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-25T07:53:45.933606](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Code-Mistral-7B/blob/main/results_2024-03-25T07-53-45.933606.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": 0.6527492220016492, "acc_stderr": 0.031870603059274874, "acc_norm": 0.6533709217123561, "acc_norm_stderr": 0.0325259522142822, "mc1": 0.3684210526315789, "mc1_stderr": 0.016886551261046046, "mc2": 0.5463721037747236, "mc2_stderr": 0.015046435516843176 }, "harness|arc:challenge|25": { "acc": 0.6083617747440273, "acc_stderr": 0.014264122124938213, "acc_norm": 0.6459044368600683, "acc_norm_stderr": 0.01397545412275656 }, "harness|hellaswag|10": { "acc": 0.6560446126269668, "acc_stderr": 0.0047405557821421735, "acc_norm": 0.8529177454690301, "acc_norm_stderr": 0.0035346403488166773 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6666666666666666, "acc_stderr": 0.04072314811876837, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.04072314811876837 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7236842105263158, "acc_stderr": 0.03639057569952929, "acc_norm": 0.7236842105263158, "acc_norm_stderr": 0.03639057569952929 }, "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": 0.7245283018867924, "acc_stderr": 0.02749566368372406, "acc_norm": 0.7245283018867924, "acc_norm_stderr": 0.02749566368372406 }, "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": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6878612716763006, "acc_stderr": 0.03533133389323657, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.03533133389323657 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.39215686274509803, "acc_stderr": 0.04858083574266345, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.04858083574266345 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4074074074074074, "acc_stderr": 0.02530590624159063, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.02530590624159063 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5158730158730159, "acc_stderr": 0.044698818540726076, "acc_norm": 0.5158730158730159, "acc_norm_stderr": 0.044698818540726076 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7806451612903226, "acc_stderr": 0.023540799358723295, "acc_norm": 0.7806451612903226, "acc_norm_stderr": 0.023540799358723295 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.0328766675860349, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.0328766675860349 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8131313131313131, "acc_stderr": 0.027772533334218967, "acc_norm": 0.8131313131313131, "acc_norm_stderr": 0.027772533334218967 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.917098445595855, "acc_stderr": 0.01989934131572178, "acc_norm": 0.917098445595855, "acc_norm_stderr": 0.01989934131572178 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6743589743589744, "acc_stderr": 0.02375966576741229, "acc_norm": 0.6743589743589744, "acc_norm_stderr": 0.02375966576741229 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34814814814814815, "acc_stderr": 0.029045600290616255, "acc_norm": 0.34814814814814815, "acc_norm_stderr": 0.029045600290616255 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.680672268907563, "acc_stderr": 0.0302839955258844, "acc_norm": 0.680672268907563, "acc_norm_stderr": 0.0302839955258844 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "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": 0.5185185185185185, "acc_stderr": 0.034076320938540516, "acc_norm": 0.5185185185185185, "acc_norm_stderr": 0.034076320938540516 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931796, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931796 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8185654008438819, "acc_stderr": 0.02508596114457966, "acc_norm": 0.8185654008438819, "acc_norm_stderr": 0.02508596114457966 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7219730941704036, "acc_stderr": 0.030069584874494043, "acc_norm": 0.7219730941704036, "acc_norm_stderr": 0.030069584874494043 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8015267175572519, "acc_stderr": 0.03498149385462472, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.03498149385462472 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7962962962962963, "acc_stderr": 0.03893542518824847, "acc_norm": 0.7962962962962963, "acc_norm_stderr": 0.03893542518824847 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7361963190184049, "acc_stderr": 0.034624199316156234, "acc_norm": 0.7361963190184049, "acc_norm_stderr": 0.034624199316156234 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8352490421455939, "acc_stderr": 0.013265346261323786, "acc_norm": 0.8352490421455939, "acc_norm_stderr": 0.013265346261323786 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7427745664739884, "acc_stderr": 0.023532925431044287, "acc_norm": 0.7427745664739884, "acc_norm_stderr": 0.023532925431044287 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3195530726256983, "acc_stderr": 0.01559552029414741, "acc_norm": 0.3195530726256983, "acc_norm_stderr": 0.01559552029414741 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7189542483660131, "acc_stderr": 0.025738854797818733, "acc_norm": 0.7189542483660131, "acc_norm_stderr": 0.025738854797818733 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7138263665594855, "acc_stderr": 0.02567025924218893, "acc_norm": 0.7138263665594855, "acc_norm_stderr": 0.02567025924218893 }, "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": 0.5, "acc_stderr": 0.029827499313594685, "acc_norm": 0.5, "acc_norm_stderr": 0.029827499313594685 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.47783572359843546, "acc_stderr": 0.012757683047716175, "acc_norm": 0.47783572359843546, "acc_norm_stderr": 0.012757683047716175 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6948529411764706, "acc_stderr": 0.027971541370170598, "acc_norm": 0.6948529411764706, "acc_norm_stderr": 0.027971541370170598 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.684640522875817, "acc_stderr": 0.01879808628488689, "acc_norm": 0.684640522875817, "acc_norm_stderr": 0.01879808628488689 }, "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": 0.726530612244898, "acc_stderr": 0.02853556033712844, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.02853556033712844 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482707, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482707 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197769, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197769 }, "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": 0.8421052631578947, "acc_stderr": 0.027966785859160893, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.027966785859160893 }, "harness|truthfulqa:mc|0": { "mc1": 0.3684210526315789, "mc1_stderr": 0.016886551261046046, "mc2": 0.5463721037747236, "mc2_stderr": 0.015046435516843176 }, "harness|winogrande|5": { "acc": 0.8224151539068666, "acc_stderr": 0.010740676861359226 }, "harness|gsm8k|5": { "acc": 0.6808188021228203, "acc_stderr": 0.012840345676251648 } } ``` ## 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]

KETI-AIR/kor_ropes

--- pretty_name: ROPES language: - ko license: - cc-by-4.0 size_categories: - 10K<n<100K task_categories: - question-answering task_ids: - extractive-qa dataset_info: features: - name: data_index_by_user dtype: int32 - name: background dtype: string - name: situation dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string splits: - name: train num_bytes: 13608462 num_examples: 10924 - name: validation num_bytes: 1864822 num_examples: 1688 - name: test num_bytes: 2158508 num_examples: 1710 download_size: 1465973 dataset_size: 17631792 --- # Dataset Card for ROPES ## Licensing Information The data is distributed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. ## Source Data Citation INformation ``` @inproceedings{Lin2019ReasoningOP, title={Reasoning Over Paragraph Effects in Situations}, author={Kevin Lin and Oyvind Tafjord and Peter Clark and Matt Gardner}, booktitle={MRQA@EMNLP}, year={2019} }

aditijha/instruct_v1_1k_and_lima

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: source dtype: string splits: - name: train num_bytes: 3698244 num_examples: 2000 download_size: 2042056 dataset_size: 3698244 --- # Dataset Card for "instruct_v1_1k_and_lima" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

arunrajuamrutha3/martin_valen_dataset

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 82739.0 num_examples: 10 download_size: 82646 dataset_size: 82739.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

gowitheflow/wiki-span

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string splits: - name: train num_bytes: 14498836027 num_examples: 6458670 download_size: 8956015300 dataset_size: 14498836027 --- # Dataset Card for "wiki-span" This dataset is constructed by sampling 25%-50% of each wikipedia record twice, as positive pairs. It can be used to train unsupervised sentence representation models.

Seanxh/twitter_dataset_1713212801

--- 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: 176063 num_examples: 412 download_size: 62997 dataset_size: 176063 configs: - config_name: default data_files: - split: train path: data/train-* ---

nlpso/m2m3_fine_tuning_ocr_ptrn_cmbert_iob2

--- language: - fr multilinguality: - monolingual task_categories: - token-classification --- # m2m3_fine_tuning_ocr_ptrn_cmbert_iob2 ## Introduction This dataset was used to fine-tuned [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) for **nested NER task** using Independant NER layers approach [M1]. It contains Paris trade directories entries from the 19th century. ## Dataset parameters * Approachrd : M2 and M3 * Dataset type : noisy (Pero OCR) * Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) * Tagging format : IOB2 * Counts : * Train : 6084 * Dev : 676 * Test : 1685 * Associated fine-tuned models : * M2 : [nlpso/m2_joint_label_ocr_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m2_joint_label_ocr_ptrn_cmbert_iob2) * M3 : [nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_iob2](https://huggingface.co/nlpso/m3_hierarchical_ner_ocr_ptrn_cmbert_iob2) ## Entity types Abbreviation|Entity group (level)|Description -|-|- O |1 & 2|Outside of a named entity PER |1|Person or company name ACT |1 & 2|Person or company professional activity TITREH |2|Military or civil distinction DESC |1|Entry full description TITREP |2|Professionnal reward SPAT |1|Address LOC |2|Street name CARDINAL |2|Street number FT |2|Geographical feature ## How to use this dataset ```python from datasets import load_dataset train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ocr_ptrn_cmbert_iob2")

Fsoft-AIC/the-vault-function

--- language: - code - en multilinguality: - multiprogramming languages task_categories: - text-generation license: mit dataset_info: features: - name: identifier dtype: string - name: return_type dtype: string - name: repo dtype: string - name: path dtype: string - name: language dtype: string - name: code dtype: string - name: code_tokens dtype: string - name: original_docstring dtype: string - name: comment dtype: string - name: docstring_tokens dtype: string - name: docstring dtype: string - name: original_string dtype: string pretty_name: The Vault Function viewer: true --- ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Statistics](#dataset-statistics) - [Usage](#usage) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [FSoft-AI4Code/TheVault](https://github.com/FSoft-AI4Code/TheVault) - **Paper:** [The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation](https://arxiv.org/abs/2305.06156) - **Contact:** support.ailab@fpt.com - **Website:** https://www.fpt-aicenter.com/ai-residency/ <p align="center"> <img src="https://raw.githubusercontent.com/FSoft-AI4Code/TheVault/main/assets/the-vault-4-logo-png.png" width="300px" alt="logo"> </p> <div align="center"> # The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation </div> ## Dataset Summary The Vault dataset is a comprehensive, large-scale, multilingual parallel dataset that features high-quality code-text pairs derived from The Stack, the largest permissively-licensed source code dataset. We provide The Vault which contains code snippets from 10 popular programming languages such as Java, JavaScript, Python, Ruby, Rust, Golang, C#, C++, C, and PHP. This dataset provides multiple code-snippet levels, metadata, and 11 docstring styles for enhanced usability and versatility. ## Supported Tasks The Vault can be used for pretraining LLMs or downstream code-text interaction tasks. A number of tasks related to code understanding and geneartion can be constructed using The Vault such as *code summarization*, *text-to-code generation* and *code search*. ## Languages The natural language text (docstring) is in English. 10 programming languages are supported in The Vault: `Python`, `Java`, `JavaScript`, `PHP`, `C`, `C#`, `C++`, `Go`, `Ruby`, `Rust` ## Dataset Structure ### Data Instances ``` { "hexsha": "5c47f0b4c173a8fd03e4e633d9b3dd8211e67ad0", "repo": "neumanna94/beepboop", "path": "js/scripts.js", "license": [ "MIT" ], "language": "JavaScript", "identifier": "beepBoopSelector", "return_type": "<not_specific>", "original_string": "function beepBoopSelector(inputString, bbFunction){\n if(bbFunction==1){\n return beepBoop(inputString);\n } else if(bbFunction==2){\n return beepBoop2(inputString);\n } else if(bbFunction==3){\n return beepBoop3(inputString);\n } else {\n }\n}", "original_docstring": "//Determines what beepBoop function to use", "docstring": "Determines what beepBoop function to use", "docstring_tokens": [ "Determines", "what", "beepBoop", "function", "to", "use" ], "code": "function beepBoopSelector(inputString, bbFunction){\n if(bbFunction==1){\n return beepBoop(inputString);\n } else if(bbFunction==2){\n return beepBoop2(inputString);\n } else if(bbFunction==3){\n return beepBoop3(inputString);\n } else {\n }\n}", "code_tokens": [ "function", "beepBoopSelector", "(", "inputString", ",", "bbFunction", ")", "{", "if", "(", "bbFunction", "==", "1", ")", "{", "return", "beepBoop", "(", "inputString", ")", ";", "}", "else", "if", "(", "bbFunction", "==", "2", ")", "{", "return", "beepBoop2", "(", "inputString", ")", ";", "}", "else", "if", "(", "bbFunction", "==", "3", ")", "{", "return", "beepBoop3", "(", "inputString", ")", ";", "}", "else", "{", "}", "}" ], "short_docstring": "Determines what beepBoop function to use", "short_docstring_tokens": [ "Determines", "what", "beepBoop", "function", "to", "use" ], "comment": [], "parameters": [ { "param": "inputString", "type": null }, { "param": "bbFunction", "type": null } ], "docstring_params": { "returns": [], "raises": [], "params": [ { "identifier": "inputString", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null }, { "identifier": "bbFunction", "type": null, "docstring": null, "docstring_tokens": [], "default": null, "is_optional": null } ], "outlier_params": [], "others": [] } } ``` ### Data Fields Data fields for function level: - **hexsha** (string): the unique git hash of file - **repo** (string): the owner/repo - **path** (string): the full path to the original file - **license** (list): licenses in the repo - **language** (string): the programming language - **identifier** (string): the function or method name - **return_type** (string): the type returned by the function - **original_string** (string): original version of function/class node - **original_docstring** (string): the raw string before tokenization or parsing - **code** (string): the part of the original that is code - **code_tokens** (list): tokenized version of `code` - **short_docstring** (string): short, brief summarization (first line of the docstring) - **short_docstring_tokens** (list): tokenized version of `short_docstring - **docstring** (string): the top-level comment or docstring (docstring version without param’s doc, return, exception fields, etc) - **docstring_tokens** (list): tokenized version of docstring - **comment** (list): list of comments (line) inside the function/class - **parameters** (list): List of parameters and its type (type can be None) - **docstring_params** (dict): Dictionary of the parsed information from docstring See [here](https://github.com/FSoft-AI4Code/TheVault/blob/main/data/README.md) for more details and examples. ### Data Splits In this repo, The Vault is divided into 5 subsets, where three training versions are split based on size of the full training set, and the remains are validation set and test set (approximate 20,000 samples in each). The statistic for languages in each split set is illustrated in the following section. Before split, the dataset is deduplicated. There are 3 versions of training set that are small (5%), medium (20%) and large (100%). ## Dataset Statistics - Compare to other benchmarks | Dataset | #Language | #Code-text pair | |:--------------------------|----------:|-----------------:| | PyMT5 | 1 | ≈ 7,700,000 | | CoDesc | 1 | 4,211,516 | | CodeSearchNet | 6 | 2,326,976 | | CodeSearchNet (CodeXGLUE) | 6 | 1,005,474 | | Deepcom | 1 | 424,028 | | CONCODE | 1 | 2,184,310 | | Funcom | 1 | 2,149,121 | | CodeT5 | 8 | 3,158,313 | | **The Vault** | **10** | **34,098,775** | - Statistic for split sets | | train/small | train/medium | train/full | validation | test | total | |:-----------|------------:|-------------:|-----------:|-----------:|-------:|--------------:| |Python | 370,657 | 1,952,110 | 7,772,647 | 30,992 | 21,652 | 7,825,291 | |Java | 351,213 | 1,612,366 | 6,629,193 | 22,677 | 15,552 | 6,667,422 | |JavaScript | 82,931 | 404,729 | 1,640,416 | 22,044 | 21,108 | 1,683,568 | |PHP | 236,638 | 1,155,476 | 4,656,371 | 21,375 | 19,010 | 4,696,756 | |C | 105,978 | 381,207 | 1,639,319 | 27,525 | 19,122 | 1,685,966 | |C# | 141,090 | 783,166 | 3,305,891 | 24,787 | 19,638 | 3,350,316 | |C++ | 87,420 | 410,907 | 1,671,268 | 20,011 | 18,169 | 1,709,448 | |Go | 267,535 | 1,319,547 | 5,109,020 | 19,102 | 25,314 | 5,153,436 | |Ruby | 23,921 | 112,574 | 424,339 | 17,338 | 19,908 | 461,585 | |Rust | 35,367 | 224,015 | 825,130 | 16,716 | 23,141 | 864,987 | |TOTAL | 1,702,750 | 8,356,097 |33,673,594 |222,567 |202,614 |**34,098,775** | ## Usage You can load The Vault dataset using datasets library: ```pip install datasets``` ```python from datasets import load_dataset # Load full function level dataset (34M samples) dataset = load_dataset("Fsoft-AIC/the-vault-function") # Load function level train/validation/test set dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"]) # Load "small" (or "medium", "full") version of function level training set dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train/small"]) # specific language (e.g. Python) dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"], languages=['Python']) # dataset streaming data = load_dataset("Fsoft-AIC/the-vault-function", split_set= ["train"], streaming= True) for sample in iter(data['train']): print(sample) ``` A back up dataset can be downloaded in azure storage. See [Download The Vault from Azure blob storage](https://github.com/FSoft-AI4Code/TheVault#download-via-link). ## Additional information ### Licensing Information MIT License ### Citation Information ``` @article{manh2023vault, title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation}, author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ}, journal={arXiv preprint arXiv:2305.06156}, year={2023} } ``` ### Contributions This dataset is developed by [FSOFT AI4Code team](https://github.com/FSoft-AI4Code).

ahishamm/isic_binary_augmented

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': benign '1': malignant splits: - name: train num_bytes: 97831304.566 num_examples: 17214 - name: test num_bytes: 44333792.176 num_examples: 7804 download_size: 152665521 dataset_size: 142165096.74199998 --- # Dataset Card for "isic_binary_augmented" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pkufool/libriheavy

--- license: apache-2.0 --- # Libriheavy: a 50,000 hours ASR corpus with punctuation casing and context Libriheavy is a labeled version of [Librilight](https://github.com/facebookresearch/libri-light), read our [paper](https://arxiv.org/abs/2309.08105) for more details. See https://github.com/k2-fsa/libriheavy for more details. ## Citation ``` @misc{kang2023libriheavy, title={Libriheavy: a 50,000 hours ASR corpus with punctuation casing and context}, author={Wei Kang and Xiaoyu Yang and Zengwei Yao and Fangjun Kuang and Yifan Yang and Liyong Guo and Long Lin and Daniel Povey}, year={2023}, eprint={2309.08105}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```

arthurneuron/USDC-WETH-Uniswap-V3-2021-to-2023

--- license: mit ---

qgiaohc/twitter_dataset_1713183929

--- 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: 24781 num_examples: 55 download_size: 13092 dataset_size: 24781 configs: - config_name: default data_files: - split: train path: data/train-* ---

dim/SlimOrcaRU

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string - name: value_ru dtype: string - name: weight dtype: float64 - name: key dtype: int64 splits: - name: train num_bytes: 183635644 num_examples: 47536 download_size: 83293621 dataset_size: 183635644 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "SlimOrcaRU" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

BitTranslate/indonesiantest

--- license: cc0-1.0 ---

ricardo-lsantos/my_cool_dataset

--- license: mit language: - pt pretty_name: My Cool Dataset ---

Atipico1/nq_test_adversary

--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: gpt_answer_sentence dtype: string - name: gpt_adv_sentence dtype: string - name: is_valid_sentence dtype: bool - name: gpt_adv_passage dtype: string - name: is_valid_passage dtype: bool splits: - name: train num_bytes: 14371495 num_examples: 3610 download_size: 8513525 dataset_size: 14371495 configs: - config_name: default data_files: - split: train path: data/train-* ---

alvations/c4p0-v1-de-en

--- dataset_info: features: - name: source dtype: string - name: target dtype: string - name: target_backto_source dtype: string - name: raw_target list: - name: generated_text dtype: string - name: raw_target_backto_source list: - name: generated_text dtype: string - name: prompt dtype: string - name: reverse_prompt dtype: string - name: source_langid dtype: string - name: target_langid dtype: string - name: target_backto_source_langid dtype: string - name: doc_id dtype: int64 - name: sent_id dtype: int64 - name: timestamp dtype: string - name: url dtype: string - name: doc_hash dtype: string - name: dataset dtype: string - name: source_lang dtype: string - name: target_lang dtype: string splits: - name: train num_bytes: 18411738 num_examples: 15146 download_size: 7768565 dataset_size: 18411738 configs: - config_name: default data_files: - split: train path: data/train-* ---

LNTANOooo/tulu_v3

--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: role dtype: string splits: - name: science.scierc_ner num_bytes: 634623.0 num_examples: 349 - name: sharegpt num_bytes: 776319873.4338813 num_examples: 72413 - name: science.scifact_json num_bytes: 2350372.0 num_examples: 919 - name: lima num_bytes: 2815967.0 num_examples: 1012 - name: gpt4_alpaca num_bytes: 16091564.0 num_examples: 19834 - name: science.evidence_inference num_bytes: 6620099.0 num_examples: 1673 - name: oasst1 num_bytes: 11027612.499452954 num_examples: 7046 - name: science.scitldr_aic num_bytes: 13392412.0 num_examples: 1957 - name: science.scierc_relation num_bytes: 735295.0 num_examples: 349 - name: science.qasper_truncated_4000 num_bytes: 34952831.0 num_examples: 2204 - name: hard_coded num_bytes: 44940.0 num_examples: 90 - name: code_alpaca num_bytes: 7102581.0 num_examples: 19992 - name: cot num_bytes: 56091350.817187 num_examples: 49709 - name: wizardlm num_bytes: 69442958.16317087 num_examples: 29597 - name: open_orca num_bytes: 52677835.20356853 num_examples: 29581 - name: flan_v2 num_bytes: 105654005.53780366 num_examples: 49108 download_size: 518036574 dataset_size: 1155954319.6550643 configs: - config_name: default data_files: - split: science.scierc_ner path: data/science.scierc_ner-* - split: sharegpt path: data/sharegpt-* - split: science.scifact_json path: data/science.scifact_json-* - split: lima path: data/lima-* - split: gpt4_alpaca path: data/gpt4_alpaca-* - split: science.evidence_inference path: data/science.evidence_inference-* - split: oasst1 path: data/oasst1-* - split: science.scitldr_aic path: data/science.scitldr_aic-* - split: science.scierc_relation path: data/science.scierc_relation-* - split: science.qasper_truncated_4000 path: data/science.qasper_truncated_4000-* - split: hard_coded path: data/hard_coded-* - split: code_alpaca path: data/code_alpaca-* - split: cot path: data/cot-* - split: wizardlm path: data/wizardlm-* - split: open_orca path: data/open_orca-* - split: flan_v2 path: data/flan_v2-* ---

mozci/tinysketch

--- license: cc-by-nc-sa-4.0 language: - en language_creators: - machine-generated multilinguality: - monolingual pretty_name: 'Sketch Scene Descriptions' size_categories: - n<10K source_datasets: - FS-COCO tags: [] task_categories: - text-to-image task_ids: [] --- # Dataset Card for Sketch Scene Descriptions _Dataset used to train [Sketch Scene text to image model]()_ We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description. For each row, the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided. ## Citation If you use this dataset, please cite it as: ``` @inproceedings{fscoco, title={FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context.} author={Chowdhury, Pinaki Nath and Sain, Aneeshan and Bhunia, Ayan Kumar and Xiang, Tao and Gryaditskaya, Yulia and Song, Yi-Zhe}, booktitle={ECCV}, year={2022} } ```

mugithi/ubuntu_question_answer_jsonl

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 2073677 num_examples: 12100 - name: test num_bytes: 882250 num_examples: 5186 download_size: 0 dataset_size: 2955927 --- # Dataset Card for "ubuntu_question_answer_jsonl" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

multi_re_qa

--- annotations_creators: - expert-generated - found language_creators: - expert-generated - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - 1M<n<10M source_datasets: - extended|other-BioASQ - extended|other-DuoRC - extended|other-HotpotQA - extended|other-Natural-Questions - extended|other-Relation-Extraction - extended|other-SQuAD - extended|other-SearchQA - extended|other-TextbookQA - extended|other-TriviaQA task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: multireqa pretty_name: MultiReQA dataset_info: - config_name: SearchQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 183902877 num_examples: 3163801 - name: validation num_bytes: 26439174 num_examples: 454836 download_size: 36991959 dataset_size: 210342051 - config_name: TriviaQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 107326326 num_examples: 1893674 - name: validation num_bytes: 13508062 num_examples: 238339 download_size: 21750402 dataset_size: 120834388 - config_name: HotpotQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 29516866 num_examples: 508879 - name: validation num_bytes: 3027229 num_examples: 52191 download_size: 6343389 dataset_size: 32544095 - config_name: SQuAD features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 16828974 num_examples: 95659 - name: validation num_bytes: 2012997 num_examples: 10642 download_size: 3003646 dataset_size: 18841971 - config_name: NaturalQuestions features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 28732767 num_examples: 448355 - name: validation num_bytes: 1418124 num_examples: 22118 download_size: 6124487 dataset_size: 30150891 - config_name: BioASQ features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 766190 num_examples: 14158 download_size: 156649 dataset_size: 766190 - config_name: RelationExtraction features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 217870 num_examples: 3301 download_size: 73019 dataset_size: 217870 - config_name: TextbookQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 4182675 num_examples: 71147 download_size: 704602 dataset_size: 4182675 - config_name: DuoRC features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 1483518 num_examples: 5525 download_size: 97625 dataset_size: 1483518 config_names: - BioASQ - DuoRC - HotpotQA - NaturalQuestions - RelationExtraction - SQuAD - SearchQA - TextbookQA - TriviaQA --- # Dataset Card for MultiReQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/google-research-datasets/MultiReQA - **Repository:** https://github.com/google-research-datasets/MultiReQA - **Paper:** https://arxiv.org/pdf/2005.02507.pdf - **Leaderboard:** - **Point of Contact:** ### Dataset Summary MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data (also includes DuoRC but not specified in the official documentation) ### Supported Tasks and Leaderboards - Question answering (QA) - Retrieval question answering (ReQA) ### Languages Sentence boundary annotation for SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, TextbookQA and DuoRC ## Dataset Structure ### Data Instances The general format is: ` { "candidate_id": <candidate_id>, "response_start": <response_start>, "response_end": <response_end> } ... ` An example from SearchQA: `{'candidate_id': 'SearchQA_000077f3912049dfb4511db271697bad/_0_1', 'response_end': 306, 'response_start': 243} ` ### Data Fields ` { "candidate_id": <STRING>, "response_start": <INT>, "response_end": <INT> } ... ` - **candidate_id:** The candidate id of the candidate sentence. It consists of the original qid from the MRQA shared task. - **response_start:** The start index of the sentence with respect to its original context. - **response_end:** The end index of the sentence with respect to its original context ### Data Splits Train and Dev splits are available only for the following datasets, - SearchQA - TriviaQA - HotpotQA - SQuAD - NaturalQuestions Test splits are available only for the following datasets, - BioASQ - RelationExtraction - TextbookQA The number of candidate sentences for each dataset in the table below. | | MultiReQA | | |--------------------|-----------|---------| | | train | test | | SearchQA | 629,160 | 454,836 | | TriviaQA | 335,659 | 238,339 | | HotpotQA | 104,973 | 52,191 | | SQuAD | 87,133 | 10,642 | | NaturalQuestions | 106,521 | 22,118 | | BioASQ | - | 14,158 | | RelationExtraction | - | 3,301 | | TextbookQA | - | 3,701 | ## Dataset Creation ### Curation Rationale MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the [MRQA shared task](https://mrqa.github.io/). The dataset was curated by converting existing QA datasets from [MRQA shared task](https://mrqa.github.io/) to the format of MultiReQA benchmark. ### Source Data #### Initial Data Collection and Normalization The Initial data collection was performed by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository ### 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 The annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{m2020multireqa, title={MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models}, author={Mandy Guo and Yinfei Yang and Daniel Cer and Qinlan Shen and Noah Constant}, year={2020}, eprint={2005.02507}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@Karthik-Bhaskar](https://github.com/Karthik-Bhaskar) for adding this dataset.

open-llm-leaderboard/details_alnrg2arg__test_wanda_240109

--- pretty_name: Evaluation run of alnrg2arg/test_wanda_240109 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [alnrg2arg/test_wanda_240109](https://huggingface.co/alnrg2arg/test_wanda_240109)\ \ 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 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the 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_alnrg2arg__test_wanda_240109\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-13T17:19:19.094893](https://huggingface.co/datasets/open-llm-leaderboard/details_alnrg2arg__test_wanda_240109/blob/main/results_2024-01-13T17-19-19.094893.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\": 0.23401038489636644,\n\ \ \"acc_stderr\": 0.029968361313724278,\n \"acc_norm\": 0.23351347966222002,\n\ \ \"acc_norm_stderr\": 0.0307471687800331,\n \"mc1\": 1.0,\n \ \ \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n \ \ },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.22525597269624573,\n\ \ \"acc_stderr\": 0.012207839995407305,\n \"acc_norm\": 0.2295221843003413,\n\ \ \"acc_norm_stderr\": 0.012288926760890797\n },\n \"harness|hellaswag|10\"\ : {\n \"acc\": 0.25542720573590916,\n \"acc_stderr\": 0.004352098082984431,\n\ \ \"acc_norm\": 0.2526389165504879,\n \"acc_norm_stderr\": 0.004336375492801798\n\ \ },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.22,\n\ \ \"acc_stderr\": 0.04163331998932268,\n \"acc_norm\": 0.22,\n \ \ \"acc_norm_stderr\": 0.04163331998932268\n },\n \"harness|hendrycksTest-anatomy|5\"\ : {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.03355677216313142,\n\ \ \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.03355677216313142\n\ \ },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.29605263157894735,\n\ \ \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.29605263157894735,\n\ \ \"acc_norm_stderr\": 0.03715062154998904\n },\n \"harness|hendrycksTest-business_ethics|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768077,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768077\n \ \ },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.21509433962264152,\n\ \ \"acc_stderr\": 0.02528839450289137,\n \"acc_norm\": 0.21509433962264152,\n\ \ \"acc_norm_stderr\": 0.02528839450289137\n },\n \"harness|hendrycksTest-college_biology|5\"\ : {\n \"acc\": 0.2569444444444444,\n \"acc_stderr\": 0.03653946969442099,\n\ \ \"acc_norm\": 0.2569444444444444,\n \"acc_norm_stderr\": 0.03653946969442099\n\ \ },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\":\ \ 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n\ \ \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-college_computer_science|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\ \ \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.18,\n\ \ \"acc_stderr\": 0.038612291966536934,\n \"acc_norm\": 0.18,\n \ \ \"acc_norm_stderr\": 0.038612291966536934\n },\n \"harness|hendrycksTest-college_medicine|5\"\ : {\n \"acc\": 0.20809248554913296,\n \"acc_stderr\": 0.030952890217749874,\n\ \ \"acc_norm\": 0.20809248554913296,\n \"acc_norm_stderr\": 0.030952890217749874\n\ \ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n\ \ \"acc_stderr\": 0.04092563958237654,\n \"acc_norm\": 0.21568627450980393,\n\ \ \"acc_norm_stderr\": 0.04092563958237654\n },\n \"harness|hendrycksTest-computer_security|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\":\ \ 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n \"\ acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\ \ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\ \ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\ acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\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\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \ \ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"\ acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.15763546798029557,\n \"acc_stderr\": 0.025639014131172404,\n \"\ acc_norm\": 0.15763546798029557,\n \"acc_norm_stderr\": 0.025639014131172404\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.17,\n \"acc_stderr\": 0.037752516806863715,\n \"acc_norm\"\ : 0.17,\n \"acc_norm_stderr\": 0.037752516806863715\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\ acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n\ \ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.20256410256410257,\n \"acc_stderr\": 0.020377660970371372,\n\ \ \"acc_norm\": 0.20256410256410257,\n \"acc_norm_stderr\": 0.020377660970371372\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \ \ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.24789915966386555,\n \"acc_stderr\": 0.028047967224176892,\n\ \ \"acc_norm\": 0.24789915966386555,\n \"acc_norm_stderr\": 0.028047967224176892\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"\ acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"\ acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"\ acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\ \ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2900763358778626,\n \"acc_stderr\": 0.03980066246467765,\n\ \ \"acc_norm\": 0.2900763358778626,\n \"acc_norm_stderr\": 0.03980066246467765\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2892561983471074,\n \"acc_stderr\": 0.041391127276354626,\n \"\ acc_norm\": 0.2892561983471074,\n \"acc_norm_stderr\": 0.041391127276354626\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2962962962962963,\n\ \ \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.2962962962962963,\n\ \ \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\ \ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n\ \ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\ \ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\ \ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n\ \ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\ \ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.22349936143039592,\n\ \ \"acc_stderr\": 0.014897235229450707,\n \"acc_norm\": 0.22349936143039592,\n\ \ \"acc_norm_stderr\": 0.014897235229450707\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.26878612716763006,\n \"acc_stderr\": 0.023868003262500114,\n\ \ \"acc_norm\": 0.26878612716763006,\n \"acc_norm_stderr\": 0.023868003262500114\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2435754189944134,\n\ \ \"acc_stderr\": 0.014355911964767864,\n \"acc_norm\": 0.2435754189944134,\n\ \ \"acc_norm_stderr\": 0.014355911964767864\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\ \ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2508038585209003,\n\ \ \"acc_stderr\": 0.024619771956697165,\n \"acc_norm\": 0.2508038585209003,\n\ \ \"acc_norm_stderr\": 0.024619771956697165\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\ \ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \ \ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\ \ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\ \ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\ \ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\ : 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\ : {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n\ \ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n\ \ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.18775510204081633,\n\ \ \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n\ \ \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness|hendrycksTest-sociology|5\"\ : {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n\ \ \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n\ \ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\ \ 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-virology|5\"\ : {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n\ \ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n\ \ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.3216374269005848,\n\ \ \"acc_stderr\": 0.03582529442573122,\n \"acc_norm\": 0.3216374269005848,\n\ \ \"acc_norm_stderr\": 0.03582529442573122\n },\n \"harness|truthfulqa:mc|0\"\ : {\n \"mc1\": 1.0,\n \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n\ \ \"mc2_stderr\": NaN\n },\n \"harness|winogrande|5\": {\n \"\ acc\": 0.4988161010260458,\n \"acc_stderr\": 0.014052446290529019\n },\n\ \ \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n\ \ }\n}\n```" repo_url: https://huggingface.co/alnrg2arg/test_wanda_240109 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_13T17_14_43.764095 path: - '**/details_harness|arc:challenge|25_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|arc:challenge|25_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-13T17-19-19.094893.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|gsm8k|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|gsm8k|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hellaswag|10_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hellaswag|10_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-13T17-14-43.764095.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-13T17-19-19.094893.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-management|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-management|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T17-19-19.094893.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|truthfulqa:mc|0_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|truthfulqa:mc|0_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-13T17-19-19.094893.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_13T17_14_43.764095 path: - '**/details_harness|winogrande|5_2024-01-13T17-14-43.764095.parquet' - split: 2024_01_13T17_19_19.094893 path: - '**/details_harness|winogrande|5_2024-01-13T17-19-19.094893.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-13T17-19-19.094893.parquet' - config_name: results data_files: - split: 2024_01_13T17_14_43.764095 path: - results_2024-01-13T17-14-43.764095.parquet - split: 2024_01_13T17_19_19.094893 path: - results_2024-01-13T17-19-19.094893.parquet - split: latest path: - results_2024-01-13T17-19-19.094893.parquet --- # Dataset Card for Evaluation run of alnrg2arg/test_wanda_240109  Dataset automatically created during the evaluation run of model [alnrg2arg/test_wanda_240109](https://huggingface.co/alnrg2arg/test_wanda_240109) 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_alnrg2arg__test_wanda_240109", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-13T17:19:19.094893](https://huggingface.co/datasets/open-llm-leaderboard/details_alnrg2arg__test_wanda_240109/blob/main/results_2024-01-13T17-19-19.094893.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": 0.23401038489636644, "acc_stderr": 0.029968361313724278, "acc_norm": 0.23351347966222002, "acc_norm_stderr": 0.0307471687800331, "mc1": 1.0, "mc1_stderr": 0.0, "mc2": NaN, "mc2_stderr": NaN }, "harness|arc:challenge|25": { "acc": 0.22525597269624573, "acc_stderr": 0.012207839995407305, "acc_norm": 0.2295221843003413, "acc_norm_stderr": 0.012288926760890797 }, "harness|hellaswag|10": { "acc": 0.25542720573590916, "acc_stderr": 0.004352098082984431, "acc_norm": 0.2526389165504879, "acc_norm_stderr": 0.004336375492801798 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.22, "acc_stderr": 0.04163331998932268, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932268 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.18518518518518517, "acc_stderr": 0.03355677216313142, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.03355677216313142 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.29605263157894735, "acc_stderr": 0.03715062154998904, "acc_norm": 0.29605263157894735, "acc_norm_stderr": 0.03715062154998904 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.26, "acc_stderr": 0.04408440022768077, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768077 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.21509433962264152, "acc_stderr": 0.02528839450289137, "acc_norm": 0.21509433962264152, "acc_norm_stderr": 0.02528839450289137 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2569444444444444, "acc_stderr": 0.03653946969442099, "acc_norm": 0.2569444444444444, "acc_norm_stderr": 0.03653946969442099 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.2, "acc_stderr": 0.04020151261036845, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036845 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.20809248554913296, "acc_stderr": 0.030952890217749874, "acc_norm": 0.20809248554913296, "acc_norm_stderr": 0.030952890217749874 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.21568627450980393, "acc_stderr": 0.04092563958237654, "acc_norm": 0.21568627450980393, "acc_norm_stderr": 0.04092563958237654 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.26382978723404255, "acc_stderr": 0.028809989854102973, "acc_norm": 0.26382978723404255, "acc_norm_stderr": 0.028809989854102973 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.23684210526315788, "acc_stderr": 0.039994238792813365, "acc_norm": 0.23684210526315788, "acc_norm_stderr": 0.039994238792813365 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135302, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135302 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.20899470899470898, "acc_stderr": 0.02094048156533486, "acc_norm": 0.20899470899470898, "acc_norm_stderr": 0.02094048156533486 }, "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": 0.18, "acc_stderr": 0.038612291966536934, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536934 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.1774193548387097, "acc_stderr": 0.02173254068932927, "acc_norm": 0.1774193548387097, "acc_norm_stderr": 0.02173254068932927 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.15763546798029557, "acc_stderr": 0.025639014131172404, "acc_norm": 0.15763546798029557, "acc_norm_stderr": 0.025639014131172404 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.17, "acc_stderr": 0.037752516806863715, "acc_norm": 0.17, "acc_norm_stderr": 0.037752516806863715 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03225078108306289, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.17676767676767677, "acc_stderr": 0.027178752639044915, "acc_norm": 0.17676767676767677, "acc_norm_stderr": 0.027178752639044915 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.19689119170984457, "acc_stderr": 0.028697873971860664, "acc_norm": 0.19689119170984457, "acc_norm_stderr": 0.028697873971860664 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.20256410256410257, "acc_stderr": 0.020377660970371372, "acc_norm": 0.20256410256410257, "acc_norm_stderr": 0.020377660970371372 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2111111111111111, "acc_stderr": 0.024882116857655075, "acc_norm": 0.2111111111111111, "acc_norm_stderr": 0.024882116857655075 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.24789915966386555, "acc_stderr": 0.028047967224176892, "acc_norm": 0.24789915966386555, "acc_norm_stderr": 0.028047967224176892 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.1986754966887417, "acc_stderr": 0.03257847384436776, "acc_norm": 0.1986754966887417, "acc_norm_stderr": 0.03257847384436776 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.1926605504587156, "acc_stderr": 0.016909276884936094, "acc_norm": 0.1926605504587156, "acc_norm_stderr": 0.016909276884936094 }, 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"harness|hendrycksTest-medical_genetics|5": { "acc": 0.26, "acc_stderr": 0.04408440022768079, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.22349936143039592, "acc_stderr": 0.014897235229450707, "acc_norm": 0.22349936143039592, "acc_norm_stderr": 0.014897235229450707 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.26878612716763006, "acc_stderr": 0.023868003262500114, "acc_norm": 0.26878612716763006, "acc_norm_stderr": 0.023868003262500114 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2435754189944134, "acc_stderr": 0.014355911964767864, "acc_norm": 0.2435754189944134, "acc_norm_stderr": 0.014355911964767864 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.22549019607843138, "acc_stderr": 0.023929155517351284, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.023929155517351284 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2508038585209003, "acc_stderr": 0.024619771956697165, "acc_norm": 0.2508038585209003, "acc_norm_stderr": 0.024619771956697165 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.21604938271604937, "acc_stderr": 0.022899162918445806, "acc_norm": 0.21604938271604937, "acc_norm_stderr": 0.022899162918445806 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.23404255319148937, "acc_stderr": 0.025257861359432417, "acc_norm": 0.23404255319148937, "acc_norm_stderr": 0.025257861359432417 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2457627118644068, "acc_stderr": 0.010996156635142692, "acc_norm": 0.2457627118644068, "acc_norm_stderr": 0.010996156635142692 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.18382352941176472, "acc_stderr": 0.023529242185193106, "acc_norm": 0.18382352941176472, "acc_norm_stderr": 0.023529242185193106 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.25, "acc_stderr": 0.01751781884501444, "acc_norm": 0.25, "acc_norm_stderr": 0.01751781884501444 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03955932861795833, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03955932861795833 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.18775510204081633, "acc_stderr": 0.02500025603954621, "acc_norm": 0.18775510204081633, "acc_norm_stderr": 0.02500025603954621 }, "harness|hendrycksTest-sociology|5": { "acc": 0.24378109452736318, "acc_stderr": 0.03036049015401465, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-virology|5": { "acc": 0.28313253012048195, "acc_stderr": 0.03507295431370518, "acc_norm": 0.28313253012048195, "acc_norm_stderr": 0.03507295431370518 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.3216374269005848, "acc_stderr": 0.03582529442573122, "acc_norm": 0.3216374269005848, "acc_norm_stderr": 0.03582529442573122 }, "harness|truthfulqa:mc|0": { "mc1": 1.0, "mc1_stderr": 0.0, "mc2": NaN, "mc2_stderr": NaN }, "harness|winogrande|5": { "acc": 0.4988161010260458, "acc_stderr": 0.014052446290529019 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description  - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Dataset Structure  [More Information Needed] ## Dataset Creation ### Curation Rationale  [More Information Needed] ### Source Data  #### Data Collection and Processing  [More Information Needed] #### Who are the source data producers?  [More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

aditijha/instruct_v3_10k

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: source dtype: string splits: - name: train num_bytes: 39309622.55416882 num_examples: 10000 download_size: 23617961 dataset_size: 39309622.55416882 --- # Dataset Card for "instruct_v3_10k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nlpso/m0_qualitative_analysis_ref_ptrn_cmbert_io

--- language: - fr multilinguality: - monolingual task_categories: - token-classification --- # m0_qualitative_analysis_ref_ptrn_cmbert_io ## Introduction This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **flat NER task** using Flat NER approach [M0]. It contains 19th-century Paris trade directories' entries. ## Dataset parameters * Approach : M0 * Dataset type : ground-truth * Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) * Tagging format : IO * Counts : * Train : 6084 * Dev : 676 * Test : 1685 * Associated fine-tuned model : [nlpso/m0_flat_ner_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m0_flat_ner_ref_ptrn_cmbert_io) ## Entity types Abbreviation|Description -|- O |Outside of a named entity PER |Person or company name ACT |Person or company professional activity TITRE |Distinction LOC |Street name CARDINAL |Street number FT |Geographical feature ## How to use this dataset ```python from datasets import load_dataset train_dev_test = load_dataset("nlpso/m0_qualitative_analysis_ref_ptrn_cmbert_io")

Maeda-miyazaki/dataset_750

--- license: cc-by-nc-3.0 ---

chats-bug/red-pyjama-sample-1T-max-chunk-16k

--- dataset_info: features: - name: text dtype: string - name: meta dtype: string splits: - name: train num_bytes: 5266104478.188356 num_examples: 924172 - name: test num_bytes: 53198269.811643824 num_examples: 9336 download_size: 3092233105 dataset_size: 5319302748.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

CyberHarem/marina_akizuki_onichichi

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Marina Akizuki This is the dataset of Marina Akizuki, containing 300 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 | 300 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 588 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | raw-stage3-eyes | 730 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. | | 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 588 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 588 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-p512-640 | 502 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. | | stage3-eyes-640 | 730 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. | | stage3-eyes-800 | 730 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |

bengisucam/tr_dataset_combined

--- license: apache-2.0 dataset_info: features: - name: Text dtype: string splits: - name: train num_bytes: 167603259 num_examples: 824809 download_size: 106342453 dataset_size: 167603259 configs: - config_name: default data_files: - split: train path: data/train-* language: - tr --- # Dataset Card for Dataset Name  This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description  This dataset is the combination of the datasets listed below: - BDas/Turkish-Dataset - turkish_product_reviews - winvoker/turkish-sentiment-analysis-dataset - **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]

arthurmluz/xlsum_data-xlsum_gptextsum_results

--- dataset_info: features: - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: summary dtype: string - name: text dtype: string - name: gen_summary dtype: string - name: rouge struct: - name: rouge1 dtype: float64 - name: rouge2 dtype: float64 - name: rougeL dtype: float64 - name: rougeLsum dtype: float64 - name: bert struct: - name: f1 sequence: float64 - name: hashcode dtype: string - name: precision sequence: float64 - name: recall sequence: float64 splits: - name: validation num_bytes: 26244213 num_examples: 7175 download_size: 15951725 dataset_size: 26244213 configs: - config_name: default data_files: - split: validation path: data/validation-* --- # Dataset Card for "wikilingua_data-xlsum_gptextsum_results" rouge= {'rouge1': 0.3230756314331615, 'rouge2': 0.12295752023585965, 'rougeL': 0.23099240967982115, 'rougeLsum': 0.23099240967982115} bert= {'precision': 0.7382304361929877, 'recall': 0.7454116297765061, 'f1': 0.7414375136205958}

lilacai/lilac-Capybara

--- tags: - Lilac --- # lilac/Capybara This dataset is a [Lilac](http://lilacml.com) processed dataset. Original dataset: [https://huggingface.co/datasets/LDJnr/Capybara](https://huggingface.co/datasets/LDJnr/Capybara) To download the dataset to a local directory: ```bash lilac download lilacai/lilac-Capybara ``` or from python with: ```py ll.download("lilacai/lilac-Capybara") ```

one-sec-cv12/chunk_105

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 splits: - name: train num_bytes: 18310590720.0 num_examples: 190640 download_size: 16453314083 dataset_size: 18310590720.0 --- # Dataset Card for "chunk_105" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jaja7744/dolly-15k-cn

--- license: apache-2.0 task_categories: - text-generation language: - zh pretty_name: d size_categories: - 10K<n<100K ---

irds/lotte_recreation_test_search

--- pretty_name: '`lotte/recreation/test/search`' viewer: false source_datasets: ['irds/lotte_recreation_test'] task_categories: - text-retrieval --- # Dataset Card for `lotte/recreation/test/search` The `lotte/recreation/test/search` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/recreation/test/search). # Data This dataset provides: - `queries` (i.e., topics); count=924 - `qrels`: (relevance assessments); count=1,991 - For `docs`, use [`irds/lotte_recreation_test`](https://huggingface.co/datasets/irds/lotte_recreation_test) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/lotte_recreation_test_search', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/lotte_recreation_test_search', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Santhanam2021ColBERTv2, title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction", author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia", journal= "arXiv preprint arXiv:2112.01488", year = "2021", url = "https://arxiv.org/abs/2112.01488" } ```

AliEdalat/Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model

--- license: apache-2.0 task_categories: - text-generation - conversational language: - fa size_categories: - 1K<n<10K --- # Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model Persian ChatBot dataset, fine-tune LLaMa on instructed data (preprocessed alpaca dataset). [GitHub](https://github.com/AliEdalat/ChatBot_for_persian_LLaMA_fine_tune.git) - we use [preprocessed alpaca dataset](https://github.com/thisserand/alpaca-lora-finetune-language.git) as a dataset. we translate no_translate data to persian with [mt5](https://huggingface.co/persiannlp/mt5-large-parsinlu-translation_en_fa). ([train dataset](https://huggingface.co/datasets/AliEdalat/Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model/tree/main) and [test data](https://huggingface.co/datasets/AliEdalat/Persian_ChatBot_dataset_Fine_Tuning_Alpaca_Model/tree/main) with 2k example is ready) - we use LLaMA as a generative model for creating a chatbot model. we fine-tune the model with our Persian dataset and test it. - for improving ChatBot performance, replace "برای اینکه این کار را بکنم" with ""

Defetya/eval_open_llama_ru

--- license: apache-2.0 ---

SS3830/image-search-sa

--- license: apache-2.0 dataset_info: features: - name: image dtype: image - name: label dtype: string splits: - name: train num_bytes: 28874455.946 num_examples: 2378 download_size: 24014632 dataset_size: 28874455.946 configs: - config_name: default data_files: - split: train path: data/train-* ---

zolak/twitter_dataset_80_1713214788

--- 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: 1420630 num_examples: 3501 download_size: 719400 dataset_size: 1420630 configs: - config_name: default data_files: - split: train path: data/train-* ---

ikuldeep1/vehicle-damage-fraud-image-balanced

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 1716555477.626 num_examples: 12729 download_size: 1433374572 dataset_size: 1716555477.626 configs: - config_name: default data_files: - split: train path: data/train-* ---

autoevaluate/autoeval-eval-futin__feed-sen_vi-894567-2175669982

--- type: predictions tags: - autotrain - evaluation datasets: - futin/feed eval_info: task: text_zero_shot_classification model: facebook/opt-2.7b metrics: [] dataset_name: futin/feed dataset_config: sen_vi dataset_split: test col_mapping: text: text classes: classes target: target --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: facebook/opt-2.7b * Dataset: futin/feed * Config: sen_vi * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

chathuranga-jayanath/context-5-rhino-finmath-times4j-html-mavendoxia-wro4j-guava-supercsv-len-10000-prompt-1

--- dataset_info: features: - name: id dtype: int64 - name: filepath dtype: string - name: start_bug_line dtype: int64 - name: end_bug_line dtype: int64 - name: bug dtype: string - name: fix dtype: string - name: ctx dtype: string splits: - name: train num_bytes: 28232907 num_examples: 45517 - name: validation num_bytes: 3535186 num_examples: 5689 - name: test num_bytes: 3535341 num_examples: 5689 download_size: 14548547 dataset_size: 35303434 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

TecnicaLLM/dolly-15k

--- license: cc-by-sa-3.0 ---

projectbaraat/kannada-Mathematical

--- dataset_info: features: - name: input dtype: string - name: response dtype: string splits: - name: train num_bytes: 519631610 num_examples: 335690 download_size: 169797917 dataset_size: 519631610 configs: - config_name: default data_files: - split: train path: data/train-* ---

Yevhenii1234/test

--- license: apache-2.0 ---

presencesw/dataset_2000_complexquestion_3

--- dataset_info: features: - name: entities sequence: 'null' - name: triplets sequence: 'null' - name: answer dtype: string - name: complex_question dtype: string splits: - name: train num_bytes: 17911 num_examples: 200 download_size: 0 dataset_size: 17911 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dataset_2000_complexquestion_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Bluepelt/Idkwhattodomate

--- license: mit ---

connorhoehn/trading_card_display_classification_1_5k_v3

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': grid '1': solo '2': spread '3': stack splits: - name: train num_bytes: 1127230775.103 num_examples: 1249 - name: test num_bytes: 155934991.0 num_examples: 307 download_size: 1317201819 dataset_size: 1283165766.103 --- # Dataset Card for "trading_card_display_classification_1_5k_v3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

freshpearYoon/v3_train_free_concat_20

--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 3842535816 num_examples: 2500 download_size: 1826001643 dataset_size: 3842535816 configs: - config_name: default data_files: - split: train path: data/train-* ---

Falah/arabic_islamic_fashion_prompts_SDXL

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 239684574 num_examples: 1000000 download_size: 27020301 dataset_size: 239684574 --- # Dataset Card for "arabic_islamic_fashion_prompts_SDXL" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Indic-Benchmark/nepali-arc-c-2.5k

--- dataset_info: features: - name: id dtype: string - name: question struct: - name: choices list: - name: label dtype: string - name: text dtype: string - name: stem dtype: string - name: answerKey dtype: string splits: - name: train num_bytes: 1841172 num_examples: 2584 download_size: 706909 dataset_size: 1841172 configs: - config_name: default data_files: - split: train path: data/train-* ---

dot-ammar/AR-dotted-mediumPlus

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: clean dtype: string splits: - name: train num_bytes: 387187864 num_examples: 1625508 download_size: 214233397 dataset_size: 387187864 --- # Dataset Card for "AR-dotted-mediumPlus-arrow" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Thermostatic/parallel_corpus_webcrawl_english_spanish_1

--- license: cc-by-4.0 task_categories: - translation language: - en - es tags: - English - Spanish - Parallel corpus size_categories: - 10K<n<100K --- # Dataset Card for Dataset Name  This parallel corpus dataset contains about 21k rows of parallel English and Spanish texts obtained by crawling different websites. It has been filtered strictly. ## Dataset Details ### Dataset Description  This is a parallel corpus of bilingual texts crawled from multilingual websites, which contains 21, 005 TUs. A strict validation process has been followed, which resulted in discarding: - TUs from crawled websites that do not comply to the PSI directive, - TUs with more than 99% of mispelled tokens, - TUs identified during the manual validation process and all the TUs from websites which error rate in the sample extracted for manual validation is strictly above the following thresholds: 50% of TUs with language identification errors, 50% of TUs with alignment errors, 50% of TUs with tokenization errors, 20% of TUs identified as machine translated content, 50% of TUs with translation errors. - **Period of crawling:** 15/11/2016 - 23/01/2017 (DD/MM/YY). - **Curated by:** Directorate-General for Communications Networks, Content and Technology. - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** English & Spanish - **License:** cc-by-4.0 ### Dataset Sources [optional]  - **Repository:** http://data.europa.eu/88u/dataset/elrc_339 - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  This dataset is perfect for training Machine Translation algorithms. ### 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]

autoevaluate/autoeval-staging-eval-project-0b0f26eb-7664951

--- type: predictions tags: - autotrain - evaluation datasets: - lener_br eval_info: task: entity_extraction model: Luciano/bertimbau-large-lener_br metrics: [] dataset_name: lener_br dataset_config: lener_br dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: Luciano/bertimbau-large-lener_br * Dataset: lener_br To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model.

tasksource/sherliic

--- dataset_info: features: - name: premise dtype: string - name: hypothesis dtype: string - name: label dtype: string splits: - name: train num_bytes: 106784 num_examples: 996 - name: test num_bytes: 322932 num_examples: 2989 download_size: 146406 dataset_size: 429716 language: - en --- # Dataset Card for "sherliic" https://github.com/mnschmit/SherLIiC ``` @inproceedings{schmitt2019sherliic, title = "{S}her{LI}i{C}: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference", author = {Schmitt, Martin and Sch{\"u}tze, Hinrich}, booktitle = "Proceedings of the 57th Conference of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-1086", pages = "902--914" } ```