davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
youyu0105/llm-MIDI4	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 570535 num_examples: 335 download_size: 131987 dataset_size: 570535 --- # Dataset Card for "llm-MIDI4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/gr_mg36_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of gr_mg36/GrMG36/MG36 (Girls' Frontline) This is the dataset of gr_mg36/GrMG36/MG36 (Girls' Frontline), containing 26 images and their tags. The core tags of this character are `blue_eyes, blonde_hair, hair_ornament, bangs, long_hair, hair_over_one_eye, hairclip, breasts, ahoge, heterochromia, yellow_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:----------\|:------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 26 \| 32.14 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/gr_mg36_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 26 \| 16.90 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/gr_mg36_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 51 \| 30.39 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/gr_mg36_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 26 \| 26.96 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/gr_mg36_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 51 \| 45.71 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/gr_mg36_girlsfrontline/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/gr_mg36_girlsfrontline', 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 \| 26 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, looking_at_viewer, solo, black_gloves, closed_mouth, collarbone, white_background, bare_shoulders, fingerless_gloves, simple_background, socks, weapon \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| solo \| black_gloves \| closed_mouth \| collarbone \| white_background \| bare_shoulders \| fingerless_gloves \| simple_background \| socks \| weapon \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------\|:---------------\|:---------------\|:-------------\|:-------------------\|:-----------------\|:--------------------\|:--------------------\|:--------\|:---------\| \| 0 \| 26 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
japanese-asr/whisper_transcriptions.reazonspeech.all_17	--- dataset_info: config_name: all features: - name: name dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 30517145303.0 num_examples: 268223 download_size: 30279684051 dataset_size: 30517145303.0 configs: - config_name: all data_files: - split: train path: all/train-* ---
poorguys/TW-Kai_2_aoyagireisyosimo2_all_512	--- dataset_info: features: - name: char dtype: string - name: unicode dtype: string - name: images dtype: image - name: target_images dtype: image - name: stroke dtype: int32 - name: strokes_sequence sequence: int32 - name: components sequence: int32 - name: jyutping dtype: string splits: - name: train num_bytes: 438261774.25 num_examples: 6351 - name: test num_bytes: 2409980857.25 num_examples: 69931 download_size: 1959823420 dataset_size: 2848242631.5 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647	--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_0_bs_16 num_bytes: 84811910.125 num_examples: 5647 - name: fewshot_1_bs_16 num_bytes: 86719029.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 90542558.125 num_examples: 5647 - name: fewshot_5_bs_16 num_bytes: 94354619.125 num_examples: 5647 - name: fewshot_8_bs_16 num_bytes: 100058064.125 num_examples: 5647 download_size: 418819193 dataset_size: 456486180.625 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_CausalLM__7B	--- pretty_name: Evaluation run of CausalLM/7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CausalLM/7B](https://huggingface.co/CausalLM/7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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_CausalLM__7B_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-19T10:15:27.073071](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__7B_public/blob/main/results_2023-11-19T10-15-27.073071.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\": 0.6094831324044202,\n\ \ \"acc_stderr\": 0.0327856640395233,\n \"acc_norm\": 0.6180866854509012,\n\ \ \"acc_norm_stderr\": 0.03347186592408746,\n \"mc1\": 0.3537331701346389,\n\ \ \"mc1_stderr\": 0.016737814358846147,\n \"mc2\": 0.5012670346064317,\n\ \ \"mc2_stderr\": 0.015282424019072406,\n \"em\": 0.3381921140939597,\n\ \ \"em_stderr\": 0.0048449283464877275,\n \"f1\": 0.4114880453020153,\n\ \ \"f1_stderr\": 0.00471092648573539\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.47013651877133106,\n \"acc_stderr\": 0.014585305840007102,\n\ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.014611390804670088\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5603465445130452,\n\ \ \"acc_stderr\": 0.004953305461311753,\n \"acc_norm\": 0.7457677753435571,\n\ \ \"acc_norm_stderr\": 0.00434538861452003\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.5407407407407407,\n\ \ \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.5407407407407407,\n\ \ \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \ \ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.66,\n\ \ \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \ \ \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.02783491252754407,\n\ \ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.02783491252754407\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7361111111111112,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.56,\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\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7167630057803468,\n\ \ \"acc_stderr\": 0.034355680560478746,\n \"acc_norm\": 0.7167630057803468,\n\ \ \"acc_norm_stderr\": 0.034355680560478746\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n\ \ \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\ \ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.40350877192982454,\n\ \ \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n\ \ \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n\ \ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4444444444444444,\n \"acc_stderr\": 0.025591857761382175,\n \"\ acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.025591857761382175\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7516129032258064,\n\ \ \"acc_stderr\": 0.024580028921481003,\n \"acc_norm\": 0.7516129032258064,\n\ \ \"acc_norm_stderr\": 0.024580028921481003\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511657,\n\ \ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511657\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8181818181818182,\n \"acc_stderr\": 0.027479603010538808,\n \"\ acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.027479603010538808\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8549222797927462,\n \"acc_stderr\": 0.025416343096306443,\n\ \ \"acc_norm\": 0.8549222797927462,\n \"acc_norm_stderr\": 0.025416343096306443\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5974358974358974,\n \"acc_stderr\": 0.024864995159767755,\n\ \ \"acc_norm\": 0.5974358974358974,\n \"acc_norm_stderr\": 0.024864995159767755\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815635,\n \ \ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815635\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6008403361344538,\n \"acc_stderr\": 0.031811100324139266,\n\ \ \"acc_norm\": 0.6008403361344538,\n \"acc_norm_stderr\": 0.031811100324139266\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.039837983066598075,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.039837983066598075\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8220183486238533,\n \"acc_stderr\": 0.01639943636661291,\n \"\ acc_norm\": 0.8220183486238533,\n \"acc_norm_stderr\": 0.01639943636661291\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501947,\n \"\ acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501947\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \ \ \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6591928251121076,\n\ \ \"acc_stderr\": 0.0318114974705536,\n \"acc_norm\": 0.6591928251121076,\n\ \ \"acc_norm_stderr\": 0.0318114974705536\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.04010358942462203,\n\ \ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.04010358942462203\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\": 0.7314814814814815,\n\ \ \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n\ \ \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6932515337423313,\n \"acc_stderr\": 0.036230899157241474,\n\ \ \"acc_norm\": 0.6932515337423313,\n \"acc_norm_stderr\": 0.036230899157241474\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8376068376068376,\n\ \ \"acc_stderr\": 0.02416161812798774,\n \"acc_norm\": 0.8376068376068376,\n\ \ \"acc_norm_stderr\": 0.02416161812798774\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\": 0.80970625798212,\n\ \ \"acc_stderr\": 0.014036945850381387,\n \"acc_norm\": 0.80970625798212,\n\ \ \"acc_norm_stderr\": 0.014036945850381387\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6560693641618497,\n \"acc_stderr\": 0.025574123786546655,\n\ \ \"acc_norm\": 0.6560693641618497,\n \"acc_norm_stderr\": 0.025574123786546655\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.2759776536312849,\n\ \ \"acc_stderr\": 0.01495010300247536,\n \"acc_norm\": 0.2759776536312849,\n\ \ \"acc_norm_stderr\": 0.01495010300247536\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.026787453111906497,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.026787453111906497\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.684887459807074,\n\ \ \"acc_stderr\": 0.02638527370346449,\n \"acc_norm\": 0.684887459807074,\n\ \ \"acc_norm_stderr\": 0.02638527370346449\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.691358024691358,\n \"acc_stderr\": 0.025702640260603746,\n\ \ \"acc_norm\": 0.691358024691358,\n \"acc_norm_stderr\": 0.025702640260603746\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.4219858156028369,\n \"acc_stderr\": 0.029462189233370593,\n \ \ \"acc_norm\": 0.4219858156028369,\n \"acc_norm_stderr\": 0.029462189233370593\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4954367666232073,\n\ \ \"acc_stderr\": 0.012769704263117526,\n \"acc_norm\": 0.4954367666232073,\n\ \ \"acc_norm_stderr\": 0.012769704263117526\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6360294117647058,\n \"acc_stderr\": 0.02922719246003203,\n\ \ \"acc_norm\": 0.6360294117647058,\n \"acc_norm_stderr\": 0.02922719246003203\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6160130718954249,\n \"acc_stderr\": 0.01967580813528152,\n \ \ \"acc_norm\": 0.6160130718954249,\n \"acc_norm_stderr\": 0.01967580813528152\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\": 0.726530612244898,\n \"acc_stderr\": 0.02853556033712845,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.02853556033712845\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.02519692987482705,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.02519692987482705\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\": 0.46987951807228917,\n\ \ \"acc_stderr\": 0.03885425420866766,\n \"acc_norm\": 0.46987951807228917,\n\ \ \"acc_norm_stderr\": 0.03885425420866766\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.031267817146631786,\n\ \ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.031267817146631786\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3537331701346389,\n\ \ \"mc1_stderr\": 0.016737814358846147,\n \"mc2\": 0.5012670346064317,\n\ \ \"mc2_stderr\": 0.015282424019072406\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.696921862667719,\n \"acc_stderr\": 0.012916727462634458\n\ \ },\n \"harness\|drop\|3\": {\n \"em\": 0.3381921140939597,\n \ \ \"em_stderr\": 0.0048449283464877275,\n \"f1\": 0.4114880453020153,\n\ \ \"f1_stderr\": 0.00471092648573539\n },\n \"harness\|gsm8k\|5\": {\n\ \ \"acc\": 0.22971948445792267,\n \"acc_stderr\": 0.011586857544997503\n\ \ }\n}\n```" repo_url: https://huggingface.co/CausalLM/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: 2023_11_19T10_15_27.073071 path: - '/details_harness\|arc:challenge\|25_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-11-19T10-15-27.073071.parquet' - config_name: harness_drop_3 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|drop\|3_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-11-19T10-15-27.073071.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|gsm8k\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hellaswag\|10_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-11-19T10-15-27.073071.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-management\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-virology\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|truthfulqa:mc\|0_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-11-19T10-15-27.073071.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '/details_harness\|winogrande\|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-11-19T10-15-27.073071.parquet' - config_name: results data_files: - split: 2023_11_19T10_15_27.073071 path: - results_2023-11-19T10-15-27.073071.parquet - split: latest path: - results_2023-11-19T10-15-27.073071.parquet --- # Dataset Card for Evaluation run of CausalLM/7B ## Dataset Description - Homepage: - Repository: https://huggingface.co/CausalLM/7B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [CausalLM/7B](https://huggingface.co/CausalLM/7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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_CausalLM__7B_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-19T10:15:27.073071](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__7B_public/blob/main/results_2023-11-19T10-15-27.073071.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": 0.6094831324044202, "acc_stderr": 0.0327856640395233, "acc_norm": 0.6180866854509012, "acc_norm_stderr": 0.03347186592408746, "mc1": 0.3537331701346389, "mc1_stderr": 0.016737814358846147, "mc2": 0.5012670346064317, "mc2_stderr": 0.015282424019072406, "em": 0.3381921140939597, "em_stderr": 0.0048449283464877275, "f1": 0.4114880453020153, "f1_stderr": 0.00471092648573539 }, "harness\|arc:challenge\|25": { "acc": 0.47013651877133106, "acc_stderr": 0.014585305840007102, "acc_norm": 0.5, "acc_norm_stderr": 0.014611390804670088 }, "harness\|hellaswag\|10": { "acc": 0.5603465445130452, "acc_stderr": 0.004953305461311753, "acc_norm": 0.7457677753435571, "acc_norm_stderr": 0.00434538861452003 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5407407407407407, "acc_stderr": 0.04304979692464242, "acc_norm": 0.5407407407407407, "acc_norm_stderr": 0.04304979692464242 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7132075471698113, "acc_stderr": 0.02783491252754407, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.02783491252754407 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7361111111111112, "acc_stderr": 0.03685651095897532, "acc_norm": 0.7361111111111112, "acc_norm_stderr": 0.03685651095897532 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7167630057803468, "acc_stderr": 0.034355680560478746, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.034355680560478746 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.73, "acc_stderr": 0.04461960433384739, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384739 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5361702127659574, "acc_stderr": 0.032600385118357715, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.032600385118357715 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.40350877192982454, "acc_stderr": 0.046151869625837026, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.046151869625837026 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5241379310344828, "acc_stderr": 0.0416180850350153, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.0416180850350153 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.025591857761382175, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.025591857761382175 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04444444444444449, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04444444444444449 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7516129032258064, "acc_stderr": 0.024580028921481003, "acc_norm": 0.7516129032258064, "acc_norm_stderr": 0.024580028921481003 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511657, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511657 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009182, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009182 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8181818181818182, "acc_stderr": 0.027479603010538808, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.027479603010538808 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8549222797927462, "acc_stderr": 0.025416343096306443, "acc_norm": 0.8549222797927462, "acc_norm_stderr": 0.025416343096306443 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5974358974358974, "acc_stderr": 0.024864995159767755, "acc_norm": 0.5974358974358974, "acc_norm_stderr": 0.024864995159767755 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3074074074074074, "acc_stderr": 0.028133252578815635, "acc_norm": 0.3074074074074074, "acc_norm_stderr": 0.028133252578815635 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6008403361344538, "acc_stderr": 0.031811100324139266, "acc_norm": 0.6008403361344538, "acc_norm_stderr": 0.031811100324139266 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.39072847682119205, "acc_stderr": 0.039837983066598075, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.039837983066598075 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8220183486238533, "acc_stderr": 0.01639943636661291, "acc_norm": 0.8220183486238533, "acc_norm_stderr": 0.01639943636661291 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7549019607843137, "acc_stderr": 0.030190282453501947, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.030190282453501947 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7763713080168776, "acc_stderr": 0.027123298205229966, "acc_norm": 0.7763713080168776, "acc_norm_stderr": 0.027123298205229966 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6591928251121076, "acc_stderr": 0.0318114974705536, "acc_norm": 0.6591928251121076, "acc_norm_stderr": 0.0318114974705536 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7022900763358778, "acc_stderr": 0.04010358942462203, "acc_norm": 0.7022900763358778, "acc_norm_stderr": 0.04010358942462203 }, "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": 0.7314814814814815, "acc_stderr": 0.042844679680521934, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.042844679680521934 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6932515337423313, "acc_stderr": 0.036230899157241474, "acc_norm": 0.6932515337423313, "acc_norm_stderr": 0.036230899157241474 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8376068376068376, "acc_stderr": 0.02416161812798774, "acc_norm": 0.8376068376068376, "acc_norm_stderr": 0.02416161812798774 }, "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": 0.80970625798212, "acc_stderr": 0.014036945850381387, "acc_norm": 0.80970625798212, "acc_norm_stderr": 0.014036945850381387 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6560693641618497, "acc_stderr": 0.025574123786546655, "acc_norm": 0.6560693641618497, "acc_norm_stderr": 0.025574123786546655 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2759776536312849, "acc_stderr": 0.01495010300247536, "acc_norm": 0.2759776536312849, "acc_norm_stderr": 0.01495010300247536 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.026787453111906497, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.026787453111906497 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.684887459807074, "acc_stderr": 0.02638527370346449, "acc_norm": 0.684887459807074, "acc_norm_stderr": 0.02638527370346449 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.691358024691358, "acc_stderr": 0.025702640260603746, "acc_norm": 0.691358024691358, "acc_norm_stderr": 0.025702640260603746 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4219858156028369, "acc_stderr": 0.029462189233370593, "acc_norm": 0.4219858156028369, "acc_norm_stderr": 0.029462189233370593 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4954367666232073, "acc_stderr": 0.012769704263117526, "acc_norm": 0.4954367666232073, "acc_norm_stderr": 0.012769704263117526 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6360294117647058, "acc_stderr": 0.02922719246003203, "acc_norm": 0.6360294117647058, "acc_norm_stderr": 0.02922719246003203 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6160130718954249, "acc_stderr": 0.01967580813528152, "acc_norm": 0.6160130718954249, "acc_norm_stderr": 0.01967580813528152 }, "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": 0.726530612244898, "acc_stderr": 0.02853556033712845, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.02853556033712845 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482705, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482705 }, "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": 0.46987951807228917, "acc_stderr": 0.03885425420866766, "acc_norm": 0.46987951807228917, "acc_norm_stderr": 0.03885425420866766 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7894736842105263, "acc_stderr": 0.031267817146631786, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.031267817146631786 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3537331701346389, "mc1_stderr": 0.016737814358846147, "mc2": 0.5012670346064317, "mc2_stderr": 0.015282424019072406 }, "harness\|winogrande\|5": { "acc": 0.696921862667719, "acc_stderr": 0.012916727462634458 }, "harness\|drop\|3": { "em": 0.3381921140939597, "em_stderr": 0.0048449283464877275, "f1": 0.4114880453020153, "f1_stderr": 0.00471092648573539 }, "harness\|gsm8k\|5": { "acc": 0.22971948445792267, "acc_stderr": 0.011586857544997503 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
ewhfef/mix_cpt	--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 426850969 num_examples: 29635 download_size: 195835115 dataset_size: 426850969 configs: - config_name: default data_files: - split: train path: data/train-* --- A mix of part of datasets: pubmed, pubmed_qa and alpaca
Otherwa/GenAi-Public-Response	--- license: openrail language: - en tags: - code - legal - finance - biology - chemistry - music - art - medical - climate size_categories: - n<1K ---
tyzhu/squad_qa_no_id_v5_full_recite_ans_sent_no_permute_rerun	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 7992127.884496851 num_examples: 4778 - name: validation num_bytes: 402971 num_examples: 300 download_size: 1428626 dataset_size: 8395098.88449685 --- # Dataset Card for "squad_qa_no_id_v5_full_recite_ans_sent_no_permute_rerun" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
irds/car_v1.5_trec-y1_manual	--- pretty_name: '`car/v1.5/trec-y1/manual`' viewer: false source_datasets: ['irds/car_v1.5'] task_categories: - text-retrieval --- # Dataset Card for `car/v1.5/trec-y1/manual` The `car/v1.5/trec-y1/manual` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/car#car/v1.5/trec-y1/manual). # Data This dataset provides: - `qrels`: (relevance assessments); count=29,571 - For `docs`, use [`irds/car_v1.5`](https://huggingface.co/datasets/irds/car_v1.5) ## Usage ```python from datasets import load_dataset qrels = load_dataset('irds/car_v1.5_trec-y1_manual', '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 ``` @inproceedings{Dietz2017TrecCar, title={TREC Complex Answer Retrieval Overview.}, author={Dietz, Laura and Verma, Manisha and Radlinski, Filip and Craswell, Nick}, booktitle={TREC}, year={2017} } @article{Dietz2017Car, title={{TREC CAR}: A Data Set for Complex Answer Retrieval}, author={Laura Dietz and Ben Gamari}, year={2017}, note={Version 1.5}, url={http://trec-car.cs.unh.edu} } ```
vigneshgs7/Boundary_detection_Doc_2	--- dataset_info: features: - name: name dtype: string - name: uuid dtype: string - name: status dtype: string - name: image dtype: image - name: label.annotations list: - name: id dtype: int32 - name: category_id dtype: int32 - name: label.segmentation_bitmap dtype: image splits: - name: train num_bytes: 4375553760.0 num_examples: 88 download_size: 286343850 dataset_size: 4375553760.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
qubitbucket/embeddings-tutorial	--- license: apache-2.0 ---
Nxrd/michaoficial	--- license: openrail ---
fairlabs/aihub-nmt-dataset-2022-07	--- dataset_info: features: - name: en dtype: string - name: ko dtype: string splits: - name: train num_bytes: 447177389 num_examples: 1200144 - name: validation num_bytes: 44752356.769347675 num_examples: 120018 - name: test num_bytes: 11186411.230652321 num_examples: 30000 download_size: 326090070 dataset_size: 503116157.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
warzin/covers	--- license: other license_name: seila license_link: LICENSE ---
sushvij/generativeaisample	--- license: openrail language: - en pretty_name: gai ---
heliosprime/twitter_dataset_1713203122	--- 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: 28747 num_examples: 78 download_size: 23120 dataset_size: 28747 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713203122" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nakkhatra/trial_bn	--- license: cc0-1.0 ---
ylacombe/librispeech_asr_tags	--- dataset_info: - config_name: clean features: - name: file dtype: string - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: float64 - name: phonemes dtype: string - name: gender dtype: string splits: - name: train.100 num_bytes: 17998991 num_examples: 28539 - name: train.360 num_bytes: 65429327 num_examples: 104014 - name: validation num_bytes: 1238969 num_examples: 2703 - name: test num_bytes: 1205066 num_examples: 2620 download_size: 40197691 dataset_size: 85872353 - config_name: other features: - name: file dtype: string - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: float64 - name: phonemes dtype: string splits: - name: train.500 num_bytes: 87768115 num_examples: 148688 - name: validation num_bytes: 1196395 num_examples: 2864 - name: test num_bytes: 1228421 num_examples: 2939 download_size: 42452591 dataset_size: 90192931 configs: - config_name: clean data_files: - split: train.100 path: clean/train.100-* - split: train.360 path: clean/train.360-* - split: validation path: clean/validation-* - split: test path: clean/test-* - config_name: other data_files: - split: train.500 path: other/train.500-* - split: validation path: other/validation-* - split: test path: other/test-* ---
Prince3069/Speedbolt	--- license: apache-2.0 ---
alpayariyak/SkunkData-Corpus-Clusters	--- configs: - config_name: default data_files: - split: config32 path: data/config32-* 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 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: config32 num_bytes: 35728713 num_examples: 58432 download_size: 14314061 dataset_size: 35728713 --- # Dataset Card for "SkunkData-Corpus-Clusters" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_liminerity__Blur-7b-v1.2	--- pretty_name: Evaluation run of liminerity/Blur-7b-v1.2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [liminerity/Blur-7b-v1.2](https://huggingface.co/liminerity/Blur-7b-v1.2) 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_liminerity__Blur-7b-v1.2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-18T13:00:27.961191](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__Blur-7b-v1.2/blob/main/results_2024-01-18T13-00-27.961191.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\": 0.6357129950975389,\n\ \ \"acc_stderr\": 0.03262066192131251,\n \"acc_norm\": 0.6382762311799055,\n\ \ \"acc_norm_stderr\": 0.03328259277014658,\n \"mc1\": 0.43451652386780903,\n\ \ \"mc1_stderr\": 0.017352738749259564,\n \"mc2\": 0.6030326315591199,\n\ \ \"mc2_stderr\": 0.015260409379504259\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6254266211604096,\n \"acc_stderr\": 0.01414419347189345,\n\ \ \"acc_norm\": 0.6535836177474402,\n \"acc_norm_stderr\": 0.013905011180063223\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6528579964150567,\n\ \ \"acc_stderr\": 0.00475088440109516,\n \"acc_norm\": 0.8387771360286795,\n\ \ \"acc_norm_stderr\": 0.0036698484004877773\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5851851851851851,\n\ \ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\ \ \"acc_norm_stderr\": 0.04256193767901408\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\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\ \ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7013888888888888,\n\ \ \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n\ \ \"acc_norm_stderr\": 0.03827052357950756\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\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\"\ : 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\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\": 0.6184971098265896,\n\ \ \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n\ \ \"acc_norm_stderr\": 0.03703851193099521\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\":\ \ 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\ \ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.45614035087719296,\n\ \ \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.45614035087719296,\n\ \ \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n\ \ \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778405,\n \"\ acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778405\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.40476190476190477,\n\ \ \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.40476190476190477,\n\ \ \"acc_norm_stderr\": 0.04390259265377562\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7612903225806451,\n\ \ \"acc_stderr\": 0.02425107126220884,\n \"acc_norm\": 0.7612903225806451,\n\ \ \"acc_norm_stderr\": 0.02425107126220884\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.541871921182266,\n \"acc_stderr\": 0.03505630140785741,\n\ \ \"acc_norm\": 0.541871921182266,\n \"acc_norm_stderr\": 0.03505630140785741\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\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\"\ : 0.803030303030303,\n \"acc_stderr\": 0.02833560973246336,\n \"acc_norm\"\ : 0.803030303030303,\n \"acc_norm_stderr\": 0.02833560973246336\n },\n\ \ \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \ \ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758723,\n\ \ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758723\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6512820512820513,\n \"acc_stderr\": 0.02416278028401772,\n \ \ \"acc_norm\": 0.6512820512820513,\n \"acc_norm_stderr\": 0.02416278028401772\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \ \ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.02995382389188704,\n \ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.02995382389188704\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\ acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8091743119266055,\n \"acc_stderr\": 0.016847676400091095,\n \"\ acc_norm\": 0.8091743119266055,\n \"acc_norm_stderr\": 0.016847676400091095\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"\ acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7679324894514767,\n \"acc_stderr\": 0.027479744550808514,\n \ \ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.027479744550808514\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\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\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7361963190184049,\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\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.023086635086841403,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.023086635086841403\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\": 0.8186462324393359,\n\ \ \"acc_stderr\": 0.013778693778464073,\n \"acc_norm\": 0.8186462324393359,\n\ \ \"acc_norm_stderr\": 0.013778693778464073\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.0246853168672578,\n\ \ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.0246853168672578\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4782122905027933,\n\ \ \"acc_stderr\": 0.016706617522176136,\n \"acc_norm\": 0.4782122905027933,\n\ \ \"acc_norm_stderr\": 0.016706617522176136\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.02609016250427905,\n\ \ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.02609016250427905\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7006172839506173,\n \"acc_stderr\": 0.025483115601195448,\n\ \ \"acc_norm\": 0.7006172839506173,\n \"acc_norm_stderr\": 0.025483115601195448\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.43617021276595747,\n \"acc_stderr\": 0.02958345203628407,\n \ \ \"acc_norm\": 0.43617021276595747,\n \"acc_norm_stderr\": 0.02958345203628407\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4556714471968709,\n\ \ \"acc_stderr\": 0.012719949543032207,\n \"acc_norm\": 0.4556714471968709,\n\ \ \"acc_norm_stderr\": 0.012719949543032207\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.0290294228156814,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.0290294228156814\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6290849673202614,\n \"acc_stderr\": 0.019542101564854128,\n \ \ \"acc_norm\": 0.6290849673202614,\n \"acc_norm_stderr\": 0.019542101564854128\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.02796678585916089,\n\ \ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.02796678585916089\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.43451652386780903,\n\ \ \"mc1_stderr\": 0.017352738749259564,\n \"mc2\": 0.6030326315591199,\n\ \ \"mc2_stderr\": 0.015260409379504259\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8058405682715075,\n \"acc_stderr\": 0.01111698339239267\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5284306292645944,\n \ \ \"acc_stderr\": 0.013750202076584422\n }\n}\n```" repo_url: https://huggingface.co/liminerity/Blur-7b-v1.2 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_18T13_00_27.961191 path: - '/details_harness\|arc:challenge\|25_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-18T13-00-27.961191.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|gsm8k\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hellaswag\|10_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-18T13-00-27.961191.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-18T13-00-27.961191.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '/details_harness\|winogrande\|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-18T13-00-27.961191.parquet' - config_name: results data_files: - split: 2024_01_18T13_00_27.961191 path: - results_2024-01-18T13-00-27.961191.parquet - split: latest path: - results_2024-01-18T13-00-27.961191.parquet --- # Dataset Card for Evaluation run of liminerity/Blur-7b-v1.2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [liminerity/Blur-7b-v1.2](https://huggingface.co/liminerity/Blur-7b-v1.2) 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_liminerity__Blur-7b-v1.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-18T13:00:27.961191](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__Blur-7b-v1.2/blob/main/results_2024-01-18T13-00-27.961191.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": 0.6357129950975389, "acc_stderr": 0.03262066192131251, "acc_norm": 0.6382762311799055, "acc_norm_stderr": 0.03328259277014658, "mc1": 0.43451652386780903, "mc1_stderr": 0.017352738749259564, "mc2": 0.6030326315591199, "mc2_stderr": 0.015260409379504259 }, "harness\|arc:challenge\|25": { "acc": 0.6254266211604096, "acc_stderr": 0.01414419347189345, "acc_norm": 0.6535836177474402, "acc_norm_stderr": 0.013905011180063223 }, "harness\|hellaswag\|10": { "acc": 0.6528579964150567, "acc_stderr": 0.00475088440109516, "acc_norm": 0.8387771360286795, "acc_norm_stderr": 0.0036698484004877773 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "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": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7056603773584905, "acc_stderr": 0.02804918631569525, "acc_norm": 0.7056603773584905, "acc_norm_stderr": 0.02804918631569525 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7013888888888888, "acc_stderr": 0.03827052357950756, "acc_norm": 0.7013888888888888, "acc_norm_stderr": 0.03827052357950756 }, "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": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "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": 0.6184971098265896, "acc_stderr": 0.03703851193099521, "acc_norm": 0.6184971098265896, "acc_norm_stderr": 0.03703851193099521 }, "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": 0.74, "acc_stderr": 0.044084400227680794, "acc_norm": 0.74, "acc_norm_stderr": 0.044084400227680794 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5702127659574469, "acc_stderr": 0.03236214467715564, "acc_norm": 0.5702127659574469, "acc_norm_stderr": 0.03236214467715564 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.45614035087719296, "acc_stderr": 0.046854730419077895, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.046854730419077895 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.593103448275862, "acc_stderr": 0.04093793981266236, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266236 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.41534391534391535, "acc_stderr": 0.025379524910778405, "acc_norm": 0.41534391534391535, "acc_norm_stderr": 0.025379524910778405 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.40476190476190477, "acc_stderr": 0.04390259265377562, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.04390259265377562 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7612903225806451, "acc_stderr": 0.02425107126220884, "acc_norm": 0.7612903225806451, "acc_norm_stderr": 0.02425107126220884 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.541871921182266, "acc_stderr": 0.03505630140785741, "acc_norm": 0.541871921182266, "acc_norm_stderr": 0.03505630140785741 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "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": 0.803030303030303, "acc_stderr": 0.02833560973246336, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.02833560973246336 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8704663212435233, "acc_stderr": 0.024233532297758723, "acc_norm": 0.8704663212435233, "acc_norm_stderr": 0.024233532297758723 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6512820512820513, "acc_stderr": 0.02416278028401772, "acc_norm": 0.6512820512820513, "acc_norm_stderr": 0.02416278028401772 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3592592592592593, "acc_stderr": 0.029252905927251972, "acc_norm": 0.3592592592592593, "acc_norm_stderr": 0.029252905927251972 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6932773109243697, "acc_stderr": 0.02995382389188704, "acc_norm": 0.6932773109243697, "acc_norm_stderr": 0.02995382389188704 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.37748344370860926, "acc_stderr": 0.0395802723112157, "acc_norm": 0.37748344370860926, "acc_norm_stderr": 0.0395802723112157 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8091743119266055, "acc_stderr": 0.016847676400091095, "acc_norm": 0.8091743119266055, "acc_norm_stderr": 0.016847676400091095 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5231481481481481, "acc_stderr": 0.03406315360711507, "acc_norm": 0.5231481481481481, "acc_norm_stderr": 0.03406315360711507 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7892156862745098, "acc_stderr": 0.028626547912437406, "acc_norm": 0.7892156862745098, "acc_norm_stderr": 0.028626547912437406 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7679324894514767, "acc_stderr": 0.027479744550808514, "acc_norm": 0.7679324894514767, "acc_norm_stderr": 0.027479744550808514 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6771300448430493, "acc_stderr": 0.031381476375754995, "acc_norm": 0.6771300448430493, "acc_norm_stderr": 0.031381476375754995 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7404580152671756, "acc_stderr": 0.03844876139785271, "acc_norm": 0.7404580152671756, "acc_norm_stderr": 0.03844876139785271 }, "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": 0.7407407407407407, "acc_stderr": 0.04236511258094633, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.04236511258094633 }, "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": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "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": 0.8547008547008547, "acc_stderr": 0.023086635086841403, "acc_norm": 0.8547008547008547, "acc_norm_stderr": 0.023086635086841403 }, "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": 0.8186462324393359, "acc_stderr": 0.013778693778464073, "acc_norm": 0.8186462324393359, "acc_norm_stderr": 0.013778693778464073 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6994219653179191, "acc_stderr": 0.0246853168672578, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.0246853168672578 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4782122905027933, "acc_stderr": 0.016706617522176136, "acc_norm": 0.4782122905027933, "acc_norm_stderr": 0.016706617522176136 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7058823529411765, "acc_stderr": 0.02609016250427905, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.02609016250427905 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.707395498392283, "acc_stderr": 0.02583989833487798, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.02583989833487798 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7006172839506173, "acc_stderr": 0.025483115601195448, "acc_norm": 0.7006172839506173, "acc_norm_stderr": 0.025483115601195448 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.43617021276595747, "acc_stderr": 0.02958345203628407, "acc_norm": 0.43617021276595747, "acc_norm_stderr": 0.02958345203628407 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4556714471968709, "acc_stderr": 0.012719949543032207, "acc_norm": 0.4556714471968709, "acc_norm_stderr": 0.012719949543032207 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6470588235294118, "acc_stderr": 0.0290294228156814, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.0290294228156814 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6290849673202614, "acc_stderr": 0.019542101564854128, "acc_norm": 0.6290849673202614, "acc_norm_stderr": 0.019542101564854128 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7142857142857143, "acc_stderr": 0.0289205832206756, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.0289205832206756 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8421052631578947, "acc_stderr": 0.02796678585916089, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.02796678585916089 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.43451652386780903, "mc1_stderr": 0.017352738749259564, "mc2": 0.6030326315591199, "mc2_stderr": 0.015260409379504259 }, "harness\|winogrande\|5": { "acc": 0.8058405682715075, "acc_stderr": 0.01111698339239267 }, "harness\|gsm8k\|5": { "acc": 0.5284306292645944, "acc_stderr": 0.013750202076584422 } } ``` ## 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]
pphuc25/bailamvan	--- dataset_info: features: - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 9514569 num_examples: 888 download_size: 4680823 dataset_size: 9514569 --- # Dataset Card for "bailamvan" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hecgo067/adv-ele	--- dataset_info: features: - name: ADV dtype: string - name: ELE dtype: string splits: - name: train num_bytes: 430918.56140350876 num_examples: 1732 - name: test num_bytes: 107978.43859649122 num_examples: 434 download_size: 296740 dataset_size: 538897.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
pdxmusic/splats	--- license: apache-2.0 ---
luizapzbn/goodtriever-data	--- license: apache-2.0 --- # Goodtriever This repository contains datasets and model generations from the `Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models` paper, published as a conference paper on EMNLP 2023. [[Paper]]()[[Code]]()[[Data]](https://huggingface.co/datasets/luizapzbn/goodtriever-data) - `data`: prompts and datasets used for datastore creation. - `continual_mitigation`: clustered WILDS data and prompts - `datastore_quality`: for the experiments on how automatic labeling impacts mitigation results - `jigsaw`: main dataset, jigsaw unintended bias - `outputs`: model generations and results for all experiments from the paper. - `alpha_temperature` - `datastore_quality` - `datastore_size` - `k_neighbors` - `model_families` (and main table results) # Citation
roborovski/phi-2-labeled	--- dataset_info: features: - name: hexsha dtype: string - name: size dtype: int64 - name: ext dtype: string - name: lang dtype: string - name: max_stars_repo_path dtype: string - name: max_stars_repo_name dtype: string - name: max_stars_repo_head_hexsha dtype: string - name: max_stars_repo_licenses sequence: string - name: max_stars_count dtype: int64 - name: max_stars_repo_stars_event_min_datetime dtype: string - name: max_stars_repo_stars_event_max_datetime dtype: string - name: max_issues_repo_path dtype: string - name: max_issues_repo_name dtype: string - name: max_issues_repo_head_hexsha dtype: string - name: max_issues_repo_licenses sequence: string - name: max_issues_count dtype: int64 - name: max_issues_repo_issues_event_min_datetime dtype: string - name: max_issues_repo_issues_event_max_datetime dtype: string - name: max_forks_repo_path dtype: string - name: max_forks_repo_name dtype: string - name: max_forks_repo_head_hexsha dtype: string - name: max_forks_repo_licenses sequence: string - name: max_forks_count dtype: int64 - name: max_forks_repo_forks_event_min_datetime dtype: string - name: max_forks_repo_forks_event_max_datetime dtype: string - name: content dtype: string - name: avg_line_length dtype: float64 - name: max_line_length dtype: int64 - name: alphanum_fraction dtype: float64 - name: label dtype: int64 - name: cost dtype: float64 splits: - name: train num_bytes: 283814677 num_examples: 50000 download_size: 112938830 dataset_size: 283814677 --- # Dataset Card for "phi-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
djfelipe/cava	--- license: openrail ---
Nexdata/4001_People_Single_Object_Multi_view_Tracking_Data	--- license: cc-by-nc-nd-4.0 --- ## Description 4,001 People Single Object Multi-view Tracking Data, the data collection site includes indoor and outdoor scenes (such as supermarket, mall and community, etc.) , where each subject appeared in at least 7 cameras. The data diversity includes different ages, different time periods, different cameras, different human body orientations and postures, different collecting scenes. It can be used for computer vision tasks such as object detection and object tracking in multi-view scenes. For more details, please refer to the link: https://www.nexdata.ai/dataset/1231?source=Huggingface ## Data size 4,001 people, about 385-2,779 images per person ## Race distribution Asian ## Gender distribution 2,052 males, 1,949 females ## Age distribution from children to the elderly ## Collecting environment including indoor and outdoor scenes (such as supermarket, mall and community, etc.) ## Data diversity different ages, different time periods, different cameras, different human body orientations and postures, different collecting scenes ## Device surveillance cameras, the image resolution is not less 1,920*1,080 ## Data format the image data format is .jpg, the annotation file format is .json ## Annotation content human body rectangular bounding boxes ## Accuracy A rectangular bounding box of human body is qualified when the deviation is not more than 3 pixels, and the qualified rate of the bounding boxes shall not be lower than 97% # Licensing Information Commercial License
Loie/VGGSound	--- task_categories: - audio-classification size_categories: - 100B<n<1T --- # VGGSound VGG-Sound is an audio-visual correspondent dataset consisting of short clips of audio sounds, extracted from videos uploaded to YouTube. - Homepage: https://www.robots.ox.ac.uk/~vgg/data/vggsound/ - Paper: https://arxiv.org/abs/2004.14368 - Github: https://github.com/hche11/VGGSound ## Analysis - 310+ classes: VGG-Sound contains audios spanning a large number of challenging acoustic environments and noise characteristics of real applications. - 200,000+ videos: All videos are captured "in the wild" with audio-visual correspondence in the sense that the sound source is visually evident. - 550+ hours: VGG-Sound consists of both audio and video. Each segment is 10 seconds long. ![](src/data.png) ## Download We provide a csv file. For each YouTube video, we provide YouTube URLs, time stamps, audio labels and train/test split. Each line in the csv file has columns defined by here. ``` # YouTube ID, start seconds, label, train/test split. ``` And you can download VGGSound directly from this [repository](https://huggingface.co/datasets/Loie/VGGSound/tree/main). ## License The VGG-Sound dataset is available to download for commercial/research purposes under a Creative Commons Attribution 4.0 International License. The copyright remains with the original owners of the video. A complete version of the license can be found [here](https://thor.robots.ox.ac.uk/datasets/vggsound/license_vggsound.txt). ## Citation Please cite the following if you make use of the dataset. ``` @InProceedings{Chen20, author = "Honglie Chen and Weidi Xie and Andrea Vedaldi and Andrew Zisserman", title = "VGGSound: A Large-scale Audio-Visual Dataset", booktitle = "International Conference on Acoustics, Speech, and Signal Processing (ICASSP)", year = "2020", } ```
pacovaldez/stackoverflow-questions-2016	--- annotations_creators: - machine-generated language: - en language_creators: - found license: - apache-2.0 multilinguality: - monolingual pretty_name: stackoverflow_post_questions size_categories: - 1M<n<10M source_datasets: - original tags: - stackoverflow - technical questions task_categories: - text-classification task_ids: - multi-class-classification --- # Dataset Card for [Stackoverflow Post Questions] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Source Data](#source-data) - [Contributions](#contributions) ## Dataset Description Companies that sell Open-source software tools usually hire an army of Customer representatives to try to answer every question asked about their tool. The first step in this process is the prioritization of the question. The classification scale usually consists of 4 values, P0, P1, P2, and P3, with different meanings across every participant in the industry. On the other hand, every software developer in the world has dealt with Stack Overflow (SO); the amount of shared knowledge there is incomparable to any other website. Questions in SO are usually annotated and curated by thousands of people, providing metadata about the quality of the question. This dataset aims to provide an accurate prioritization for programming questions. ### Dataset Summary The dataset contains the title and body of stackoverflow questions and a label value(0,1,2,3) that was calculated using thresholds defined by SO badges. ### Languages English ## Dataset Structure title: string, body: string, label: int ### Data Splits The split is 40/40/20, where classes have been balaned to be around the same size. ## Dataset Creation The data set was extracted and labeled with the following query in BigQuery: ``` SELECT title, body, CASE WHEN score >= 100 OR favorite_count >= 100 OR view_count >= 10000 THEN 0 WHEN score >= 25 OR favorite_count >= 25 OR view_count >= 2500 THEN 1 WHEN score >= 10 OR favorite_count >= 10 OR view_count >= 1000 THEN 2 ELSE 3 END AS label FROM `bigquery-public-data`.stackoverflow.posts_questions ``` ### Source Data The data was extracted from the Big Query public dataset: `bigquery-public-data.stackoverflow.posts_questions` #### Initial Data Collection and Normalization The original dataset contained high class imbalance: label count 0 977424 1 2401534 2 3418179 3 16222990 Grand Total 23020127 The data was sampled from each class to have around the same amount of records on every class. ### Contributions Thanks to [@pacofvf](https://github.com/pacofvf) for adding this dataset.
youssef101/artelingo	--- license: other task_categories: - text-generation - text-classification - image-classification - image-to-text - text-to-image language: - en - ar - zh tags: - art - Affective Captioning - Emotions - Emotion Prediction - Image Captioning - Multilingual - Cultural - Diversity pretty_name: ArtELingo size_categories: - 10K<n<100K - 100K<n<1M - 1M<n<10M multilinguality: - multilingual source_datasets: - original --- # Dataset Card for "ArtELingo" ## 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) - [Dataset Configurations](#dataset-configurations) - [Data Fields](#data-fields) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [artelingo.org/](https://www.artelingo.org/) - Repository: [More Information Needed](https://github.com/Vision-CAIR/artelingo) - Paper: [More Information Needed](https://arxiv.org/abs/2211.10780) - Point of Contact: [More Information Needed](artelingo.dataset@gmail.com) ### Dataset Summary ArtELingo is a benchmark and dataset introduced in a research paper aimed at promoting work on diversity across languages and cultures. It is an extension of ArtEmis, which is a collection of 80,000 artworks from WikiArt with 450,000 emotion labels and English-only captions. ArtELingo expands this dataset by adding 790,000 annotations in Arabic and Chinese. The purpose of these additional annotations is to evaluate the performance of "cultural-transfer" in AI systems. The goal of ArtELingo is to encourage research on multilinguality and culturally-aware AI. By including annotations in multiple languages and considering cultural differences, the dataset aims to build more human-compatible AI that is sensitive to emotional nuances across various cultural contexts. The researchers believe that studying emotions in this way is crucial to understanding a significant aspect of human intelligence. ### Supported Tasks and Leaderboards We have two tasks: - [Emotion Label Prediction](https://eval.ai/web/challenges/challenge-page/2106/overview) - [Affective Image Captioning](https://eval.ai/web/challenges/challenge-page/2104/overview) Both challenges have a leaderboard on Eval.ai. Submission deadlines can be viewed from the above links. In addition, we are hosting the challenge at the ICCV23 workshop [WECIA](https://iccv23-wecia.github.io/). We have cash prizes for winners. ### Languages We have 3 languages: English, Arabic, and Chinese. For each image, we have at least 5 captions in each language. In total we have 80,000 images which are downloaded automatically with the dataset. ## Dataset Structure We show detailed information for all the configurations of the dataset. ### Dataset Configurations We have 4 Configurations: #### artelingo - Size of downloaded dataset files: 23 GB - Splits: \['train', 'test', 'val'\] - Number of Samples per splits: \[920K, 94.1K, 46.9K\] - Loading Script: ```python from datasets import load_dataset dataset = load_dataset(path="youssef101/artelingo", name='artelingo') ``` you can also provide a `splits:LIST(str)` parameter to avoid downloading the huge files for all the splits. (especially the train set :)) ```python from datasets import load_dataset dataset = load_dataset(path="youssef101/artelingo", name='artelingo', splits=['val']) ``` Notice that this deems the next dev configuration redundant. #### dev - Size of downloaded dataset files: 3 GB - Splits: \['test', 'val'\] - Number of Samples per splits: \[94.1K, 46.9K\] - Loading Script: ```python from datasets import load_dataset dataset = load_dataset(path="youssef101/artelingo", name='dev') ``` #### wecia-emo Intended for the [WECIA](https://iccv23-wecia.github.io/) emotion prediction challenge. Instances does not have the emotion or the language attributes. - Size of downloaded dataset files: 1.2 GB - Splits: \['dev'\] - Number of Samples per splits: \[27.9K\] - Loading Script: ```python from datasets import load_dataset dataset = load_dataset(path="youssef101/artelingo", name='wecia-emo') ``` #### wecia-cap Intended for the [WECIA](https://iccv23-wecia.github.io/) affective caption generation challenge. Instances does not have the text. - Size of downloaded dataset files: 1.2 GB - Splits: \['dev'\] - Number of Samples per splits: \[16.3K\] - Loading Script: ```python from datasets import load_dataset dataset = load_dataset(path="youssef101/artelingo", name='wecia-cap') ``` ### Data Fields The data fields are the same among all configs. - `uid`: a `int32` feature. A unique identifier for each instance. - `image`: a `PIL.Image` feature. The image of the artwork from the wikiart dataset. - `art_style`: a `string` feature. The art style of the artwork. Styles are a subset from the [wikiart styles](https://www.wikiart.org/en/paintings-by-style). - `painting`: a `string` feature. The name of the painting according to the wikiart dataset. - `emotion`: a `string` feature. The emotion associated with the image caption pair. - `language`: a `string` feature. The language used to write the caption. - `text`: a `string` feature. The affective caption that describes the painting under the context of the selected emotion. ## Dataset Creation ### Curation Rationale ArtELingo is a benchmark and dataset designed to promote research on diversity across languages and cultures. It builds upon ArtEmis, a collection of 80,000 artworks from WikiArt with 450,000 emotion labels and English-only captions. ArtELingo extends this dataset by adding 790,000 annotations in Arabic and Chinese, as well as 4,800 annotations in Spanish, allowing for the evaluation of "cultural-transfer" performance in AI systems. With many artworks having multiple annotations in three languages, the dataset enables the investigation of similarities and differences across linguistic and cultural contexts. Additionally, ArtELingo explores captioning tasks, demonstrating how diversity in annotations can improve the performance of baseline AI models. The hope is that ArtELingo will facilitate future research on multilinguality and culturally-aware AI. The dataset is publicly available, including standard splits and baseline models, to support and ease further research in this area. ### Source Data #### Initial Data Collection and Normalization ArtELingo uses images from the [wikiart dataset](https://www.wikiart.org/). The images are mainly artworks since they are created with the intention to have an emotional impact on the viewer. ArtELingo assumes that WikiArt is a representative sample of the cultures of interest. While WikiArt is remarkably comprehensive, it has better coverage of the West than other regions of the world based on WikiArt’s assignment of artworks to nationalities. The data was collected via Amazon Mechanical Turk, where only native speakers were allowed to annotate the images. The English, Arabic, and Chinese subsets were collected by 6377, 656, and 745 workers respectively. All workers were compensated with above minimal wage in each respective country. #### Who are the source language producers? The data comes from Human annotators who natively speak each respective language. ## Considerations for Using the Data ### Social Impact of Dataset When using the ArtELingo dataset, researchers and developers must be mindful of the potential social impact of the data. Emotions, cultural expressions, and artistic representations can be sensitive topics, and AI systems trained on such data may have implications on how they perceive and respond to users. It is crucial to ensure that the dataset's usage does not perpetuate stereotypes or biases related to specific cultures or languages. Ethical considerations should be taken into account during the development and deployment of AI models trained on ArtELingo to avoid any harmful consequences on individuals or communities. ### Discussion of Biases ArtELingo was filtered against hate speech, racism, and obvious stereotypes. However, Like any dataset, ArtELingo may contain inherent biases that could influence the performance and behavior of AI systems. These biases could arise from various sources, such as cultural differences in emotional interpretations, variations in annotator perspectives, or imbalances in the distribution of annotations across languages and cultures. Researchers should be cautious about potential biases that might impact the dataset's outcomes and address them appropriately. Transparently discussing and documenting these biases is essential to facilitate a fair understanding of the dataset's limitations and potential areas of improvement. ## Additional Information ### Dataset Curators The corpus was put together by [Youssef Mohamed](https://cemse.kaust.edu.sa/people/person/youssef-s-mohamed), [Mohamed Abdelfattah](https://people.epfl.ch/mohamed.abdelfattah/?lang=en), [Shyma Alhuwaider](https://cemse.kaust.edu.sa/aanslab/people/person/shyma-y-alhuwaider), [Feifan Li](https://www.linkedin.com/in/feifan-li-3280a6249/), [Xiangliang Zhang](https://engineering.nd.edu/faculty/xiangliang-zhang/), [Kenneth Ward Church](https://www.khoury.northeastern.edu/people/kenneth-church/) and [Mohamed Elhoseiny](https://cemse.kaust.edu.sa/people/person/mohamed-elhoseiny). ### Licensing Information Terms of Use: Before we are able to offer you access to the database, please agree to the following terms of use. After approval, you (the 'Researcher') receive permission to use the ArtELingo database (the 'Database') at King Abdullah University of Science and Technology (KAUST). In exchange for being able to join the ArtELingo community and receive such permission, Researcher hereby agrees to the following terms and conditions: [1.] The Researcher shall use the Database only for non-commercial research and educational purposes. [2.] The Universities 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 Universities, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, and Researcher's use of any copies of copyrighted 2D artworks originally uploaded to http://www.wikiart.org that the Researcher may use in connection with 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 Universities 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. [7.] The international copyright laws shall apply to all disputes under this agreement. ### Citation Information ``` @inproceedings{mohamed2022artelingo, title={ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture}, author={Mohamed, Youssef and Abdelfattah, Mohamed and Alhuwaider, Shyma and Li, Feifan and Zhang, Xiangliang and Church, Kenneth and Elhoseiny, Mohamed}, booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, pages={8770--8785}, year={2022} } ``` ### Contributions Thanks to [@youssef101](https://github.com/Mo-youssef) for adding this dataset. [@Faizan](https://faixan-khan.github.io/) for testing.
Minata/70000_method2test_tokonized_ForCausalLM	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: test num_bytes: 598079592 num_examples: 89694 download_size: 109394438 dataset_size: 598079592 --- # Dataset Card for "70000_method2test_tokonized_ForCausalLM" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lshowway/wikipedia.reorder.osv.fr	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 886603410 num_examples: 490371 download_size: 404858868 dataset_size: 886603410 --- # Dataset Card for "wikipedia.reorder.osv.fr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joey234/mmlu-professional_psychology	--- 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: fewshot_context_neg dtype: string splits: - name: dev num_bytes: 5774 num_examples: 5 - name: test num_bytes: 3081725 num_examples: 612 download_size: 304580 dataset_size: 3087499 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* --- # Dataset Card for "mmlu-professional_psychology" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/hanabatake_yoshiko_ahogirl	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Hanabatake Yoshiko This is the dataset of Hanabatake Yoshiko, containing 200 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 200 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 463 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 200 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 200 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 200 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 200 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 200 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 463 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 463 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 463 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|
MadNLP769/isacarsm	--- license: mit ---
Jzuluaga/uwb_atcc	--- dataset_info: features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: segment_start_time dtype: float32 - name: segment_end_time dtype: float32 - name: duration dtype: float32 splits: - name: test num_bytes: 140620332.25 num_examples: 2822 - name: train num_bytes: 608597323.625 num_examples: 11291 download_size: 711464914 dataset_size: 749217655.875 tags: - audio - automatic-speech-recognition - en-atc - en - noisy-speech-recognition - speech-recognition task_categories: - automatic-speech-recognition language: - en multilinguality: - monolingual license: - cc-by-nc-sa-4.0 --- # Dataset Card for UWB-ATCC corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages and Other Details](#languages-and-other-details) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - Homepage: [UWB-ATCC corpus homepage](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0) - Repository: [GitHub repository (used in research)](https://github.com/idiap/w2v2-air-traffic) - Paper: [Air traffic control communication (ATCC) speech corpora and their use for ASR and TTS development](https://link.springer.com/article/10.1007/s10579-019-09449-5) - Paper of this research: [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822) ### Dataset Summary The UWB-ATCC Corpus is provided provided by University of West Bohemia, Department of Cybernetics. The corpus contains recordings of communication between air traffic controllers and pilots. The speech is manually transcribed and labeled with the information about the speaker (pilot/controller, not the full identity of the person). The corpus is currently small (20 hours) but we plan to search for additional data next year. The audio data format is: 8kHz, 16bit PCM, mono. Important, from the `<id (string)>` field, you can obtain the speaker roles. For instance: - `_PI`: segment with only pilot speech - `_AT`: segment with only ATCO speech - `PIAT`: segment with both, ATCO and pilot speech ### Supported Tasks and Leaderboards - `automatic-speech-recognition`. Already adapted/fine-tuned models are available here --> [XLS-R-300m](https://huggingface.co/Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim). ### Languages and other details The text and the recordings are in English. The authors took advantage of the fact that one of their industrial partners develops complex IT solutions for several ATC authorities and airports and, as such, has access to the ATC communication recordings collected in the Czech airspace. This partner was able to secure the following data: - Ground control—communication before takeoff and after landing—19.2 h of data. - Tower control—communication during takeoff, landing and landing standby—22.5 h. - Approach control—communication during landing approach—25.5 h. - Area control—communication during overflights and cruises—71.3 h. (Not all data is released. Check their website [here](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0)) ## Dataset Structure ### Data Fields - `id (string)`: a string of recording identifier for each example, corresponding to its. - `audio (audio)`: audio data for the given ID - `text (string)`: transcript of the file already normalized. Follow these repositories for more details [w2v2-air-traffic](https://github.com/idiap/w2v2-air-traffic) and [bert-text-diarization-atc](https://github.com/idiap/bert-text-diarization-atc) - `segment_start_time (float32)`: segment start time (normally 0) - `segment_end_time (float32): segment end time - `duration (float32)`: duration of the recording, compute as segment_end_time - segment_start_time ## Additional Information ### Licensing Information The licensing status of the dataset hinges on the legal status of the [UWB-ATCC corpus](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0) creators. They used [Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) licensing. ### Citation Information Contributors who prepared, processed, normalized and uploaded the dataset in HuggingFace: ``` @article{zuluaga2022how, title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } @article{zuluaga2022bertraffic, title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } @article{zuluaga2022atco2, title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others}, journal={arXiv preprint arXiv:2211.04054}, year={2022} } ``` Authors of the dataset: ``` @article{vsmidl2019air, title={Air traffic control communication (ATCC) speech corpora and their use for ASR and TTS development}, author={{\v{S}}m{\'\i}dl, Lubo{\v{s}} and {\v{S}}vec, Jan and Tihelka, Daniel and Matou{\v{s}}ek, Jind{\v{r}}ich and Romportl, Jan and Ircing, Pavel}, journal={Language Resources and Evaluation}, volume={53}, number={3}, pages={449--464}, year={2019}, publisher={Springer} } ```
McSpicyWithMilo/instruction-types-0.3split	--- dataset_info: features: - name: instruction_type dtype: string - name: instruction dtype: string splits: - name: train num_bytes: 24468 num_examples: 280 - name: test num_bytes: 10561 num_examples: 120 download_size: 18875 dataset_size: 35029 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "instruction-types-0.3split" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TanveerAman/AMI-Corpus-Text-Summarization	--- task_categories: - summarization language: - en ---
CognitiveScience/data2	--- license: mit from: https://huggingface.co/datasets/saranya132/dialog_uid_gpt2 ---
amitness/logits-mt-ar-512	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: teacher_logits sequence: sequence: float64 - name: teacher_indices sequence: sequence: int64 - name: teacher_mask_indices sequence: int64 splits: - name: train num_bytes: 17102298098.701529 num_examples: 940987 - name: test num_bytes: 3018061158.5240602 num_examples: 166057 download_size: 7348415360 dataset_size: 20120359257.22559 --- # Dataset Card for "logits-mt-ar-512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CATIE-AQ/universal_dependencies_fr_gsd_fr_prompt_pos	--- language: - fr license: cc-by-sa-4.0 size_categories: - 100K<n<1M task_categories: - token-classification tags: - pos - DFP - french prompts annotations_creators: - found language_creators: - found multilinguality: - monolingual source_datasets: - universal_dependencies_fr_gsd --- # universal_dependencies_fr_gsd_fr_prompt_pos ## Summary universal_dependencies_fr_gsd_fr_prompt_pos is a subset of the [Dataset of French Prompts (DFP)](https://huggingface.co/datasets/CATIE-AQ/DFP). It contains 343,161 rows that can be used for a part-of-speech task. The original data (without prompts) comes from the dataset [universal_dependencies](https://huggingface.co/datasets/universal_dependencies) where only the French gsd split has been kept. A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al. ## Prompts used ### List 21 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement. ``` 'Extraire les classes des mots du texte suivant : '+text, 'Extrais les classes des mots du texte suivant : '+text, 'Extrayez les classes des mots du texte suivant : '+text, 'Isoler les classes des mots du texte suivant : '+text, 'Isole les classes des mots du texte suivant : '+text, 'Isolez les classes des mots du texte suivant : '+text, 'Dégager les classes des mots dans le texte : '+text, 'Dégage les classes des mots dans le texte : '+text, 'Dégagez les classes des mots dans le texte : '+text, 'Générer les classes des mots issues du texte suivant : '+text, 'Génère les classes des mots issues du texte suivant : '+text, 'Générez les classes des mots issues du texte suivant : '+text, 'Trouver les classes des mots du texte : '+text, 'Trouve les classes des mots du texte : '+text, 'Trouvez les classes des mots du texte : '+text, 'Repérer les classes des mots présentes dans le texte suivant : '+text, 'Repère les classes des mots présentes dans le texte suivant : '+text, 'Repérez les classes des mots présentes dans le texte suivant : '+text, 'Indiquer les classes des mots du texte :'+text, 'Indique les classes des mots du texte : '+text, 'Indiquez les classes des mots du texte : '+text ``` ### Features used in the prompts In the prompt list above, `text` and `targets` have been constructed from: ``` fr_gsd = load_dataset('universal_dependencies', 'fr_gsd') # text fr_gsd['train']['tokens'] = list(map(lambda i: ' '.join(fr_gsd['train']['tokens'][i]), range(len(fr_gsd['train']['tokens'])))) # targets fr_gsd['train']['upos'] = list(map(lambda x: x.replace("[","").replace("]","").replace('17','AUX').replace('16','VERB').replace('15','INTJ').replace('14','ADV').replace('13','_').replace('12','X').replace('11','PRON').replace('10','PROPN').replace('9','CCONJ').replace('8','DET').replace('7','PART').replace('6','ADJ').replace('5','SCONJ').replace('4','SYM').replace('3','NUM').replace('2','ADP').replace('1','PUNCT').replace('0','NOUN'), map(str,fr_gsd['train']['upos']))) ``` # Splits - `train` with 303,429 samples - `valid` with 30,996 samples - `test` with 8,736 samples # How to use? ``` from datasets import load_dataset dataset = load_dataset("CATIE-AQ/universal_dependencies_fr_gsd_fr_prompt_pos") ``` # Citation ## Original data > Contributors: de Marneffe, Marie-Catherine; Guillaume, Bruno; McDonald, Ryan; Suhr, Alane; Nivre, Joakim; Grioni, Matias; Dickerson, Carly; Perrier, Guy ## This Dataset > @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023, author = { {Centre Aquitain des Technologies de l'Information et Electroniques} }, title = { DFP (Revision 1d24c09) }, year = 2023, url = { https://huggingface.co/datasets/CATIE-AQ/DFP }, doi = { 10.57967/hf/1200 }, publisher = { Hugging Face } } ## License CC BY-SA 4.0
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/52026443	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1342 dataset_size: 188 --- # Dataset Card for "52026443" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Mamakhan/Tools	--- license: openrail ---
irds/antique_train_split200-valid	--- pretty_name: '`antique/train/split200-valid`' viewer: false source_datasets: ['irds/antique'] task_categories: - text-retrieval --- # Dataset Card for `antique/train/split200-valid` The `antique/train/split200-valid` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/antique#antique/train/split200-valid). # Data This dataset provides: - `queries` (i.e., topics); count=200 - `qrels`: (relevance assessments); count=2,193 - For `docs`, use [`irds/antique`](https://huggingface.co/datasets/irds/antique) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/antique_train_split200-valid', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/antique_train_split200-valid', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ...} ``` 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 ``` @inproceedings{Hashemi2020Antique, title={ANTIQUE: A Non-Factoid Question Answering Benchmark}, author={Helia Hashemi and Mohammad Aliannejadi and Hamed Zamani and Bruce Croft}, booktitle={ECIR}, year={2020} } ```
heliosprime/twitter_dataset_1713090697	--- 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: 6144 num_examples: 15 download_size: 10882 dataset_size: 6144 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713090697" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
seonglae/resrer-nq	--- dataset_info: features: - name: document_text dtype: string - name: long_answer_candidates list: - name: end_token dtype: int64 - name: start_token dtype: int64 - name: top_level dtype: bool - name: question_text dtype: string - name: annotations list: - name: annotation_id dtype: float64 - name: long_answer struct: - name: candidate_index dtype: int64 - name: end_token dtype: int64 - name: start_token dtype: int64 - name: short_answers list: - name: end_token dtype: int64 - name: start_token dtype: int64 - name: yes_no_answer dtype: string - name: document_url dtype: string - name: example_id dtype: int64 - name: long_answer_text dtype: string - name: short_answer_text dtype: string - name: split_id dtype: string - name: answer_exist_chunk dtype: bool - name: summarization_text dtype: string splits: - name: train num_bytes: 497862929.4517183 num_examples: 55113 download_size: 121306017 dataset_size: 497862929.4517183 configs: - config_name: default data_files: - split: train path: data/train-* ---
cstech/ssd-warp-2024	--- license: deepfloyd-if-license ---
CyberHarem/kurosaki_honoka_encouragementofclimb	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Kurosaki Honoka This is the dataset of Kurosaki Honoka, containing 82 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 \| 82 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 196 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 228 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 82 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 82 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 82 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 196 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 196 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 172 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 228 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 228 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|
joey234/mmlu-professional_medicine-rule-neg-prepend	--- 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: neg_prompt dtype: string splits: - name: test num_bytes: 440522 num_examples: 272 download_size: 250093 dataset_size: 440522 --- # Dataset Card for "mmlu-professional_medicine-rule-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joheras/adv-ele	--- dataset_info: features: - name: ADV dtype: string - name: ELE dtype: string splits: - name: train num_bytes: 430918.56140350876 num_examples: 1732 - name: test num_bytes: 107978.43859649122 num_examples: 434 download_size: 299002 dataset_size: 538897.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "adv-ele" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Isotonic__Mixnueza-Chat-6x32M-MoE	--- pretty_name: Evaluation run of Isotonic/Mixnueza-Chat-6x32M-MoE dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Isotonic/Mixnueza-Chat-6x32M-MoE](https://huggingface.co/Isotonic/Mixnueza-Chat-6x32M-MoE)\ \ 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_Isotonic__Mixnueza-Chat-6x32M-MoE\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-07T02:09:55.470077](https://huggingface.co/datasets/open-llm-leaderboard/details_Isotonic__Mixnueza-Chat-6x32M-MoE/blob/main/results_2024-04-07T02-09-55.470077.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\": 0.25475467189344064,\n\ \ \"acc_stderr\": 0.030639762090785793,\n \"acc_norm\": 0.2552581810114775,\n\ \ \"acc_norm_stderr\": 0.03144935013039305,\n \"mc1\": 0.25703794369645044,\n\ \ \"mc1_stderr\": 0.015298077509485083,\n \"mc2\": 0.4727026528122458,\n\ \ \"mc2_stderr\": 0.015699277111857743\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.18088737201365188,\n \"acc_stderr\": 0.011248574467407034,\n\ \ \"acc_norm\": 0.20392491467576793,\n \"acc_norm_stderr\": 0.011774262478702256\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.2629954192391954,\n\ \ \"acc_stderr\": 0.004393601887506585,\n \"acc_norm\": 0.26528579964150567,\n\ \ \"acc_norm_stderr\": 0.004405829993258718\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.22962962962962963,\n\ \ \"acc_stderr\": 0.03633384414073461,\n \"acc_norm\": 0.22962962962962963,\n\ \ \"acc_norm_stderr\": 0.03633384414073461\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.19078947368421054,\n \"acc_stderr\": 0.031975658210325,\n\ \ \"acc_norm\": 0.19078947368421054,\n \"acc_norm_stderr\": 0.031975658210325\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.2,\n\ \ \"acc_stderr\": 0.040201512610368445,\n \"acc_norm\": 0.2,\n \ \ \"acc_norm_stderr\": 0.040201512610368445\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.025288394502891356,\n\ \ \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.025288394502891356\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2638888888888889,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.2638888888888889,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\ : 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847415,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847415\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.2023121387283237,\n\ \ \"acc_stderr\": 0.030631145539198816,\n \"acc_norm\": 0.2023121387283237,\n\ \ \"acc_norm_stderr\": 0.030631145539198816\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\":\ \ 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \"acc_norm\": 0.22,\n\ \ \"acc_norm_stderr\": 0.041633319989322695\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.251063829787234,\n \"acc_stderr\": 0.028346963777162452,\n\ \ \"acc_norm\": 0.251063829787234,\n \"acc_norm_stderr\": 0.028346963777162452\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.19298245614035087,\n\ \ \"acc_stderr\": 0.037124548537213684,\n \"acc_norm\": 0.19298245614035087,\n\ \ \"acc_norm_stderr\": 0.037124548537213684\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.2896551724137931,\n \"acc_stderr\": 0.03780019230438014,\n\ \ \"acc_norm\": 0.2896551724137931,\n \"acc_norm_stderr\": 0.03780019230438014\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"\ acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.15079365079365079,\n\ \ \"acc_stderr\": 0.03200686497287392,\n \"acc_norm\": 0.15079365079365079,\n\ \ \"acc_norm_stderr\": 0.03200686497287392\n },\n \"harness\|hendrycksTest-global_facts\|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_biology\|5\": {\n \"acc\": 0.3161290322580645,\n\ \ \"acc_stderr\": 0.02645087448904277,\n \"acc_norm\": 0.3161290322580645,\n\ \ \"acc_norm_stderr\": 0.02645087448904277\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\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.21212121212121213,\n \"acc_stderr\": 0.03192271569548299,\n\ \ \"acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.03192271569548299\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.20202020202020202,\n \"acc_stderr\": 0.028606204289229872,\n \"\ acc_norm\": 0.20202020202020202,\n \"acc_norm_stderr\": 0.028606204289229872\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.27461139896373055,\n \"acc_stderr\": 0.03221024508041154,\n\ \ \"acc_norm\": 0.27461139896373055,\n \"acc_norm_stderr\": 0.03221024508041154\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.3564102564102564,\n \"acc_stderr\": 0.024283140529467295,\n\ \ \"acc_norm\": 0.3564102564102564,\n \"acc_norm_stderr\": 0.024283140529467295\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.27037037037037037,\n \"acc_stderr\": 0.027080372815145668,\n \ \ \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.027080372815145668\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.3025210084033613,\n \"acc_stderr\": 0.02983796238829193,\n \ \ \"acc_norm\": 0.3025210084033613,\n \"acc_norm_stderr\": 0.02983796238829193\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763744,\n \"\ acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763744\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.22752293577981653,\n \"acc_stderr\": 0.017974463578776502,\n \"\ acc_norm\": 0.22752293577981653,\n \"acc_norm_stderr\": 0.017974463578776502\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\ \ \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.22058823529411764,\n\ \ \"acc_stderr\": 0.02910225438967409,\n \"acc_norm\": 0.22058823529411764,\n\ \ \"acc_norm_stderr\": 0.02910225438967409\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.26582278481012656,\n \"acc_stderr\": 0.028756799629658342,\n\ \ \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.028756799629658342\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.27802690582959644,\n\ \ \"acc_stderr\": 0.030069584874494047,\n \"acc_norm\": 0.27802690582959644,\n\ \ \"acc_norm_stderr\": 0.030069584874494047\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.24427480916030533,\n \"acc_stderr\": 0.037683359597287434,\n\ \ \"acc_norm\": 0.24427480916030533,\n \"acc_norm_stderr\": 0.037683359597287434\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.2727272727272727,\n \"acc_stderr\": 0.04065578140908705,\n \"\ acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.04065578140908705\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.18518518518518517,\n\ \ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.18518518518518517,\n\ \ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.3128834355828221,\n \"acc_stderr\": 0.036429145782924055,\n\ \ \"acc_norm\": 0.3128834355828221,\n \"acc_norm_stderr\": 0.036429145782924055\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.19642857142857142,\n\ \ \"acc_stderr\": 0.03770970049347018,\n \"acc_norm\": 0.19642857142857142,\n\ \ \"acc_norm_stderr\": 0.03770970049347018\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\": 0.2264957264957265,\n\ \ \"acc_stderr\": 0.027421007295392926,\n \"acc_norm\": 0.2264957264957265,\n\ \ \"acc_norm_stderr\": 0.027421007295392926\n },\n \"harness\|hendrycksTest-medical_genetics\|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-miscellaneous\|5\": {\n \"acc\": 0.2848020434227331,\n\ \ \"acc_stderr\": 0.01613917409652258,\n \"acc_norm\": 0.2848020434227331,\n\ \ \"acc_norm_stderr\": 0.01613917409652258\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.22832369942196531,\n \"acc_stderr\": 0.022598703804321635,\n\ \ \"acc_norm\": 0.22832369942196531,\n \"acc_norm_stderr\": 0.022598703804321635\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\": 0.28938906752411575,\n\ \ \"acc_stderr\": 0.025755865922632924,\n \"acc_norm\": 0.28938906752411575,\n\ \ \"acc_norm_stderr\": 0.025755865922632924\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.2345679012345679,\n \"acc_stderr\": 0.023576881744005723,\n\ \ \"acc_norm\": 0.2345679012345679,\n \"acc_norm_stderr\": 0.023576881744005723\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.2624113475177305,\n \"acc_stderr\": 0.026244920349843007,\n \ \ \"acc_norm\": 0.2624113475177305,\n \"acc_norm_stderr\": 0.026244920349843007\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.23728813559322035,\n\ \ \"acc_stderr\": 0.010865436690780264,\n \"acc_norm\": 0.23728813559322035,\n\ \ \"acc_norm_stderr\": 0.010865436690780264\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121593,\n\ \ \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121593\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.2173202614379085,\n \"acc_stderr\": 0.016684820929148587,\n \ \ \"acc_norm\": 0.2173202614379085,\n \"acc_norm_stderr\": 0.016684820929148587\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\": 0.23265306122448978,\n \"acc_stderr\": 0.02704925791589618,\n\ \ \"acc_norm\": 0.23265306122448978,\n \"acc_norm_stderr\": 0.02704925791589618\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.23880597014925373,\n\ \ \"acc_stderr\": 0.030147775935409224,\n \"acc_norm\": 0.23880597014925373,\n\ \ \"acc_norm_stderr\": 0.030147775935409224\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\": 0.3313253012048193,\n\ \ \"acc_stderr\": 0.036643147772880864,\n \"acc_norm\": 0.3313253012048193,\n\ \ \"acc_norm_stderr\": 0.036643147772880864\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.0312678171466318,\n\ \ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.0312678171466318\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.25703794369645044,\n\ \ \"mc1_stderr\": 0.015298077509485083,\n \"mc2\": 0.4727026528122458,\n\ \ \"mc2_stderr\": 0.015699277111857743\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.505130228887135,\n \"acc_stderr\": 0.01405174596179052\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/Isotonic/Mixnueza-Chat-6x32M-MoE leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|arc:challenge\|25_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-07T02-09-55.470077.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|gsm8k\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hellaswag\|10_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T02-09-55.470077.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-07T02-09-55.470077.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '/details_harness\|winogrande\|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-07T02-09-55.470077.parquet' - config_name: results data_files: - split: 2024_04_07T02_09_55.470077 path: - results_2024-04-07T02-09-55.470077.parquet - split: latest path: - results_2024-04-07T02-09-55.470077.parquet --- # Dataset Card for Evaluation run of Isotonic/Mixnueza-Chat-6x32M-MoE <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Isotonic/Mixnueza-Chat-6x32M-MoE](https://huggingface.co/Isotonic/Mixnueza-Chat-6x32M-MoE) 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_Isotonic__Mixnueza-Chat-6x32M-MoE", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-07T02:09:55.470077](https://huggingface.co/datasets/open-llm-leaderboard/details_Isotonic__Mixnueza-Chat-6x32M-MoE/blob/main/results_2024-04-07T02-09-55.470077.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": 0.25475467189344064, "acc_stderr": 0.030639762090785793, "acc_norm": 0.2552581810114775, "acc_norm_stderr": 0.03144935013039305, "mc1": 0.25703794369645044, "mc1_stderr": 0.015298077509485083, "mc2": 0.4727026528122458, "mc2_stderr": 0.015699277111857743 }, "harness\|arc:challenge\|25": { "acc": 0.18088737201365188, "acc_stderr": 0.011248574467407034, "acc_norm": 0.20392491467576793, "acc_norm_stderr": 0.011774262478702256 }, "harness\|hellaswag\|10": { "acc": 0.2629954192391954, "acc_stderr": 0.004393601887506585, "acc_norm": 0.26528579964150567, "acc_norm_stderr": 0.004405829993258718 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.26, "acc_stderr": 0.04408440022768081, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768081 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.22962962962962963, "acc_stderr": 0.03633384414073461, "acc_norm": 0.22962962962962963, "acc_norm_stderr": 0.03633384414073461 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.19078947368421054, "acc_stderr": 0.031975658210325, "acc_norm": 0.19078947368421054, "acc_norm_stderr": 0.031975658210325 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.2, "acc_stderr": 0.040201512610368445, "acc_norm": 0.2, "acc_norm_stderr": 0.040201512610368445 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.21509433962264152, "acc_stderr": 0.025288394502891356, "acc_norm": 0.21509433962264152, "acc_norm_stderr": 0.025288394502891356 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2638888888888889, "acc_stderr": 0.03685651095897532, "acc_norm": 0.2638888888888889, "acc_norm_stderr": 0.03685651095897532 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.27, "acc_stderr": 0.044619604333847415, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847415 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.2023121387283237, "acc_stderr": 0.030631145539198816, "acc_norm": 0.2023121387283237, "acc_norm_stderr": 0.030631145539198816 }, "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": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.251063829787234, "acc_stderr": 0.028346963777162452, "acc_norm": 0.251063829787234, "acc_norm_stderr": 0.028346963777162452 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.19298245614035087, "acc_stderr": 0.037124548537213684, "acc_norm": 0.19298245614035087, "acc_norm_stderr": 0.037124548537213684 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2896551724137931, "acc_stderr": 0.03780019230438014, "acc_norm": 0.2896551724137931, "acc_norm_stderr": 0.03780019230438014 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.25396825396825395, "acc_stderr": 0.022418042891113942, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.022418042891113942 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.15079365079365079, "acc_stderr": 0.03200686497287392, "acc_norm": 0.15079365079365079, "acc_norm_stderr": 0.03200686497287392 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.3161290322580645, "acc_stderr": 0.02645087448904277, "acc_norm": 0.3161290322580645, "acc_norm_stderr": 0.02645087448904277 }, "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": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.21212121212121213, "acc_stderr": 0.03192271569548299, "acc_norm": 0.21212121212121213, "acc_norm_stderr": 0.03192271569548299 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.20202020202020202, "acc_stderr": 0.028606204289229872, "acc_norm": 0.20202020202020202, "acc_norm_stderr": 0.028606204289229872 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.27461139896373055, "acc_stderr": 0.03221024508041154, "acc_norm": 0.27461139896373055, "acc_norm_stderr": 0.03221024508041154 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.3564102564102564, "acc_stderr": 0.024283140529467295, "acc_norm": 0.3564102564102564, "acc_norm_stderr": 0.024283140529467295 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.27037037037037037, "acc_stderr": 0.027080372815145668, "acc_norm": 0.27037037037037037, "acc_norm_stderr": 0.027080372815145668 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.3025210084033613, "acc_stderr": 0.02983796238829193, "acc_norm": 0.3025210084033613, "acc_norm_stderr": 0.02983796238829193 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2781456953642384, "acc_stderr": 0.03658603262763744, "acc_norm": 0.2781456953642384, "acc_norm_stderr": 0.03658603262763744 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.22752293577981653, "acc_stderr": 0.017974463578776502, "acc_norm": 0.22752293577981653, "acc_norm_stderr": 0.017974463578776502 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.22058823529411764, "acc_stderr": 0.02910225438967409, "acc_norm": 0.22058823529411764, "acc_norm_stderr": 0.02910225438967409 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.26582278481012656, "acc_stderr": 0.028756799629658342, "acc_norm": 0.26582278481012656, "acc_norm_stderr": 0.028756799629658342 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.27802690582959644, "acc_stderr": 0.030069584874494047, "acc_norm": 0.27802690582959644, "acc_norm_stderr": 0.030069584874494047 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.24427480916030533, "acc_stderr": 0.037683359597287434, "acc_norm": 0.24427480916030533, "acc_norm_stderr": 0.037683359597287434 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2727272727272727, "acc_stderr": 0.04065578140908705, "acc_norm": 0.2727272727272727, "acc_norm_stderr": 0.04065578140908705 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.18518518518518517, "acc_stderr": 0.03755265865037181, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.03755265865037181 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.3128834355828221, "acc_stderr": 0.036429145782924055, "acc_norm": 0.3128834355828221, "acc_norm_stderr": 0.036429145782924055 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.19642857142857142, "acc_stderr": 0.03770970049347018, "acc_norm": 0.19642857142857142, "acc_norm_stderr": 0.03770970049347018 }, "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": 0.2264957264957265, "acc_stderr": 0.027421007295392926, "acc_norm": 0.2264957264957265, "acc_norm_stderr": 0.027421007295392926 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.2848020434227331, "acc_stderr": 0.01613917409652258, "acc_norm": 0.2848020434227331, "acc_norm_stderr": 0.01613917409652258 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.22832369942196531, "acc_stderr": 0.022598703804321635, "acc_norm": 0.22832369942196531, "acc_norm_stderr": 0.022598703804321635 }, "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": 0.28938906752411575, "acc_stderr": 0.025755865922632924, "acc_norm": 0.28938906752411575, "acc_norm_stderr": 0.025755865922632924 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.2345679012345679, "acc_stderr": 0.023576881744005723, "acc_norm": 0.2345679012345679, "acc_norm_stderr": 0.023576881744005723 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2624113475177305, "acc_stderr": 0.026244920349843007, "acc_norm": 0.2624113475177305, "acc_norm_stderr": 0.026244920349843007 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.23728813559322035, "acc_stderr": 0.010865436690780264, "acc_norm": 0.23728813559322035, "acc_norm_stderr": 0.010865436690780264 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.4485294117647059, "acc_stderr": 0.030211479609121593, "acc_norm": 0.4485294117647059, "acc_norm_stderr": 0.030211479609121593 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.2173202614379085, "acc_stderr": 0.016684820929148587, "acc_norm": 0.2173202614379085, "acc_norm_stderr": 0.016684820929148587 }, "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": 0.23265306122448978, "acc_stderr": 0.02704925791589618, "acc_norm": 0.23265306122448978, "acc_norm_stderr": 0.02704925791589618 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.23880597014925373, "acc_stderr": 0.030147775935409224, "acc_norm": 0.23880597014925373, "acc_norm_stderr": 0.030147775935409224 }, "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": 0.3313253012048193, "acc_stderr": 0.036643147772880864, "acc_norm": 0.3313253012048193, "acc_norm_stderr": 0.036643147772880864 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.21052631578947367, "acc_stderr": 0.0312678171466318, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.0312678171466318 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.25703794369645044, "mc1_stderr": 0.015298077509485083, "mc2": 0.4727026528122458, "mc2_stderr": 0.015699277111857743 }, "harness\|winogrande\|5": { "acc": 0.505130228887135, "acc_stderr": 0.01405174596179052 }, "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 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Li-Tang/cn_text	--- license: apache-2.0 ---
open-llm-leaderboard/details_cognitivecomputations__dolphin-2.8-experiment26-7b	--- pretty_name: Evaluation run of cognitivecomputations/dolphin-2.8-experiment26-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cognitivecomputations/dolphin-2.8-experiment26-7b](https://huggingface.co/cognitivecomputations/dolphin-2.8-experiment26-7b)\ \ 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_cognitivecomputations__dolphin-2.8-experiment26-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-05T00:47:48.033781](https://huggingface.co/datasets/open-llm-leaderboard/details_cognitivecomputations__dolphin-2.8-experiment26-7b/blob/main/results_2024-03-05T00-47-48.033781.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\": 0.6259859767682346,\n\ \ \"acc_stderr\": 0.032722228933055555,\n \"acc_norm\": 0.6269857052836199,\n\ \ \"acc_norm_stderr\": 0.03338852136792568,\n \"mc1\": 0.3806609547123623,\n\ \ \"mc1_stderr\": 0.01699762787190793,\n \"mc2\": 0.5510246746247679,\n\ \ \"mc2_stderr\": 0.015278599523000265\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6040955631399317,\n \"acc_stderr\": 0.014291228393536588,\n\ \ \"acc_norm\": 0.636518771331058,\n \"acc_norm_stderr\": 0.014056207319068285\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6484763991236805,\n\ \ \"acc_stderr\": 0.004764703145680275,\n \"acc_norm\": 0.8369846644094802,\n\ \ \"acc_norm_stderr\": 0.003686247559361841\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5481481481481482,\n\ \ \"acc_stderr\": 0.04299268905480864,\n \"acc_norm\": 0.5481481481481482,\n\ \ \"acc_norm_stderr\": 0.04299268905480864\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.038947344870133176,\n\ \ \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.038947344870133176\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.02845015479411864,\n\ \ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.02845015479411864\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7569444444444444,\n\ \ \"acc_stderr\": 0.03586879280080342,\n \"acc_norm\": 0.7569444444444444,\n\ \ \"acc_norm_stderr\": 0.03586879280080342\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.5,\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\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_medicine\|5\"\ : {\n \"acc\": 0.6127167630057804,\n \"acc_stderr\": 0.03714325906302065,\n\ \ \"acc_norm\": 0.6127167630057804,\n \"acc_norm_stderr\": 0.03714325906302065\n\ \ },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.3235294117647059,\n\ \ \"acc_stderr\": 0.046550104113196177,\n \"acc_norm\": 0.3235294117647059,\n\ \ \"acc_norm_stderr\": 0.046550104113196177\n },\n \"harness\|hendrycksTest-computer_security\|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\ \ \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5404255319148936,\n\ \ \"acc_stderr\": 0.03257901482099835,\n \"acc_norm\": 0.5404255319148936,\n\ \ \"acc_norm_stderr\": 0.03257901482099835\n },\n \"harness\|hendrycksTest-econometrics\|5\"\ : {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n\ \ \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n\ \ },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\"\ : 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"\ acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3915343915343915,\n \"acc_stderr\": 0.025138091388851105,\n \"\ acc_norm\": 0.3915343915343915,\n \"acc_norm_stderr\": 0.025138091388851105\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04426266681379909,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04426266681379909\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\": 0.7516129032258064,\n\ \ \"acc_stderr\": 0.024580028921481006,\n \"acc_norm\": 0.7516129032258064,\n\ \ \"acc_norm_stderr\": 0.024580028921481006\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.03517603540361008,\n\ \ \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.03517603540361008\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\ \ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\ acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6333333333333333,\n \"acc_stderr\": 0.02443301646605246,\n \ \ \"acc_norm\": 0.6333333333333333,\n \"acc_norm_stderr\": 0.02443301646605246\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3296296296296296,\n \"acc_stderr\": 0.02866120111652458,\n \ \ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.02866120111652458\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\"\ : 0.33112582781456956,\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\"\ : 0.8330275229357799,\n \"acc_stderr\": 0.0159901548850734,\n \"acc_norm\"\ : 0.8330275229357799,\n \"acc_norm_stderr\": 0.0159901548850734\n },\n\ \ \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5046296296296297,\n\ \ \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.5046296296296297,\n\ \ \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\"\ : {\n \"acc\": 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n\ \ \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601446,\n \ \ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601446\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6547085201793722,\n\ \ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\ \ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7592592592592593,\n\ \ \"acc_stderr\": 0.04133119440243838,\n \"acc_norm\": 0.7592592592592593,\n\ \ \"acc_norm_stderr\": 0.04133119440243838\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6871165644171779,\n \"acc_stderr\": 0.03642914578292406,\n\ \ \"acc_norm\": 0.6871165644171779,\n \"acc_norm_stderr\": 0.03642914578292406\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.044532548363264673,\n\ \ \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.044532548363264673\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077805,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077805\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7956577266922095,\n\ \ \"acc_stderr\": 0.014419123980931895,\n \"acc_norm\": 0.7956577266922095,\n\ \ \"acc_norm_stderr\": 0.014419123980931895\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.024818350129436593,\n\ \ \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.024818350129436593\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3027932960893855,\n\ \ \"acc_stderr\": 0.015366860386397112,\n \"acc_norm\": 0.3027932960893855,\n\ \ \"acc_norm_stderr\": 0.015366860386397112\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6928104575163399,\n \"acc_stderr\": 0.026415601914388995,\n\ \ \"acc_norm\": 0.6928104575163399,\n \"acc_norm_stderr\": 0.026415601914388995\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6975308641975309,\n \"acc_stderr\": 0.025557653981868052,\n\ \ \"acc_norm\": 0.6975308641975309,\n \"acc_norm_stderr\": 0.025557653981868052\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.46099290780141844,\n \"acc_stderr\": 0.02973659252642444,\n \ \ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.02973659252642444\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4556714471968709,\n\ \ \"acc_stderr\": 0.012719949543032207,\n \"acc_norm\": 0.4556714471968709,\n\ \ \"acc_norm_stderr\": 0.012719949543032207\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6213235294117647,\n \"acc_stderr\": 0.02946513363977613,\n\ \ \"acc_norm\": 0.6213235294117647,\n \"acc_norm_stderr\": 0.02946513363977613\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6470588235294118,\n \"acc_stderr\": 0.019333142020797157,\n \ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.019333142020797157\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\": 0.6979591836734694,\n \"acc_stderr\": 0.0293936093198798,\n\ \ \"acc_norm\": 0.6979591836734694,\n \"acc_norm_stderr\": 0.0293936093198798\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197768,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197768\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368032,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368032\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3806609547123623,\n\ \ \"mc1_stderr\": 0.01699762787190793,\n \"mc2\": 0.5510246746247679,\n\ \ \"mc2_stderr\": 0.015278599523000265\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7876874506708761,\n \"acc_stderr\": 0.011493384687249787\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6262319939347991,\n \ \ \"acc_stderr\": 0.013326342860737018\n }\n}\n```" repo_url: https://huggingface.co/cognitivecomputations/dolphin-2.8-experiment26-7b 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_02T20_37_13.780824 path: - '/details_harness\|arc:challenge\|25_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|arc:challenge\|25_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-05T00-47-48.033781.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|gsm8k\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|gsm8k\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hellaswag\|10_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hellaswag\|10_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-02T20-37-13.780824.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-05T00-47-48.033781.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-05T00-47-48.033781.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '/details_harness\|winogrande\|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '/details_harness\|winogrande\|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-05T00-47-48.033781.parquet' - config_name: results data_files: - split: 2024_03_02T20_37_13.780824 path: - results_2024-03-02T20-37-13.780824.parquet - split: 2024_03_05T00_47_48.033781 path: - results_2024-03-05T00-47-48.033781.parquet - split: latest path: - results_2024-03-05T00-47-48.033781.parquet --- # Dataset Card for Evaluation run of cognitivecomputations/dolphin-2.8-experiment26-7b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [cognitivecomputations/dolphin-2.8-experiment26-7b](https://huggingface.co/cognitivecomputations/dolphin-2.8-experiment26-7b) 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_cognitivecomputations__dolphin-2.8-experiment26-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-05T00:47:48.033781](https://huggingface.co/datasets/open-llm-leaderboard/details_cognitivecomputations__dolphin-2.8-experiment26-7b/blob/main/results_2024-03-05T00-47-48.033781.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": 0.6259859767682346, "acc_stderr": 0.032722228933055555, "acc_norm": 0.6269857052836199, "acc_norm_stderr": 0.03338852136792568, "mc1": 0.3806609547123623, "mc1_stderr": 0.01699762787190793, "mc2": 0.5510246746247679, "mc2_stderr": 0.015278599523000265 }, "harness\|arc:challenge\|25": { "acc": 0.6040955631399317, "acc_stderr": 0.014291228393536588, "acc_norm": 0.636518771331058, "acc_norm_stderr": 0.014056207319068285 }, "harness\|hellaswag\|10": { "acc": 0.6484763991236805, "acc_stderr": 0.004764703145680275, "acc_norm": 0.8369846644094802, "acc_norm_stderr": 0.003686247559361841 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5481481481481482, "acc_stderr": 0.04299268905480864, "acc_norm": 0.5481481481481482, "acc_norm_stderr": 0.04299268905480864 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6447368421052632, "acc_stderr": 0.038947344870133176, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.038947344870133176 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.690566037735849, "acc_stderr": 0.02845015479411864, "acc_norm": 0.690566037735849, "acc_norm_stderr": 0.02845015479411864 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7569444444444444, "acc_stderr": 0.03586879280080342, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.03586879280080342 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3235294117647059, "acc_stderr": 0.046550104113196177, "acc_norm": 0.3235294117647059, "acc_norm_stderr": 0.046550104113196177 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.73, "acc_stderr": 0.0446196043338474, "acc_norm": 0.73, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5404255319148936, "acc_stderr": 0.03257901482099835, "acc_norm": 0.5404255319148936, "acc_norm_stderr": 0.03257901482099835 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, 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0.6470588235294118, "acc_norm_stderr": 0.019333142020797157 }, "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": 0.6979591836734694, "acc_stderr": 0.0293936093198798, "acc_norm": 0.6979591836734694, "acc_norm_stderr": 0.0293936093198798 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.03487350880197768, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197768 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8128654970760234, "acc_stderr": 0.029913127232368032, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.029913127232368032 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3806609547123623, "mc1_stderr": 0.01699762787190793, "mc2": 0.5510246746247679, "mc2_stderr": 0.015278599523000265 }, "harness\|winogrande\|5": { "acc": 0.7876874506708761, "acc_stderr": 0.011493384687249787 }, "harness\|gsm8k\|5": { "acc": 0.6262319939347991, "acc_stderr": 0.013326342860737018 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More 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CyberHarem/hazuki_ren_lovelivesuperstar	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of hazuki_ren/葉月恋 (Love Live! Superstar!!) This is the dataset of hazuki_ren/葉月恋 (Love Live! Superstar!!), containing 474 images and their tags. The core tags of this character are `black_hair, long_hair, yellow_eyes, bangs, ponytail, high_ponytail, bow, breasts, ribbon, hair_bow, shiny_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 474 \| 638.63 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hazuki_ren_lovelivesuperstar/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 474 \| 331.00 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hazuki_ren_lovelivesuperstar/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1112 \| 718.87 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hazuki_ren_lovelivesuperstar/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 474 \| 546.44 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hazuki_ren_lovelivesuperstar/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1112 \| 1.08 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/hazuki_ren_lovelivesuperstar/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/hazuki_ren_lovelivesuperstar', 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, blue_jacket, grey_dress, long_sleeves, looking_at_viewer, neck_ribbon, open_jacket, red_ribbon, smile, solo, yuigaoka_school_uniform, birthday, blush, pinafore_dress, medium_breasts, upper_body \| \| 1 \| 14 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blue_jacket, grey_dress, looking_at_viewer, neck_ribbon, open_jacket, pinafore_dress, red_ribbon, solo, yuigaoka_school_uniform, blush, collared_shirt, long_sleeves, smile, simple_background, white_background, closed_mouth, cowboy_shot, white_shirt \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, blue_jacket, brown_footwear, closed_mouth, full_body, grey_dress, loafers, long_sleeves, looking_at_viewer, neck_ribbon, open_jacket, pinafore_dress, red_ribbon, smile, solo, standing, white_background, white_shirt, white_socks, yuigaoka_school_uniform, collared_shirt, simple_background, white_bow, arms_behind_back, blush, kneehighs, leaning_forward \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, birthday, looking_at_viewer, smile, solo, upper_body, blush, shiny \| \| 4 \| 10 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, birthday, looking_at_viewer, smile, solo, white_gloves, blush, medium_breasts, shiny, upper_body, sleeveless, white_dress, bubble, signature \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| red_bowtie, school_uniform, 1girl, collared_shirt, solo, upper_body, blush, closed_mouth, looking_at_viewer, short_sleeves, medium_breasts, skirt, white_shirt \| \| 6 \| 6 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, open_jacket, solo, full_body, looking_at_viewer, thigh_strap, white_footwear, white_jacket, dress, frills, skirt, detached_collar, simple_background, smile, white_background \| \| 7 \| 7 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, blush, cleavage, collarbone, looking_at_viewer, navel, solo, medium_breasts, white_background, white_bikini, cowboy_shot, parted_lips, simple_background, smile, stomach, bare_shoulders, blue_bikini, halterneck, large_breasts \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blue_jacket \| grey_dress \| long_sleeves \| looking_at_viewer \| neck_ribbon \| open_jacket \| red_ribbon \| smile \| solo \| yuigaoka_school_uniform \| birthday \| blush \| pinafore_dress \| medium_breasts \| upper_body \| collared_shirt \| simple_background \| white_background \| closed_mouth \| cowboy_shot \| white_shirt \| brown_footwear \| full_body \| loafers \| standing \| white_socks \| white_bow \| arms_behind_back \| kneehighs \| leaning_forward \| shiny \| white_gloves \| sleeveless \| white_dress \| bubble \| signature \| red_bowtie \| school_uniform \| short_sleeves \| skirt \| thigh_strap \| white_footwear \| white_jacket \| dress \| frills \| detached_collar \| cleavage \| collarbone \| navel \| white_bikini \| parted_lips \| stomach \| bare_shoulders \| blue_bikini \| halterneck \| large_breasts \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------\|:-------------\|:---------------\|:--------------------\|:--------------\|:--------------\|:-------------\|:--------\|:-------\|:--------------------------\|:-----------\|:--------\|:-----------------\|:-----------------\|:-------------\|:-----------------\|:--------------------\|:-------------------\|:---------------\|:--------------\|:--------------\|:-----------------\|:------------\|:----------\|:-----------\|:--------------\|:------------\|:-------------------\|:------------\|:------------------\|:--------\|:---------------\|:-------------\|:--------------\|:---------\|:------------\|:-------------\|:-----------------\|:----------------\|:--------\|:--------------\|:-----------------\|:---------------\|:--------\|:---------\|:------------------\|:-----------\|:-------------\|:--------\|:---------------\|:--------------\|:----------\|:-----------------\|:--------------\|:-------------\|:----------------\| \| 0 \| 5 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 14 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| \| \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| \| \| X \| \| \| \| X \| X \| \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 10 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| \| \| X \| \| \| \| X \| X \| \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| \| \| X \| \| \| \| \| X \| \| \| X \| \| X \| X \| X \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 6 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| \| \| X \| \| X \| \| X \| X \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 7 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| \| \| X \| \| \| \| X \| X \| \| \| X \| \| X \| \| \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
thisisHJLee/vi_data_made3	--- license: apache-2.0 ---
CVasNLPExperiments/textvqa_mini_validation_google_flan_t5_xxl_mode_OCR_VQA_Q_rices_ns_10	--- dataset_info: features: - name: id dtype: int64 - name: prompt sequence: string - name: question dtype: string - name: true_label sequence: string - name: prediction dtype: string splits: - name: fewshot_0 num_bytes: 15008 num_examples: 10 download_size: 7112 dataset_size: 15008 configs: - config_name: default data_files: - split: fewshot_0 path: data/fewshot_0-* ---
gu37/In-Shop-Clothes-Segmentation	--- license: mit ---
open-llm-leaderboard/details_gagan3012__MetaModel	--- pretty_name: Evaluation run of gagan3012/MetaModel dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [gagan3012/MetaModel](https://huggingface.co/gagan3012/MetaModel) 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_gagan3012__MetaModel\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-04T14:09:43.780941](https://huggingface.co/datasets/open-llm-leaderboard/details_gagan3012__MetaModel/blob/main/results_2024-01-04T14-09-43.780941.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\": 0.6664380298886512,\n\ \ \"acc_stderr\": 0.031642195230944255,\n \"acc_norm\": 0.6671639222858992,\n\ \ \"acc_norm_stderr\": 0.03228745343467652,\n \"mc1\": 0.5691554467564259,\n\ \ \"mc1_stderr\": 0.01733527247533237,\n \"mc2\": 0.7184177934834866,\n\ \ \"mc2_stderr\": 0.014995634120330182\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6843003412969283,\n \"acc_stderr\": 0.013582571095815291,\n\ \ \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.01325001257939344\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7132045409281019,\n\ \ \"acc_stderr\": 0.004513409114983828,\n \"acc_norm\": 0.8844851623182632,\n\ \ \"acc_norm_stderr\": 0.0031898897894046684\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.743421052631579,\n \"acc_stderr\": 0.0355418036802569,\n\ \ \"acc_norm\": 0.743421052631579,\n \"acc_norm_stderr\": 0.0355418036802569\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\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\": 0.48,\n \"acc_stderr\": 0.05021167315686781,\n \"acc_norm\"\ : 0.48,\n \"acc_norm_stderr\": 0.05021167315686781\n },\n \"harness\|hendrycksTest-college_mathematics\|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-college_medicine\|5\": {\n \"acc\": 0.6647398843930635,\n\ \ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\ \ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.625531914893617,\n \"acc_stderr\": 0.03163910665367291,\n\ \ \"acc_norm\": 0.625531914893617,\n \"acc_norm_stderr\": 0.03163910665367291\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.039966295748767186,\n\ \ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.039966295748767186\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.025751310131230234,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.025751310131230234\n },\n \"harness\|hendrycksTest-formal_logic\|5\"\ : {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.0442626668137991,\n\ \ \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.0442626668137991\n\ \ },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.35,\n\ \ \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \ \ \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-high_school_biology\|5\"\ : {\n \"acc\": 0.8129032258064516,\n \"acc_stderr\": 0.022185710092252252,\n\ \ \"acc_norm\": 0.8129032258064516,\n \"acc_norm_stderr\": 0.022185710092252252\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"\ acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\ \ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"\ acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644244,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644244\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.02385479568097114,\n \ \ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.02385479568097114\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \ \ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.029344572500634332,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.029344572500634332\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009246,\n \"\ acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009246\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5740740740740741,\n \"acc_stderr\": 0.03372343271653062,\n \"\ acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.03372343271653062\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.02450980392156862,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.02450980392156862\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8565400843881856,\n \"acc_stderr\": 0.022818291821017012,\n \ \ \"acc_norm\": 0.8565400843881856,\n \"acc_norm_stderr\": 0.022818291821017012\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728743,\n\ \ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728743\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.035865947385739734,\n\ \ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.035865947385739734\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8589743589743589,\n\ \ \"acc_stderr\": 0.02280138253459753,\n \"acc_norm\": 0.8589743589743589,\n\ \ \"acc_norm_stderr\": 0.02280138253459753\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\": 0.8084291187739464,\n\ \ \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n\ \ \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7572254335260116,\n \"acc_stderr\": 0.023083658586984204,\n\ \ \"acc_norm\": 0.7572254335260116,\n \"acc_norm_stderr\": 0.023083658586984204\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.39664804469273746,\n\ \ \"acc_stderr\": 0.016361354769822468,\n \"acc_norm\": 0.39664804469273746,\n\ \ \"acc_norm_stderr\": 0.016361354769822468\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\ \ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7202572347266881,\n\ \ \"acc_stderr\": 0.025494259350694905,\n \"acc_norm\": 0.7202572347266881,\n\ \ \"acc_norm_stderr\": 0.025494259350694905\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.02313237623454333,\n\ \ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02313237623454333\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5035460992907801,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.5035460992907801,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.49478487614080835,\n\ \ \"acc_stderr\": 0.012769541449652547,\n \"acc_norm\": 0.49478487614080835,\n\ \ \"acc_norm_stderr\": 0.012769541449652547\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.026303648393696036,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.026303648393696036\n \ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\"\ : 0.6813725490196079,\n \"acc_stderr\": 0.018850084696468712,\n \"\ acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.018850084696468712\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399677,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399677\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \ \ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5783132530120482,\n\ \ \"acc_stderr\": 0.038444531817709175,\n \"acc_norm\": 0.5783132530120482,\n\ \ \"acc_norm_stderr\": 0.038444531817709175\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03188578017686398,\n\ \ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03188578017686398\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5691554467564259,\n\ \ \"mc1_stderr\": 0.01733527247533237,\n \"mc2\": 0.7184177934834866,\n\ \ \"mc2_stderr\": 0.014995634120330182\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8342541436464088,\n \"acc_stderr\": 0.010450899545370632\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6535253980288097,\n \ \ \"acc_stderr\": 0.013107179054313398\n }\n}\n```" repo_url: https://huggingface.co/gagan3012/MetaModel 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_04T14_09_43.780941 path: - '/details_harness\|arc:challenge\|25_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-04T14-09-43.780941.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|gsm8k\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hellaswag\|10_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T14-09-43.780941.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-04T14-09-43.780941.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '/details_harness\|winogrande\|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-04T14-09-43.780941.parquet' - config_name: results data_files: - split: 2024_01_04T14_09_43.780941 path: - results_2024-01-04T14-09-43.780941.parquet - split: latest path: - results_2024-01-04T14-09-43.780941.parquet --- # Dataset Card for Evaluation run of gagan3012/MetaModel <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [gagan3012/MetaModel](https://huggingface.co/gagan3012/MetaModel) 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_gagan3012__MetaModel", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-04T14:09:43.780941](https://huggingface.co/datasets/open-llm-leaderboard/details_gagan3012__MetaModel/blob/main/results_2024-01-04T14-09-43.780941.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": 0.6664380298886512, "acc_stderr": 0.031642195230944255, "acc_norm": 0.6671639222858992, "acc_norm_stderr": 0.03228745343467652, "mc1": 0.5691554467564259, "mc1_stderr": 0.01733527247533237, "mc2": 0.7184177934834866, "mc2_stderr": 0.014995634120330182 }, "harness\|arc:challenge\|25": { "acc": 0.6843003412969283, "acc_stderr": 0.013582571095815291, "acc_norm": 0.7107508532423208, "acc_norm_stderr": 0.01325001257939344 }, "harness\|hellaswag\|10": { "acc": 0.7132045409281019, "acc_stderr": 0.004513409114983828, "acc_norm": 0.8844851623182632, "acc_norm_stderr": 0.0031898897894046684 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.743421052631579, "acc_stderr": 0.0355418036802569, "acc_norm": 0.743421052631579, "acc_norm_stderr": 0.0355418036802569 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "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": 0.48, "acc_stderr": 0.05021167315686781, "acc_norm": 0.48, "acc_norm_stderr": 0.05021167315686781 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.625531914893617, "acc_stderr": 0.03163910665367291, "acc_norm": 0.625531914893617, "acc_norm_stderr": 0.03163910665367291 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6413793103448275, "acc_stderr": 0.039966295748767186, "acc_norm": 0.6413793103448275, "acc_norm_stderr": 0.039966295748767186 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.5, "acc_stderr": 0.025751310131230234, "acc_norm": 0.5, "acc_norm_stderr": 0.025751310131230234 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.0442626668137991, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.0442626668137991 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8129032258064516, "acc_stderr": 0.022185710092252252, "acc_norm": 0.8129032258064516, "acc_norm_stderr": 0.022185710092252252 }, 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0.018850084696468712 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.746938775510204, "acc_stderr": 0.027833023871399677, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.027833023871399677 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.91, "acc_stderr": 0.028762349126466125, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466125 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5783132530120482, "acc_stderr": 0.038444531817709175, "acc_norm": 0.5783132530120482, "acc_norm_stderr": 0.038444531817709175 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03188578017686398, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03188578017686398 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5691554467564259, "mc1_stderr": 0.01733527247533237, "mc2": 0.7184177934834866, "mc2_stderr": 0.014995634120330182 }, "harness\|winogrande\|5": { "acc": 0.8342541436464088, "acc_stderr": 0.010450899545370632 }, "harness\|gsm8k\|5": { "acc": 0.6535253980288097, "acc_stderr": 0.013107179054313398 } } ``` ## 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 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CyberHarem/yanfei_genshin	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yanfei/煙緋/烟绯 (Genshin Impact) This is the dataset of yanfei/煙緋/烟绯 (Genshin Impact), containing 500 images and their tags. The core tags of this character are `pink_hair, long_hair, horns, green_eyes, hair_between_eyes, red_headwear, hat, breasts, small_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:----------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 1.06 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/yanfei_genshin/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 500 \| 878.81 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yanfei_genshin/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1337 \| 1.80 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/yanfei_genshin/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/yanfei_genshin', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, bare_shoulders, black_bra, cleavage, crop_top, detached_sleeves, long_sleeves, looking_at_viewer, midriff, solo, cowboy_shot, navel, stomach, antlers, red_skirt, white_background, :d, open_mouth, simple_background, yellow_bow, standing, very_long_hair, holding, book \| \| 1 \| 6 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, bare_shoulders, black_bra, cleavage, crop_top, detached_sleeves, long_sleeves, looking_at_viewer, midriff, smile, solo, antlers, upper_body, white_background, navel, simple_background, stomach \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, bare_shoulders, bloomers, cleavage, crop_top, detached_sleeves, long_sleeves, midriff, navel, smile, solo, standing, stomach, antlers, black_bra, cowboy_shot, black_shorts, looking_at_viewer, red_skirt, fire, simple_background, thighs, yellow_bow \| \| 3 \| 9 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, antlers, bare_shoulders, crop_top, detached_sleeves, long_sleeves, looking_at_viewer, midriff, solo, navel, open_mouth, stomach, cleavage, red_skirt, :d \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, antlers, bare_shoulders, crop_top, detached_sleeves, long_sleeves, looking_at_viewer, open_mouth, solo, boots, midriff, red_footwear, skirt, :d, holding, staff \| \| 5 \| 6 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, antlers, bare_shoulders, detached_sleeves, looking_at_viewer, solo, upper_body, holding_book, long_sleeves, smile, closed_mouth \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| bare_shoulders \| black_bra \| cleavage \| crop_top \| detached_sleeves \| long_sleeves \| looking_at_viewer \| midriff \| solo \| cowboy_shot \| navel \| stomach \| antlers \| red_skirt \| white_background \| :d \| open_mouth \| simple_background \| yellow_bow \| standing \| very_long_hair \| holding \| book \| smile \| upper_body \| bloomers \| black_shorts \| fire \| thighs \| boots \| red_footwear \| skirt \| staff \| holding_book \| closed_mouth \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------\|:------------\|:-----------\|:-----------\|:-------------------\|:---------------\|:--------------------\|:----------\|:-------\|:--------------\|:--------\|:----------\|:----------\|:------------\|:-------------------\|:-----\|:-------------\|:--------------------\|:-------------\|:-----------\|:-----------------\|:----------\|:-------\|:--------\|:-------------\|:-----------\|:---------------\|:-------\|:---------\|:--------\|:---------------\|:--------\|:--------\|:---------------\|:---------------\| \| 0 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 6 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| \| X \| \| \| X \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| X \| X \| X \| \| \| \| X \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| 3 \| 9 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| X \| \| \| X \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| 5 \| 6 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| \| \| \| X \| X \| X \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| X \| X \|
huggingartists/elton-john	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/elton-john" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Repository: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of the generated dataset: 1.422945 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/ec76d346c4c8b057169194c1781021fd.1000x1000x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/elton-john"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Elton John</div> <a href="https://genius.com/artists/elton-john"> <div style="text-align: center; font-size: 14px;">@elton-john</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/elton-john). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/elton-john") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits \| train \|validation\|test\| \|------:\|---------:\|---:\| \|1311\| -\| -\| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/elton-john") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])train_percentage), int(len(datasets['train']['text'])(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About Built by Aleksey Korshuk [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)
ysharma/dummy123	--- license: mit ---
CVasNLPExperiments/OxfordPets_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_3669	--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 1065643 num_examples: 3669 download_size: 193852 dataset_size: 1065643 --- # Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_3669" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
skytnt/japanese-lyric	--- license: cc0-1.0 task_categories: - text-generation language: - ja tags: - music pretty_name: Japanese Lyric size_categories: - 10K<n<100K ---
HuggingFaceM4/ScienceQAImg_Modif	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: context dtype: string - name: label dtype: class_label: names: '0': A '1': B '2': C '3': D '4': E splits: - name: train num_bytes: 204229907.55288386 num_examples: 6218 - name: validation num_bytes: 68613530.46875736 num_examples: 2097 - name: test num_bytes: 65108877.472058475 num_examples: 2017 download_size: 661814327 dataset_size: 337952315.4936997 --- # Dataset Card for "ScienceQAImg_Modif" This dataset contains the [ScienceQA benchmark](https://arxiv.org/pdf/2209.09513.pdf) where only examples with an image are kept, and where we formatted the prompt.
csaybar/CloudSEN12-nolabel	--- license: cc-by-nc-4.0 --- # CloudSEN12 NOLABEL ## A Benchmark Dataset for Cloud Semantic Understanding ![CloudSEN12 Images](https://cloudsen12.github.io/thumbnails/cloudsen12.gif) CloudSEN12 is a LARGE dataset (~1 TB) for cloud semantic understanding that consists of 49,400 image patches (IP) that are evenly spread throughout all continents except Antarctica. Each IP covers 5090 x 5090 meters and contains data from Sentinel-2 levels 1C and 2A, hand-crafted annotations of thick and thin clouds and cloud shadows, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from six cutting-edge cloud detection algorithms. CloudSEN12 is designed to support both weakly and self-/semi-supervised learning strategies by including three distinct forms of hand-crafted labeling data: high-quality, scribble and no-annotation. For more details on how we created the dataset see our paper. Ready to start using [CloudSEN12](https://cloudsen12.github.io/)? [Download Dataset](https://cloudsen12.github.io/download.html) [Paper - Scientific Data](https://www.nature.com/articles/s41597-022-01878-2) [Inference on a new S2 image](https://colab.research.google.com/github/cloudsen12/examples/blob/master/example02.ipynb) [Enter to cloudApp](https://github.com/cloudsen12/CloudApp) [CloudSEN12 in Google Earth Engine](https://gee-community-catalog.org/projects/cloudsen12/) <br> ### Description <br> \| File \| Name \| Scale \| Wavelength \| Description \| Datatype \| \|---------------\|-----------------\|--------\|------------------------------\|------------------------------------------------------------------------------------------------------\|----------\| \| L1C_ & L2A_ \| B1 \| 0.0001 \| 443.9nm (S2A) / 442.3nm (S2B)\| Aerosols. \| np.int16 \| \| \| B2 \| 0.0001 \| 496.6nm (S2A) / 492.1nm (S2B)\| Blue. \| np.int16 \| \| \| B3 \| 0.0001 \| 560nm (S2A) / 559nm (S2B) \| Green. \| np.int16 \| \| \| B4 \| 0.0001 \| 664.5nm (S2A) / 665nm (S2B) \| Red. \| np.int16 \| \| \| B5 \| 0.0001 \| 703.9nm (S2A) / 703.8nm (S2B)\| Red Edge 1. \| np.int16 \| \| \| B6 \| 0.0001 \| 740.2nm (S2A) / 739.1nm (S2B)\| Red Edge 2. \| np.int16 \| \| \| B7 \| 0.0001 \| 782.5nm (S2A) / 779.7nm (S2B)\| Red Edge 3. \| np.int16 \| \| \| B8 \| 0.0001 \| 835.1nm (S2A) / 833nm (S2B) \| NIR. \| np.int16 \| \| \| B8A \| 0.0001 \| 864.8nm (S2A) / 864nm (S2B) \| Red Edge 4. \| np.int16 \| \| \| B9 \| 0.0001 \| 945nm (S2A) / 943.2nm (S2B) \| Water vapor. \| np.int16 \| \| \| B11 \| 0.0001 \| 1613.7nm (S2A) / 1610.4nm (S2B)\| SWIR 1. \| np.int16 \| \| \| B12 \| 0.0001 \| 2202.4nm (S2A) / 2185.7nm (S2B)\| SWIR 2. \| np.int16 \| \| L1C_ \| B10 \| 0.0001 \| 1373.5nm (S2A) / 1376.9nm (S2B)\| Cirrus. \| np.int16 \| \| L2A_ \| AOT \| 0.001 \| - \| Aerosol Optical Thickness. \| np.int16 \| \| \| WVP \| 0.001 \| - \| Water Vapor Pressure. \| np.int16 \| \| \| TCI_R \| 1 \| - \| True Color Image, Red. \| np.int16 \| \| \| TCI_G \| 1 \| - \| True Color Image, Green. \| np.int16 \| \| \| TCI_B \| 1 \| - \| True Color Image, Blue. \| np.int16 \| \| S1_ \| VV \| 1 \| 5.405GHz \| Dual-band cross-polarization, vertical transmit/horizontal receive. \|np.float32\| \| \| VH \| 1 \| 5.405GHz \| Single co-polarization, vertical transmit/vertical receive. \|np.float32\| \| \| angle \| 1 \| - \| Incidence angle generated by interpolating the ‘incidenceAngle’ property. \|np.float32\| \| EXTRA_ \| CDI \| 0.0001 \| - \| Cloud Displacement Index. \| np.int16 \| \| \| Shwdirection \| 0.01 \| - \| Azimuth. Values range from 0°- 360°. \| np.int16 \| \| \| elevation \| 1 \| - \| Elevation in meters. Obtained from MERIT Hydro datasets. \| np.int16 \| \| \| ocurrence \| 1 \| - \| JRC Global Surface Water. The frequency with which water was present. \| np.int16 \| \| \| LC100 \| 1 \| - \| Copernicus land cover product. CGLS-LC100 Collection 3. \| np.int16 \| \| \| LC10 \| 1 \| - \| ESA WorldCover 10m v100 product. \| np.int16 \| \| LABEL_ \| fmask \| 1 \| - \| Fmask4.0 cloud masking. \| np.int16 \| \| \| QA60 \| 1 \| - \| SEN2 Level-1C cloud mask. \| np.int8 \| \| \| s2cloudless \| 1 \| - \| sen2cloudless results. \| np.int8 \| \| \| sen2cor \| 1 \| - \| Scene Classification band. Obtained from SEN2 level 2A. \| np.int8 \| \| \| cd_fcnn_rgbi \| 1 \| - \| López-Puigdollers et al. results based on RGBI bands. \| np.int8 \| \| \|cd_fcnn_rgbi_swir\| 1 \| - \| López-Puigdollers et al. results based on RGBISWIR bands. \| np.int8 \| \| \| kappamask_L1C \| 1 \| - \| KappaMask results using SEN2 level L1C as input. \| np.int8 \| \| \| kappamask_L2A \| 1 \| - \| KappaMask results using SEN2 level L2A as input. \| np.int8 \| \| \| manual_hq \| 1 \| \| High-quality pixel-wise manual annotation. \| np.int8 \| \| \| manual_sc \| 1 \| \| Scribble manual annotation. \| np.int8 \| <br> ### Label Description \| CloudSEN12 \| KappaMask \| Sen2Cor \| Fmask \| s2cloudless \| CD-FCNN \| QA60 \| \|------------------\|------------------\|-------------------------\|-----------------\|-----------------------\|---------------------\|--------------------\| \| 0 Clear \| 1 Clear \| 4 Vegetation \| 0 Clear land \| 0 Clear \| 0 Clear \| 0 Clear \| \| \| \| 2 Dark area pixels \| 1 Clear water \| \| \| \| \| \| \| 5 Bare Soils \| 3 Snow \| \| \| \| \| \| \| 6 Water \| \| \| \| \| \| \| \| 11 Snow \| \| \| \| \| \| 1 Thick cloud \| 4 Cloud \| 8 Cloud medium probability \| 4 Cloud \| 1 Cloud \| 1 Cloud \| 1024 Opaque cloud \| \| \| \| 9 Cloud high probability \| \| \| \| \| \| 2 Thin cloud \| 3 Semi-transparent cloud \| 10 Thin cirrus \| \| \| \| 2048 Cirrus cloud \| \| 3 Cloud shadow \| 2 Cloud shadow \| 3 Cloud shadows \| 2 Cloud shadow \| \| \| \| <br> ### np.memmap shape information <br> cloudfree (0\%) shape: (5880, 512, 512) <br> almostclear (0-25 \%) shape: (5880, 512, 512) <br> lowcloudy (25-45 \%) shape: (5880, 512, 512) <br> midcloudy (45-65 \%) shape: (5880, 512, 512) <br> cloudy (65 > \%) shape: (5880, 512, 512) <br> ### Example <br> ```py import numpy as np # Read high-quality train cloudfree_shape = (5880, 512, 512) B4X = np.memmap('cloudfree/L1C_B04.dat', dtype='int16', mode='r', shape=cloudfree_shape) y = np.memmap('cloudfree/manual_hq.dat', dtype='int8', mode='r', shape=cloudfree_shape) # Read high-quality val almostclear_shape = (5880, 512, 512) B4X = np.memmap('almostclear/L1C_B04.dat', dtype='int16', mode='r', shape=almostclear_shape) y = np.memmap('almostclear/kappamask_L1C.dat', dtype='int8', mode='r', shape=almostclear_shape) # Read high-quality test midcloudy_shape = (5880, 512, 512) B4X = np.memmap('midcloudy/L1C_B04.dat', dtype='int16', mode='r', shape=midcloudy_shape) y = np.memmap('midcloudy/kappamask_L1C.dat', dtype='int8', mode='r', shape=midcloudy_shape) ``` <br> This work has been partially supported by the Spanish Ministry of Science and Innovation project PID2019-109026RB-I00 (MINECO-ERDF) and the Austrian Space Applications Programme within the [SemantiX project](https://austria-in-space.at/en/projects/2019/semantix.php).
communityai/communityai_apt-instruct-code-micro-70k	--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 291560981.6281767 num_examples: 70000 download_size: 129436280 dataset_size: 291560981.6281767 configs: - config_name: default data_files: - split: train path: data/train-* ---
yjernite/prof_report__dalle-2__sd_21__24	--- dataset_info: features: - name: cluster_id dtype: int64 - name: cluster_size dtype: int64 - name: img_ids sequence: int64 - name: img_cluster_scores sequence: float64 splits: - name: paralegal num_bytes: 3624 num_examples: 11 - name: bartender num_bytes: 3576 num_examples: 9 - name: facilities_manager num_bytes: 3576 num_examples: 9 - name: accountant num_bytes: 3480 num_examples: 5 - name: graphic_designer num_bytes: 3600 num_examples: 10 - name: network_administrator num_bytes: 3648 num_examples: 12 - name: financial_manager num_bytes: 3504 num_examples: 6 - name: baker num_bytes: 3600 num_examples: 10 - name: security_guard num_bytes: 3528 num_examples: 7 - name: artist num_bytes: 3696 num_examples: 14 - name: author num_bytes: 3648 num_examples: 12 - name: printing_press_operator num_bytes: 3504 num_examples: 6 - name: public_relations_specialist num_bytes: 3552 num_examples: 8 - name: sheet_metal_worker num_bytes: 3504 num_examples: 6 - name: clergy num_bytes: 3600 num_examples: 10 - name: payroll_clerk num_bytes: 3624 num_examples: 11 - name: teller num_bytes: 3600 num_examples: 10 - name: real_estate_broker num_bytes: 3480 num_examples: 5 - name: customer_service_representative num_bytes: 3600 num_examples: 10 - name: painter num_bytes: 3696 num_examples: 14 - name: tractor_operator num_bytes: 3552 num_examples: 8 - name: dental_hygienist num_bytes: 3528 num_examples: 7 - name: industrial_engineer num_bytes: 3576 num_examples: 9 - name: electrician num_bytes: 3576 num_examples: 9 - name: head_cook num_bytes: 3576 num_examples: 9 - name: health_technician num_bytes: 3576 num_examples: 9 - name: carpet_installer num_bytes: 3624 num_examples: 11 - name: purchasing_agent num_bytes: 3552 num_examples: 8 - name: supervisor num_bytes: 3552 num_examples: 8 - name: civil_engineer num_bytes: 3504 num_examples: 6 - name: lawyer num_bytes: 3576 num_examples: 9 - name: language_pathologist num_bytes: 3576 num_examples: 9 - name: ceo num_bytes: 3480 num_examples: 5 - name: computer_support_specialist num_bytes: 3480 num_examples: 5 - name: postal_worker num_bytes: 3576 num_examples: 9 - name: mechanical_engineer num_bytes: 3576 num_examples: 9 - name: nursing_assistant num_bytes: 3528 num_examples: 7 - name: dentist num_bytes: 3600 num_examples: 10 - name: tutor num_bytes: 3576 num_examples: 9 - name: butcher num_bytes: 3552 num_examples: 8 - name: insurance_agent num_bytes: 3552 num_examples: 8 - name: courier num_bytes: 3552 num_examples: 8 - name: computer_programmer num_bytes: 3528 num_examples: 7 - name: truck_driver num_bytes: 3480 num_examples: 5 - name: mechanic num_bytes: 3576 num_examples: 9 - name: marketing_manager num_bytes: 3528 num_examples: 7 - name: sales_manager num_bytes: 3480 num_examples: 5 - name: correctional_officer num_bytes: 3528 num_examples: 7 - name: manager num_bytes: 3504 num_examples: 6 - name: underwriter num_bytes: 3528 num_examples: 7 - name: executive_assistant num_bytes: 3528 num_examples: 7 - name: designer num_bytes: 3576 num_examples: 9 - name: groundskeeper num_bytes: 3624 num_examples: 11 - name: mental_health_counselor num_bytes: 3600 num_examples: 10 - name: aerospace_engineer num_bytes: 3552 num_examples: 8 - name: taxi_driver num_bytes: 3552 num_examples: 8 - name: nurse num_bytes: 3504 num_examples: 6 - name: data_entry_keyer num_bytes: 3624 num_examples: 11 - name: musician num_bytes: 3624 num_examples: 11 - name: event_planner num_bytes: 3696 num_examples: 14 - name: writer num_bytes: 3576 num_examples: 9 - name: cook num_bytes: 3648 num_examples: 12 - name: welder num_bytes: 3552 num_examples: 8 - name: producer num_bytes: 3648 num_examples: 12 - name: hairdresser num_bytes: 3672 num_examples: 13 - name: farmer num_bytes: 3528 num_examples: 7 - name: construction_worker num_bytes: 3576 num_examples: 9 - name: air_conditioning_installer num_bytes: 3504 num_examples: 6 - name: electrical_engineer num_bytes: 3504 num_examples: 6 - name: occupational_therapist num_bytes: 3552 num_examples: 8 - name: career_counselor num_bytes: 3528 num_examples: 7 - name: interior_designer num_bytes: 3648 num_examples: 12 - name: jailer num_bytes: 3528 num_examples: 7 - name: office_clerk num_bytes: 3504 num_examples: 6 - name: market_research_analyst num_bytes: 3576 num_examples: 9 - name: laboratory_technician num_bytes: 3576 num_examples: 9 - name: social_assistant num_bytes: 3552 num_examples: 8 - name: medical_records_specialist num_bytes: 3624 num_examples: 11 - name: machinery_mechanic num_bytes: 3504 num_examples: 6 - name: police_officer num_bytes: 3552 num_examples: 8 - name: software_developer num_bytes: 3480 num_examples: 5 - name: clerk num_bytes: 3480 num_examples: 5 - name: salesperson num_bytes: 3552 num_examples: 8 - name: social_worker num_bytes: 3672 num_examples: 13 - name: director num_bytes: 3504 num_examples: 6 - name: fast_food_worker num_bytes: 3648 num_examples: 12 - name: singer num_bytes: 3696 num_examples: 14 - name: metal_worker num_bytes: 3576 num_examples: 9 - name: cleaner num_bytes: 3648 num_examples: 12 - name: computer_systems_analyst num_bytes: 3600 num_examples: 10 - name: dental_assistant num_bytes: 3504 num_examples: 6 - name: psychologist num_bytes: 3576 num_examples: 9 - name: machinist num_bytes: 3504 num_examples: 6 - name: therapist num_bytes: 3504 num_examples: 6 - name: veterinarian num_bytes: 3528 num_examples: 7 - name: teacher num_bytes: 3576 num_examples: 9 - name: architect num_bytes: 3552 num_examples: 8 - name: office_worker num_bytes: 3552 num_examples: 8 - name: drywall_installer num_bytes: 3552 num_examples: 8 - name: nutritionist num_bytes: 3552 num_examples: 8 - name: librarian num_bytes: 3576 num_examples: 9 - name: childcare_worker num_bytes: 3576 num_examples: 9 - name: school_bus_driver num_bytes: 3504 num_examples: 6 - name: file_clerk num_bytes: 3504 num_examples: 6 - name: logistician num_bytes: 3528 num_examples: 7 - name: scientist num_bytes: 3528 num_examples: 7 - name: teaching_assistant num_bytes: 3576 num_examples: 9 - name: radiologic_technician num_bytes: 3504 num_examples: 6 - name: manicurist num_bytes: 3600 num_examples: 10 - name: community_manager num_bytes: 3528 num_examples: 7 - name: carpenter num_bytes: 3600 num_examples: 10 - name: claims_appraiser num_bytes: 3552 num_examples: 8 - name: dispatcher num_bytes: 3576 num_examples: 9 - name: cashier num_bytes: 3600 num_examples: 10 - name: roofer num_bytes: 3504 num_examples: 6 - name: photographer num_bytes: 3600 num_examples: 10 - name: detective num_bytes: 3576 num_examples: 9 - name: financial_advisor num_bytes: 3504 num_examples: 6 - name: wholesale_buyer num_bytes: 3576 num_examples: 9 - name: it_specialist num_bytes: 3456 num_examples: 4 - name: pharmacy_technician num_bytes: 3600 num_examples: 10 - name: engineer num_bytes: 3456 num_examples: 4 - name: mover num_bytes: 3696 num_examples: 14 - name: plane_mechanic num_bytes: 3504 num_examples: 6 - name: interviewer num_bytes: 3552 num_examples: 8 - name: massage_therapist num_bytes: 3624 num_examples: 11 - name: dishwasher num_bytes: 3624 num_examples: 11 - name: fitness_instructor num_bytes: 3576 num_examples: 9 - name: credit_counselor num_bytes: 3552 num_examples: 8 - name: stocker num_bytes: 3672 num_examples: 13 - name: pharmacist num_bytes: 3504 num_examples: 6 - name: doctor num_bytes: 3552 num_examples: 8 - name: compliance_officer num_bytes: 3552 num_examples: 8 - name: aide num_bytes: 3600 num_examples: 10 - name: bus_driver num_bytes: 3552 num_examples: 8 - name: financial_analyst num_bytes: 3504 num_examples: 6 - name: receptionist num_bytes: 3624 num_examples: 11 - name: janitor num_bytes: 3576 num_examples: 9 - name: plumber num_bytes: 3528 num_examples: 7 - name: physical_therapist num_bytes: 3528 num_examples: 7 - name: inventory_clerk num_bytes: 3576 num_examples: 9 - name: firefighter num_bytes: 3528 num_examples: 7 - name: coach num_bytes: 3528 num_examples: 7 - name: maid num_bytes: 3528 num_examples: 7 - name: pilot num_bytes: 3504 num_examples: 6 - name: repair_worker num_bytes: 3576 num_examples: 9 download_size: 867106 dataset_size: 520104 --- # Dataset Card for "prof_report__dalle-2__sd_21__24" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/hisaishi_kanade_soundeuphonium	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Hisaishi Kanade/久石奏 (Sound! Euphonium) This is the dataset of Hisaishi Kanade/久石奏 (Sound! Euphonium), containing 182 images and their tags. The core tags of this character are `short_hair, black_hair, bow, hair_bow, red_eyes, red_bow`, 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 \| 182 \| 122.80 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hisaishi_kanade_soundeuphonium/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 182 \| 122.72 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hisaishi_kanade_soundeuphonium/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 359 \| 220.91 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hisaishi_kanade_soundeuphonium/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/hisaishi_kanade_soundeuphonium', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 19 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, blue_sailor_collar, blush, green_neckerchief, kitauji_high_school_uniform, serafuku, white_shirt, indoors, solo, closed_mouth, looking_at_viewer, short_sleeves, chalkboard, holding_instrument \| \| 1 \| 8 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blue_sailor_collar, blue_skirt, blush, chair, classroom, closed_mouth, indoors, kitauji_high_school_uniform, serafuku, sitting, white_shirt, desk, holding_instrument, pleated_skirt, short_sleeves, brown_eyes, green_neckerchief, chalkboard, looking_at_viewer, solo_focus, smile \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, blue_sailor_collar, blue_skirt, blurry, blush, chair, chalkboard, classroom, desk, green_neckerchief, indoors, instrument, kitauji_high_school_uniform, pleated_skirt, serafuku, short_sleeves, solo, standing, white_shirt, open_mouth, bag, looking_at_viewer, smile, sweatdrop \| \| 3 \| 8 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, blush, brown_shirt, closed_mouth, kitauji_high_school_uniform, serafuku, solo, white_sailor_collar, indoors, smile, green_neckerchief, blurry_background, long_sleeves, looking_at_viewer \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, blurry_background, blush, brown_shirt, brown_skirt, green_neckerchief, kitauji_high_school_uniform, long_sleeves, pleated_skirt, serafuku, smile, solo, standing, white_sailor_collar, closed_mouth, indoors \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, blue_sailor_collar, blue_skirt, blush, closed_mouth, green_neckerchief, hand_up, indoors, kitauji_high_school_uniform, pleated_skirt, school_bag, serafuku, short_sleeves, solo, standing, white_shirt, window, black_bag, brown_hair, looking_at_viewer, mole_under_eye, smile, blurry_background, brown_eyes, hallway \| \| 6 \| 8 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, blue_sailor_collar, blurry_background, blush, kitauji_high_school_uniform, outdoors, rain, serafuku, solo, white_shirt, open_mouth, green_neckerchief, parted_lips, brown_eyes, building, wet_hair \| \| 7 \| 9 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, solo, green_jacket, blush, closed_mouth, blurry_background, looking_at_viewer, holding_instrument, long_sleeves, outdoors, shirt, track_jacket \| \| 8 \| 17 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, blush, yellow_headwear, solo, closed_mouth, holding_instrument, short_sleeves, band_uniform, orange_headwear, shirt, red_gloves, blurry_background, hat_feather, grey_background, looking_at_viewer \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blue_sailor_collar \| blush \| green_neckerchief \| kitauji_high_school_uniform \| serafuku \| white_shirt \| indoors \| solo \| closed_mouth \| looking_at_viewer \| short_sleeves \| chalkboard \| holding_instrument \| blue_skirt \| chair \| classroom \| sitting \| desk \| pleated_skirt \| brown_eyes \| solo_focus \| smile \| blurry \| instrument \| standing \| open_mouth \| bag \| sweatdrop \| brown_shirt \| white_sailor_collar \| blurry_background \| long_sleeves \| brown_skirt \| hand_up \| school_bag \| window \| black_bag \| brown_hair \| mole_under_eye \| hallway \| outdoors \| rain \| parted_lips \| building \| wet_hair \| green_jacket \| shirt \| track_jacket \| yellow_headwear \| band_uniform \| orange_headwear \| red_gloves \| hat_feather \| grey_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------------\|:--------\|:--------------------\|:------------------------------\|:-----------\|:--------------\|:----------\|:-------\|:---------------\|:--------------------\|:----------------\|:-------------\|:---------------------\|:-------------\|:--------\|:------------\|:----------\|:-------\|:----------------\|:-------------\|:-------------\|:--------\|:---------\|:-------------\|:-----------\|:-------------\|:------\|:------------\|:--------------\|:----------------------\|:--------------------\|:---------------\|:--------------\|:----------\|:-------------\|:---------\|:------------\|:-------------\|:-----------------\|:----------\|:-----------\|:-------\|:--------------\|:-----------\|:-----------\|:---------------\|:--------\|:---------------\|:------------------\|:---------------\|:------------------\|:-------------\|:--------------\|:------------------\| \| 0 \| 19 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 8 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| \| X \| X \| X \| \| X \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 8 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| X \| X \| X \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| X \| X \| X \| X \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| X \| \| \| \| \| X \| X \| \| X \| \| \| X \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 8 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| 7 \| 9 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| X \| \| \| \| \| \| X \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| 8 \| 17 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| X \| \| \| \| \| \| X \| X \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \|
autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861032	--- type: predictions tags: - autotrain - evaluation datasets: - phpthinh/examplei eval_info: task: text_zero_shot_classification model: bigscience/bloom-7b1 metrics: ['f1'] dataset_name: phpthinh/examplei dataset_config: all 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: bigscience/bloom-7b1 * Dataset: phpthinh/examplei * Config: all * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.
malteos/wikinews-tmp2	--- dataset_info: - config_name: de features: - name: language dtype: string - name: wiki_page_id dtype: string - name: wiki_revision_id dtype: string - name: revision_timestamp dtype: timestamp[us, tz=UTC] - name: revision_year dtype: uint16 - name: revision_month dtype: uint16 - name: article_timestamp dtype: timestamp[us, tz=UTC] - name: article_year dtype: uint16 - name: article_month dtype: uint16 - name: url dtype: string - name: title dtype: string - name: raw_text dtype: string - name: cleaned_text dtype: string - name: categories sequence: string - name: sources sequence: string - name: dump dtype: string splits: - name: 2004_q4_12 num_bytes: 1060779 num_examples: 251 - name: 2005_q1_01 num_bytes: 402111 num_examples: 99 - name: 2005_q1_02 num_bytes: 602415 num_examples: 162 - name: 2005_q1_03 num_bytes: 845392 num_examples: 195 - name: 2005_q3_08 num_bytes: 1392393 num_examples: 360 - name: 2005_q2_04 num_bytes: 754328 num_examples: 186 - name: 2005_q2_05 num_bytes: 750409 num_examples: 179 - name: 2005_q3_07 num_bytes: 1380652 num_examples: 334 - name: 2005_q2_06 num_bytes: 993773 num_examples: 257 - name: 2005_q4_10 num_bytes: 1716394 num_examples: 410 - name: 2005_q4_11 num_bytes: 934471 num_examples: 230 - name: 2007_q1_03 num_bytes: 901035 num_examples: 175 - name: 2005_q3_09 num_bytes: 1659850 num_examples: 392 - name: 2004_q3_08 num_bytes: 7316 num_examples: 2 - name: 2005_q4_12 num_bytes: 1086986 num_examples: 268 - name: 2006_q1_01 num_bytes: 1209718 num_examples: 279 - name: 2006_q1_02 num_bytes: 819639 num_examples: 194 - name: 2006_q1_03 num_bytes: 1074845 num_examples: 247 - name: 2006_q2_06 num_bytes: 1170821 num_examples: 263 - name: 2006_q2_04 num_bytes: 978701 num_examples: 221 - name: 2006_q2_05 num_bytes: 1136732 num_examples: 271 - name: 2006_q3_07 num_bytes: 1161245 num_examples: 249 - name: 2006_q3_08 num_bytes: 1275797 num_examples: 241 - name: 2006_q3_09 num_bytes: 873844 num_examples: 157 - name: 2006_q4_10 num_bytes: 913674 num_examples: 206 - name: 2006_q4_11 num_bytes: 986117 num_examples: 193 - 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split: 2005_q1_02 path: de/2005_q1_02-* - split: 2005_q1_03 path: de/2005_q1_03-* - split: 2005_q3_08 path: de/2005_q3_08-* - split: 2005_q2_04 path: de/2005_q2_04-* - split: 2005_q2_05 path: de/2005_q2_05-* - split: 2005_q3_07 path: de/2005_q3_07-* - split: 2005_q2_06 path: de/2005_q2_06-* - split: 2005_q4_10 path: de/2005_q4_10-* - split: 2005_q4_11 path: de/2005_q4_11-* - split: 2007_q1_03 path: de/2007_q1_03-* - split: 2005_q3_09 path: de/2005_q3_09-* - split: 2004_q3_08 path: de/2004_q3_08-* - split: 2005_q4_12 path: de/2005_q4_12-* - split: 2006_q1_01 path: de/2006_q1_01-* - split: 2006_q1_02 path: de/2006_q1_02-* - split: 2006_q1_03 path: de/2006_q1_03-* - split: 2006_q2_06 path: de/2006_q2_06-* - split: 2006_q2_04 path: de/2006_q2_04-* - split: 2006_q2_05 path: de/2006_q2_05-* - split: 2006_q3_07 path: de/2006_q3_07-* - split: 2006_q3_08 path: de/2006_q3_08-* - split: 2006_q3_09 path: de/2006_q3_09-* - split: 2006_q4_10 path: de/2006_q4_10-* - split: 2006_q4_11 path: de/2006_q4_11-* - split: 2006_q4_12 path: de/2006_q4_12-* - split: 2007_q1_02 path: de/2007_q1_02-* - split: 2007_q1_01 path: de/2007_q1_01-* - split: 2007_q2_06 path: de/2007_q2_06-* - split: 2007_q2_04 path: de/2007_q2_04-* - split: 2007_q2_05 path: de/2007_q2_05-* - split: 2007_q3_07 path: de/2007_q3_07-* - split: 2007_q3_08 path: de/2007_q3_08-* - split: 2007_q3_09 path: de/2007_q3_09-* - split: 2007_q4_10 path: de/2007_q4_10-* - split: 2007_q4_11 path: de/2007_q4_11-* - split: 2007_q4_12 path: de/2007_q4_12-* - split: 2008_q1_01 path: de/2008_q1_01-* - split: 2008_q1_02 path: de/2008_q1_02-* - split: 2008_q1_03 path: de/2008_q1_03-* - split: 2008_q2_04 path: de/2008_q2_04-* - split: 2008_q2_05 path: de/2008_q2_05-* - split: 2008_q2_06 path: de/2008_q2_06-* - split: 2008_q3_07 path: de/2008_q3_07-* - split: 2008_q3_08 path: de/2008_q3_08-* - split: 2008_q3_09 path: de/2008_q3_09-* - split: 2008_q4_10 path: de/2008_q4_10-* - split: 2008_q4_11 path: de/2008_q4_11-* - split: 2008_q4_12 path: de/2008_q4_12-* - split: 2009_q1_01 path: de/2009_q1_01-* - split: 2009_q1_02 path: de/2009_q1_02-* - split: 2009_q1_03 path: de/2009_q1_03-* - split: 2009_q2_04 path: de/2009_q2_04-* - split: 2009_q2_05 path: de/2009_q2_05-* - split: 2009_q2_06 path: de/2009_q2_06-* - split: 2009_q3_07 path: de/2009_q3_07-* - split: 2009_q3_08 path: de/2009_q3_08-* - split: 2009_q3_09 path: de/2009_q3_09-* - split: 2009_q4_10 path: de/2009_q4_10-* - split: 2009_q4_11 path: de/2009_q4_11-* - split: 2009_q4_12 path: de/2009_q4_12-* - split: 2010_q1_01 path: de/2010_q1_01-* - split: 2010_q1_02 path: de/2010_q1_02-* - split: 2010_q1_03 path: de/2010_q1_03-* - split: 2010_q2_04 path: de/2010_q2_04-* - split: 2010_q2_05 path: de/2010_q2_05-* - split: 2010_q2_06 path: de/2010_q2_06-* - split: 2010_q3_07 path: de/2010_q3_07-* - split: 2010_q3_08 path: de/2010_q3_08-* - split: 2010_q3_09 path: de/2010_q3_09-* - split: 2010_q4_10 path: de/2010_q4_10-* - split: 2010_q4_11 path: de/2010_q4_11-* - split: 2010_q4_12 path: de/2010_q4_12-* - split: 2011_q1_01 path: de/2011_q1_01-* - split: 2011_q1_02 path: de/2011_q1_02-* - split: 2011_q1_03 path: de/2011_q1_03-* - split: 2011_q2_04 path: de/2011_q2_04-* - split: 2011_q2_05 path: de/2011_q2_05-* - split: 2011_q2_06 path: de/2011_q2_06-* - split: 2011_q3_07 path: de/2011_q3_07-* - split: 2011_q3_08 path: de/2011_q3_08-* - split: 2011_q3_09 path: de/2011_q3_09-* - split: 2011_q4_10 path: de/2011_q4_10-* - split: 2011_q4_11 path: de/2011_q4_11-* - split: 2011_q4_12 path: de/2011_q4_12-* - split: 2012_q1_01 path: de/2012_q1_01-* - split: 2012_q1_02 path: de/2012_q1_02-* - split: 2012_q1_03 path: de/2012_q1_03-* - split: 2012_q2_04 path: de/2012_q2_04-* - split: 2012_q2_05 path: de/2012_q2_05-* - split: 2012_q2_06 path: de/2012_q2_06-* - split: 2012_q3_07 path: de/2012_q3_07-* - split: 2012_q3_08 path: de/2012_q3_08-* - split: 2012_q3_09 path: de/2012_q3_09-* - split: 2012_q4_10 path: de/2012_q4_10-* - split: 2012_q4_11 path: de/2012_q4_11-* - split: 2012_q4_12 path: de/2012_q4_12-* - split: 2013_q1_01 path: de/2013_q1_01-* - split: no_date path: de/no_date-* - split: 2013_q1_02 path: de/2013_q1_02-* - split: 2013_q1_03 path: de/2013_q1_03-* - split: 2013_q2_04 path: de/2013_q2_04-* - split: 2013_q2_05 path: de/2013_q2_05-* - split: 2013_q2_06 path: de/2013_q2_06-* - split: 2013_q3_07 path: de/2013_q3_07-* - split: 2013_q3_09 path: de/2013_q3_09-* - split: 2013_q3_08 path: de/2013_q3_08-* - split: 2013_q4_10 path: de/2013_q4_10-* - split: 2013_q4_11 path: de/2013_q4_11-* - split: 2013_q4_12 path: de/2013_q4_12-* - split: 2014_q1_01 path: de/2014_q1_01-* - split: 2014_q1_02 path: de/2014_q1_02-* - split: 2014_q1_03 path: de/2014_q1_03-* - split: 2014_q2_04 path: de/2014_q2_04-* - split: 2014_q2_05 path: de/2014_q2_05-* - split: 2014_q2_06 path: de/2014_q2_06-* - split: 2014_q3_07 path: de/2014_q3_07-* - split: 2014_q3_08 path: de/2014_q3_08-* - split: 2014_q3_09 path: de/2014_q3_09-* - split: 2014_q4_10 path: de/2014_q4_10-* - split: 2014_q4_11 path: de/2014_q4_11-* - split: 2014_q4_12 path: de/2014_q4_12-* - split: 2015_q1_01 path: de/2015_q1_01-* - split: 2015_q1_02 path: de/2015_q1_02-* - split: 2015_q1_03 path: de/2015_q1_03-* - split: 2015_q2_04 path: de/2015_q2_04-* - split: 2015_q2_06 path: de/2015_q2_06-* - split: 2015_q2_05 path: de/2015_q2_05-* - split: 2015_q3_07 path: de/2015_q3_07-* - split: 2015_q3_08 path: de/2015_q3_08-* - split: 2015_q3_09 path: de/2015_q3_09-* - split: 2015_q4_10 path: de/2015_q4_10-* - split: 2015_q4_11 path: de/2015_q4_11-* - split: 2015_q4_12 path: de/2015_q4_12-* - split: 2016_q1_02 path: de/2016_q1_02-* - split: 2016_q1_01 path: de/2016_q1_01-* - split: 2016_q1_03 path: de/2016_q1_03-* - split: 2016_q2_05 path: de/2016_q2_05-* - split: 2016_q2_04 path: de/2016_q2_04-* - split: 2016_q2_06 path: de/2016_q2_06-* - split: 2016_q3_07 path: de/2016_q3_07-* - split: 2016_q3_08 path: de/2016_q3_08-* - split: 2016_q3_09 path: de/2016_q3_09-* - split: 2016_q4_10 path: de/2016_q4_10-* - split: 2016_q4_12 path: de/2016_q4_12-* - split: 2016_q4_11 path: de/2016_q4_11-* - split: 2017_q1_01 path: de/2017_q1_01-* - split: 2017_q1_02 path: de/2017_q1_02-* - split: 2017_q1_03 path: de/2017_q1_03-* - split: 2017_q2_04 path: de/2017_q2_04-* - split: 2017_q2_05 path: de/2017_q2_05-* - split: 2017_q2_06 path: de/2017_q2_06-* - split: 2017_q3_07 path: de/2017_q3_07-* - split: 2017_q3_08 path: de/2017_q3_08-* - split: 2017_q3_09 path: de/2017_q3_09-* - split: 2017_q4_11 path: de/2017_q4_11-* - split: 2017_q4_10 path: de/2017_q4_10-* - split: 2017_q4_12 path: de/2017_q4_12-* - split: 2018_q1_01 path: de/2018_q1_01-* - split: 2018_q1_02 path: de/2018_q1_02-* - split: 2018_q1_03 path: de/2018_q1_03-* - split: 2018_q2_04 path: de/2018_q2_04-* - split: 2018_q2_05 path: de/2018_q2_05-* - split: 2018_q2_06 path: de/2018_q2_06-* - split: 2018_q3_07 path: de/2018_q3_07-* - split: 2018_q3_08 path: de/2018_q3_08-* - split: 2018_q3_09 path: de/2018_q3_09-* - split: 2018_q4_10 path: de/2018_q4_10-* - split: 2018_q4_11 path: de/2018_q4_11-* - split: 2018_q4_12 path: de/2018_q4_12-* - split: 2019_q1_01 path: de/2019_q1_01-* - split: 2019_q1_02 path: de/2019_q1_02-* - split: 2019_q1_03 path: de/2019_q1_03-* - split: 2019_q2_04 path: de/2019_q2_04-* - split: 2019_q2_05 path: de/2019_q2_05-* - split: 2019_q2_06 path: de/2019_q2_06-* - split: 2019_q3_07 path: de/2019_q3_07-* - split: 2019_q3_08 path: de/2019_q3_08-* - split: 2019_q3_09 path: de/2019_q3_09-* - split: 2019_q4_10 path: de/2019_q4_10-* - split: 2019_q4_11 path: de/2019_q4_11-* - split: 2019_q4_12 path: de/2019_q4_12-* - split: 2020_q1_01 path: de/2020_q1_01-* - split: 2020_q1_02 path: de/2020_q1_02-* - split: 2021_q1_01 path: de/2021_q1_01-* - split: 2020_q1_03 path: de/2020_q1_03-* - split: 2020_q2_04 path: de/2020_q2_04-* - split: 2020_q2_05 path: de/2020_q2_05-* - split: 2020_q2_06 path: de/2020_q2_06-* - split: 2020_q3_07 path: de/2020_q3_07-* - split: 2020_q3_08 path: de/2020_q3_08-* - split: 2020_q4_10 path: de/2020_q4_10-* - split: 2020_q4_11 path: de/2020_q4_11-* - split: 2021_q2_06 path: de/2021_q2_06-* - split: 2021_q1_02 path: de/2021_q1_02-* - split: 2021_q1_03 path: de/2021_q1_03-* - split: 2021_q2_04 path: de/2021_q2_04-* - split: 2021_q2_05 path: de/2021_q2_05-* - split: 2021_q3_08 path: de/2021_q3_08-* - split: 2021_q3_09 path: de/2021_q3_09-* - split: 2021_q4_10 path: de/2021_q4_10-* - split: 2021_q4_11 path: de/2021_q4_11-* - split: 2021_q4_12 path: de/2021_q4_12-* - split: 2022_q1_01 path: de/2022_q1_01-* - split: 2022_q1_02 path: de/2022_q1_02-* - split: 2022_q1_03 path: de/2022_q1_03-* - split: 2022_q2_04 path: de/2022_q2_04-* - split: 2022_q2_05 path: de/2022_q2_05-* - split: 2022_q2_06 path: de/2022_q2_06-* - split: 2022_q3_07 path: de/2022_q3_07-* - split: 2022_q3_08 path: de/2022_q3_08-* - split: 2022_q3_09 path: de/2022_q3_09-* - split: 2022_q4_10 path: de/2022_q4_10-* - split: 2022_q4_11 path: de/2022_q4_11-* - split: 2022_q4_12 path: de/2022_q4_12-* - split: 2023_q1_01 path: de/2023_q1_01-* - split: 2023_q1_02 path: de/2023_q1_02-* - split: 2023_q1_03 path: de/2023_q1_03-* - split: 2023_q2_05 path: de/2023_q2_05-* - split: 2023_q2_06 path: de/2023_q2_06-* - split: 2023_q3_07 path: de/2023_q3_07-* - split: 2023_q3_09 path: de/2023_q3_09-* - split: 2023_q4_10 path: de/2023_q4_10-* - split: 2023_q4_11 path: de/2023_q4_11-* - split: 2023_q4_12 path: de/2023_q4_12-* - split: 2024_q1_01 path: de/2024_q1_01-* - split: '2004' path: de/2004_* - split: '2005' path: de/2005_* - split: '2006' path: de/2006_* - split: '2007' path: de/2007_* - split: '2008' path: de/2008_* - split: '2009' path: de/2009_* - split: '2010' path: de/2010_* - split: '2011' path: de/2011_* - split: '2012' path: de/2012_* - split: '2013' path: de/2013_* - split: '2014' path: de/2014_* - split: '2015' path: de/2015_* - split: '2016' path: de/2016_* - split: '2017' path: de/2017_* - split: '2018' path: de/2018_* - split: '2019' path: de/2019_* - split: '2020' path: de/2020_* - split: '2021' path: de/2021_* - split: '2022' path: de/2022_* - split: '2023' path: de/2023_* - split: '2024' path: de/2024_* - split: 2005_q2 path: de/2005_q2_* - split: 2016_q2 path: de/2016_q2_* - split: 2017_q1 path: de/2017_q1_* - split: 2010_q4 path: de/2010_q4_* - split: 2021_q1 path: de/2021_q1_* - split: 2014_q4 path: de/2014_q4_* - split: 2004_q3 path: de/2004_q3_* - split: 2015_q3 path: de/2015_q3_* - split: 2019_q3 path: de/2019_q3_* - split: 2005_q4 path: de/2005_q4_* - split: 2016_q4 path: de/2016_q4_* - split: 2017_q3 path: de/2017_q3_* - split: 2021_q3 path: de/2021_q3_* - split: 2006_q2 path: de/2006_q2_* - split: 2024_q1 path: de/2024_q1_* - split: 2011_q1 path: de/2011_q1_* - split: 2022_q1 path: de/2022_q1_* - split: 2008_q2 path: de/2008_q2_* - split: 2012_q2 path: de/2012_q2_* - split: 2023_q2 path: de/2023_q2_* - split: 2013_q1 path: de/2013_q1_* - split: 2006_q4 path: de/2006_q4_* - split: 2011_q3 path: de/2011_q3_* - split: 2022_q3 path: de/2022_q3_* - split: 2008_q4 path: de/2008_q4_* - split: 2012_q4 path: de/2012_q4_* - split: 2014_q1 path: de/2014_q1_* - split: 2013_q3 path: de/2013_q3_* - split: 2023_q4 path: de/2023_q4_* - split: 2007_q1 path: de/2007_q1_* - split: 2018_q1 path: de/2018_q1_* - split: 2015_q2 path: de/2015_q2_* - split: 2019_q2 path: de/2019_q2_* - split: 2009_q1 path: de/2009_q1_* - split: 2020_q1 path: de/2020_q1_* - split: 2017_q2 path: de/2017_q2_* - split: 2007_q3 path: de/2007_q3_* - split: 2018_q3 path: de/2018_q3_* - split: 2021_q2 path: de/2021_q2_* - split: 2004_q4 path: de/2004_q4_* - split: 2015_q4 path: de/2015_q4_* - split: 2019_q4 path: de/2019_q4_* - split: 2009_q3 path: de/2009_q3_* - split: 2020_q3 path: de/2020_q3_* - split: 2021_q4 path: de/2021_q4_* - split: 2010_q1 path: de/2010_q1_* - split: 2011_q2 path: de/2011_q2_* - split: 2022_q2 path: de/2022_q2_* - split: 2005_q1 path: de/2005_q1_* - split: 2016_q1 path: de/2016_q1_* - split: 2010_q3 path: de/2010_q3_* - split: 2013_q2 path: de/2013_q2_* - split: 2014_q3 path: de/2014_q3_* - split: 2011_q4 path: de/2011_q4_* - split: 2022_q4 path: de/2022_q4_* - split: 2005_q3 path: de/2005_q3_* - split: 2016_q3 path: de/2016_q3_* - split: 2013_q4 path: de/2013_q4_* - split: 2019_q1 path: de/2019_q1_* - split: 2006_q1 path: de/2006_q1_* - split: 2007_q2 path: de/2007_q2_* - split: 2017_q4 path: de/2017_q4_* - split: 2008_q1 path: de/2008_q1_* - split: 2018_q2 path: de/2018_q2_* - split: 2012_q1 path: de/2012_q1_* - split: 2023_q1 path: de/2023_q1_* - split: 2006_q3 path: de/2006_q3_* - split: 2009_q2 path: de/2009_q2_* - split: 2020_q2 path: de/2020_q2_* - split: 2007_q4 path: de/2007_q4_* - split: 2018_q4 path: de/2018_q4_* - split: 2008_q3 path: de/2008_q3_* - split: 2012_q3 path: de/2012_q3_* - split: 2023_q3 path: de/2023_q3_* - split: 2009_q4 path: de/2009_q4_* - split: 2020_q4 path: de/2020_q4_* - split: 2010_q2 path: de/2010_q2_* - split: 2014_q2 path: de/2014_q2_* - split: 2015_q1 path: de/2015_q1_* - config_name: en data_files: - split: 2004_q4_11 path: en/2004_q4_11-* - split: no_date path: en/no_date-* - split: 2004_q4_12 path: en/2004_q4_12-* - split: 2005_q1_01 path: en/2005_q1_01-* - split: 2007_q1_01 path: en/2007_q1_01-* - split: 2005_q1_02 path: en/2005_q1_02-* - split: 2005_q2_04 path: en/2005_q2_04-* - split: 2015_q3_08 path: en/2015_q3_08-* - split: 2005_q1_03 path: en/2005_q1_03-* - split: 2024_q1_03 path: en/2024_q1_03-* - split: 2024_q2_04 path: en/2024_q2_04-* - split: 2005_q2_05 path: en/2005_q2_05-* - split: 2005_q3_09 path: en/2005_q3_09-* - split: 2005_q2_06 path: en/2005_q2_06-* - split: 2005_q3_07 path: en/2005_q3_07-* - split: 2005_q3_08 path: en/2005_q3_08-* - split: 2006_q4_12 path: en/2006_q4_12-* - split: 2005_q4_10 path: en/2005_q4_10-* - split: 2005_q4_11 path: en/2005_q4_11-* - split: 2005_q4_12 path: en/2005_q4_12-* - split: 2006_q1_01 path: en/2006_q1_01-* - split: 2006_q1_03 path: en/2006_q1_03-* - split: 2006_q1_02 path: en/2006_q1_02-* - split: 2009_q1_03 path: en/2009_q1_03-* - split: 2006_q2_04 path: en/2006_q2_04-* - split: 2006_q2_05 path: en/2006_q2_05-* - split: 2006_q2_06 path: en/2006_q2_06-* - split: 2006_q3_07 path: en/2006_q3_07-* - split: 2006_q3_08 path: en/2006_q3_08-* - split: 2006_q4_10 path: en/2006_q4_10-* - split: 2006_q3_09 path: en/2006_q3_09-* - split: 2006_q4_11 path: en/2006_q4_11-* - split: 2007_q1_02 path: en/2007_q1_02-* - split: 2007_q1_03 path: en/2007_q1_03-* - split: 2007_q2_05 path: en/2007_q2_05-* - split: 2007_q2_04 path: en/2007_q2_04-* - split: 2007_q3_08 path: en/2007_q3_08-* - split: 2011_q4_10 path: en/2011_q4_10-* - split: 2008_q2_06 path: en/2008_q2_06-* - split: 2009_q4_11 path: en/2009_q4_11-* - split: 2007_q4_10 path: en/2007_q4_10-* - split: 2007_q2_06 path: en/2007_q2_06-* - split: 2007_q4_11 path: en/2007_q4_11-* - split: 2007_q3_07 path: en/2007_q3_07-* - split: 2007_q3_09 path: en/2007_q3_09-* - split: 2008_q1_01 path: en/2008_q1_01-* - split: 2007_q4_12 path: en/2007_q4_12-* - split: 2009_q1_01 path: en/2009_q1_01-* - split: 2008_q1_02 path: en/2008_q1_02-* - split: 2008_q1_03 path: en/2008_q1_03-* - split: 2008_q2_04 path: en/2008_q2_04-* - split: 2008_q3_08 path: en/2008_q3_08-* - split: 2008_q2_05 path: en/2008_q2_05-* - split: 2009_q3_08 path: en/2009_q3_08-* - split: 2008_q3_07 path: en/2008_q3_07-* - split: 2008_q3_09 path: en/2008_q3_09-* - split: 2009_q3_09 path: en/2009_q3_09-* - split: 2009_q3_07 path: en/2009_q3_07-* - split: 2008_q4_10 path: en/2008_q4_10-* - split: 2008_q4_11 path: en/2008_q4_11-* - split: 2008_q4_12 path: en/2008_q4_12-* - split: 2009_q1_02 path: en/2009_q1_02-* - split: 2009_q2_05 path: en/2009_q2_05-* - split: 2009_q2_04 path: en/2009_q2_04-* - split: 2009_q2_06 path: en/2009_q2_06-* - split: 2009_q4_10 path: en/2009_q4_10-* - split: 2009_q4_12 path: en/2009_q4_12-* - split: 2010_q1_01 path: en/2010_q1_01-* - split: 2010_q2_04 path: en/2010_q2_04-* - split: 2010_q1_02 path: en/2010_q1_02-* - split: 2010_q1_03 path: en/2010_q1_03-* - split: 2010_q4_12 path: en/2010_q4_12-* - split: 2010_q2_05 path: en/2010_q2_05-* - split: 2010_q3_09 path: en/2010_q3_09-* - split: 2010_q2_06 path: en/2010_q2_06-* - split: 2010_q3_07 path: en/2010_q3_07-* - split: 2010_q3_08 path: en/2010_q3_08-* - split: 2010_q4_10 path: en/2010_q4_10-* - split: 2010_q4_11 path: en/2010_q4_11-* - split: 2011_q4_12 path: en/2011_q4_12-* - split: 2011_q1_01 path: en/2011_q1_01-* - split: 2011_q1_02 path: en/2011_q1_02-* - split: 2011_q1_03 path: en/2011_q1_03-* - split: 2011_q2_04 path: en/2011_q2_04-* - split: 2011_q2_05 path: en/2011_q2_05-* - split: 2011_q2_06 path: en/2011_q2_06-* - split: 2011_q3_07 path: en/2011_q3_07-* - split: 2011_q3_08 path: en/2011_q3_08-* - split: 2011_q3_09 path: en/2011_q3_09-* - split: 2011_q4_11 path: en/2011_q4_11-* - split: 2012_q1_01 path: en/2012_q1_01-* - split: 2012_q2_05 path: en/2012_q2_05-* - split: 2012_q1_02 path: en/2012_q1_02-* - split: 2012_q1_03 path: en/2012_q1_03-* - split: 2012_q2_04 path: en/2012_q2_04-* - split: 2012_q2_06 path: en/2012_q2_06-* - split: 2012_q3_07 path: en/2012_q3_07-* - split: 2012_q3_08 path: en/2012_q3_08-* - split: 2012_q3_09 path: en/2012_q3_09-* - split: 2012_q4_10 path: en/2012_q4_10-* - split: 2012_q4_11 path: en/2012_q4_11-* - split: 2012_q4_12 path: en/2012_q4_12-* - split: 2013_q1_02 path: en/2013_q1_02-* - split: 2013_q1_01 path: en/2013_q1_01-* - split: 2013_q1_03 path: en/2013_q1_03-* - split: 2013_q2_04 path: en/2013_q2_04-* - split: 2013_q2_05 path: en/2013_q2_05-* - split: 2013_q2_06 path: en/2013_q2_06-* - split: 2013_q3_07 path: en/2013_q3_07-* - split: 2013_q3_08 path: en/2013_q3_08-* - split: 2013_q3_09 path: en/2013_q3_09-* - split: 2013_q4_10 path: en/2013_q4_10-* - split: 2013_q4_11 path: en/2013_q4_11-* - split: 2013_q4_12 path: en/2013_q4_12-* - split: 2014_q1_01 path: en/2014_q1_01-* - split: 2014_q1_02 path: en/2014_q1_02-* - split: 2014_q1_03 path: en/2014_q1_03-* - split: 2014_q2_04 path: en/2014_q2_04-* - split: 2014_q2_05 path: en/2014_q2_05-* - split: 2014_q2_06 path: en/2014_q2_06-* - split: 2014_q3_07 path: en/2014_q3_07-* - split: 2014_q3_08 path: en/2014_q3_08-* - split: 2014_q3_09 path: en/2014_q3_09-* - split: 2014_q4_11 path: en/2014_q4_11-* - split: 2014_q4_10 path: en/2014_q4_10-* - split: 2014_q4_12 path: en/2014_q4_12-* - split: 2015_q1_01 path: en/2015_q1_01-* - split: 2015_q1_02 path: en/2015_q1_02-* - split: 2015_q1_03 path: en/2015_q1_03-* - split: 2015_q2_04 path: en/2015_q2_04-* - split: 2015_q2_05 path: en/2015_q2_05-* - split: 2015_q2_06 path: en/2015_q2_06-* - split: 2015_q3_07 path: en/2015_q3_07-* - split: 2015_q3_09 path: en/2015_q3_09-* - split: 2015_q4_10 path: en/2015_q4_10-* - split: 2015_q4_11 path: en/2015_q4_11-* - split: 2015_q4_12 path: en/2015_q4_12-* - split: 2016_q1_01 path: en/2016_q1_01-* - split: 2016_q1_02 path: en/2016_q1_02-* - split: 2016_q1_03 path: en/2016_q1_03-* - split: 2016_q2_04 path: en/2016_q2_04-* - split: 2016_q2_05 path: en/2016_q2_05-* - split: 2016_q2_06 path: en/2016_q2_06-* - split: 2016_q3_07 path: en/2016_q3_07-* - split: 2016_q3_08 path: en/2016_q3_08-* - split: 2016_q3_09 path: en/2016_q3_09-* - split: 2016_q4_10 path: en/2016_q4_10-* - split: 2016_q4_11 path: en/2016_q4_11-* - split: 2016_q4_12 path: en/2016_q4_12-* - split: 2017_q1_01 path: en/2017_q1_01-* - split: 2017_q1_02 path: en/2017_q1_02-* - split: 2017_q1_03 path: en/2017_q1_03-* - split: 2017_q2_04 path: en/2017_q2_04-* - split: 2017_q2_05 path: en/2017_q2_05-* - split: 2018_q1_01 path: en/2018_q1_01-* - split: 2017_q2_06 path: en/2017_q2_06-* - split: 2017_q3_07 path: en/2017_q3_07-* - split: 2017_q3_08 path: en/2017_q3_08-* - split: 2017_q4_10 path: en/2017_q4_10-* - split: 2017_q3_09 path: en/2017_q3_09-* - split: 2017_q4_11 path: en/2017_q4_11-* - split: 2017_q4_12 path: en/2017_q4_12-* - split: 2018_q1_02 path: en/2018_q1_02-* - split: 2018_q1_03 path: en/2018_q1_03-* - split: 2018_q2_04 path: en/2018_q2_04-* - split: 2018_q2_05 path: en/2018_q2_05-* - split: 2018_q2_06 path: en/2018_q2_06-* - split: 2018_q3_07 path: en/2018_q3_07-* - split: 2018_q3_08 path: en/2018_q3_08-* - split: 2018_q3_09 path: en/2018_q3_09-* - split: 2018_q4_10 path: en/2018_q4_10-* - split: 2019_q1_01 path: en/2019_q1_01-* - split: 2018_q4_11 path: en/2018_q4_11-* - split: 2018_q4_12 path: en/2018_q4_12-* - split: 2019_q1_02 path: en/2019_q1_02-* - split: 2019_q1_03 path: en/2019_q1_03-* - split: 2019_q2_04 path: en/2019_q2_04-* - split: 2019_q2_05 path: en/2019_q2_05-* - split: 2019_q2_06 path: en/2019_q2_06-* - split: 2019_q3_07 path: en/2019_q3_07-* - split: 2019_q3_08 path: en/2019_q3_08-* - split: 2019_q3_09 path: en/2019_q3_09-* - split: 2019_q4_10 path: en/2019_q4_10-* - split: 2019_q4_11 path: en/2019_q4_11-* - split: 2019_q4_12 path: en/2019_q4_12-* - split: 2020_q1_01 path: en/2020_q1_01-* - split: 2020_q1_02 path: en/2020_q1_02-* - split: 2020_q1_03 path: en/2020_q1_03-* - split: 2020_q2_04 path: en/2020_q2_04-* - split: 2020_q3_08 path: en/2020_q3_08-* - split: 2020_q2_05 path: en/2020_q2_05-* - split: 2020_q2_06 path: en/2020_q2_06-* - split: 2020_q3_07 path: en/2020_q3_07-* - split: 2020_q3_09 path: en/2020_q3_09-* - split: 2020_q4_10 path: en/2020_q4_10-* - split: 2020_q4_12 path: en/2020_q4_12-* - split: 2020_q4_11 path: en/2020_q4_11-* - split: 2021_q2_04 path: en/2021_q2_04-* - split: 2021_q1_01 path: en/2021_q1_01-* - split: 2021_q1_02 path: en/2021_q1_02-* - split: 2021_q1_03 path: en/2021_q1_03-* - split: 2021_q2_05 path: en/2021_q2_05-* - split: 2021_q2_06 path: en/2021_q2_06-* - split: 2021_q3_07 path: en/2021_q3_07-* - split: 2021_q3_08 path: en/2021_q3_08-* - split: 2021_q3_09 path: en/2021_q3_09-* - split: 2021_q4_10 path: en/2021_q4_10-* - split: 2021_q4_11 path: en/2021_q4_11-* - split: 2022_q1_02 path: en/2022_q1_02-* - split: 2021_q4_12 path: en/2021_q4_12-* - split: 2022_q1_01 path: en/2022_q1_01-* - split: 2022_q1_03 path: en/2022_q1_03-* - split: 2022_q2_04 path: en/2022_q2_04-* - split: 2022_q2_05 path: en/2022_q2_05-* - split: 2022_q2_06 path: en/2022_q2_06-* - split: 2022_q3_07 path: en/2022_q3_07-* - split: 2022_q3_08 path: en/2022_q3_08-* - split: 2022_q3_09 path: en/2022_q3_09-* - split: 2022_q4_11 path: en/2022_q4_11-* - split: 2022_q4_10 path: en/2022_q4_10-* - split: 2022_q4_12 path: en/2022_q4_12-* - split: 2023_q1_01 path: en/2023_q1_01-* - split: 2023_q1_02 path: en/2023_q1_02-* - split: 2023_q1_03 path: en/2023_q1_03-* - split: 2023_q2_04 path: en/2023_q2_04-* - split: 2023_q2_05 path: en/2023_q2_05-* - split: 2023_q2_06 path: en/2023_q2_06-* - split: 2023_q3_07 path: en/2023_q3_07-* - split: 2023_q3_08 path: en/2023_q3_08-* - split: 2023_q3_09 path: en/2023_q3_09-* - split: 2023_q4_10 path: en/2023_q4_10-* - split: 2023_q4_12 path: en/2023_q4_12-* - split: 2023_q4_11 path: en/2023_q4_11-* - split: 2024_q1_01 path: en/2024_q1_01-* - split: '2004' path: en/2004_* - split: '2005' path: en/2005_* - split: '2006' path: en/2006_* - split: '2007' path: en/2007_* - split: '2008' path: en/2008_* - split: '2009' path: en/2009_* - split: '2010' path: en/2010_* - split: '2011' path: en/2011_* - split: '2012' path: en/2012_* - split: '2013' path: en/2013_* - split: '2014' path: en/2014_* - split: '2015' path: en/2015_* - split: '2016' path: en/2016_* - split: '2017' path: en/2017_* - split: '2018' path: en/2018_* - split: '2019' path: en/2019_* - split: '2020' path: en/2020_* - split: '2021' path: en/2021_* - split: '2022' path: en/2022_* - split: '2023' path: en/2023_* - split: '2024' path: en/2024_* - split: 2005_q2 path: en/2005_q2_* - split: 2016_q2 path: en/2016_q2_* - split: 2017_q1 path: en/2017_q1_* - split: 2010_q4 path: en/2010_q4_* - split: 2021_q1 path: en/2021_q1_* - split: 2014_q4 path: en/2014_q4_* - split: 2015_q3 path: en/2015_q3_* - split: 2019_q3 path: en/2019_q3_* - split: 2005_q4 path: en/2005_q4_* - split: 2016_q4 path: en/2016_q4_* - split: 2017_q3 path: en/2017_q3_* - split: 2021_q3 path: en/2021_q3_* - split: 2006_q2 path: en/2006_q2_* - split: 2024_q1 path: en/2024_q1_* - split: 2011_q1 path: en/2011_q1_* - split: 2022_q1 path: en/2022_q1_* - split: 2008_q2 path: en/2008_q2_* - split: 2012_q2 path: en/2012_q2_* - split: 2023_q2 path: en/2023_q2_* - split: 2013_q1 path: en/2013_q1_* - split: 2006_q4 path: en/2006_q4_* - split: 2011_q3 path: en/2011_q3_* - split: 2022_q3 path: en/2022_q3_* - split: 2008_q4 path: en/2008_q4_* - split: 2012_q4 path: en/2012_q4_* - split: 2014_q1 path: en/2014_q1_* - split: 2013_q3 path: en/2013_q3_* - split: 2023_q4 path: en/2023_q4_* - split: 2007_q1 path: en/2007_q1_* - split: 2018_q1 path: en/2018_q1_* - split: 2015_q2 path: en/2015_q2_* - split: 2019_q2 path: en/2019_q2_* - split: 2009_q1 path: en/2009_q1_* - split: 2020_q1 path: en/2020_q1_* - split: 2017_q2 path: en/2017_q2_* - split: 2007_q3 path: en/2007_q3_* - split: 2018_q3 path: en/2018_q3_* - split: 2021_q2 path: en/2021_q2_* - split: 2004_q4 path: en/2004_q4_* - split: 2015_q4 path: en/2015_q4_* - split: 2019_q4 path: en/2019_q4_* - split: 2009_q3 path: en/2009_q3_* - split: 2020_q3 path: en/2020_q3_* - split: 2021_q4 path: en/2021_q4_* - split: 2010_q1 path: en/2010_q1_* - split: 2011_q2 path: en/2011_q2_* - split: 2022_q2 path: en/2022_q2_* - split: 2005_q1 path: en/2005_q1_* - split: 2016_q1 path: en/2016_q1_* - split: 2010_q3 path: en/2010_q3_* - split: 2013_q2 path: en/2013_q2_* - split: 2014_q3 path: en/2014_q3_* - split: 2011_q4 path: en/2011_q4_* - split: 2022_q4 path: en/2022_q4_* - split: 2005_q3 path: en/2005_q3_* - split: 2016_q3 path: en/2016_q3_* - split: 2013_q4 path: en/2013_q4_* - split: 2019_q1 path: en/2019_q1_* - split: 2006_q1 path: en/2006_q1_* - split: 2007_q2 path: en/2007_q2_* - split: 2017_q4 path: en/2017_q4_* - split: 2008_q1 path: en/2008_q1_* - split: 2018_q2 path: en/2018_q2_* - split: 2012_q1 path: en/2012_q1_* - split: 2023_q1 path: en/2023_q1_* - split: 2006_q3 path: en/2006_q3_* - split: 2009_q2 path: en/2009_q2_* - split: 2020_q2 path: en/2020_q2_* - split: 2024_q2 path: en/2024_q2_* - split: 2007_q4 path: en/2007_q4_* - split: 2018_q4 path: en/2018_q4_* - split: 2008_q3 path: en/2008_q3_* - split: 2012_q3 path: en/2012_q3_* - split: 2023_q3 path: en/2023_q3_* - split: 2009_q4 path: en/2009_q4_* - split: 2020_q4 path: en/2020_q4_* - split: 2010_q2 path: en/2010_q2_* - split: 2014_q2 path: en/2014_q2_* - split: 2015_q1 path: en/2015_q1_* - config_name: es data_files: - split: 2005_q1_01 path: es/2005_q1_01-* - split: 2005_q1_02 path: es/2005_q1_02-* - split: 2004_q1_02 path: es/2004_q1_02-* - split: 2005_q1_03 path: es/2005_q1_03-* - split: no_date path: es/no_date-* - split: 2005_q2_04 path: es/2005_q2_04-* - split: 2005_q2_05 path: es/2005_q2_05-* - split: 2005_q2_06 path: es/2005_q2_06-* - split: 2005_q3_07 path: es/2005_q3_07-* - split: 2005_q3_08 path: es/2005_q3_08-* - split: 2005_q3_09 path: es/2005_q3_09-* - split: 2005_q4_10 path: es/2005_q4_10-* - split: 2005_q4_12 path: es/2005_q4_12-* - split: 2006_q4_10 path: es/2006_q4_10-* - split: 2005_q4_11 path: es/2005_q4_11-* - split: 2006_q1_01 path: es/2006_q1_01-* - split: 2006_q1_02 path: es/2006_q1_02-* - split: 2006_q1_03 path: es/2006_q1_03-* - split: 2006_q2_04 path: es/2006_q2_04-* - split: 2006_q2_05 path: es/2006_q2_05-* - split: 2006_q2_06 path: es/2006_q2_06-* - split: 2006_q3_07 path: es/2006_q3_07-* - split: 2006_q3_08 path: es/2006_q3_08-* - split: 2006_q3_09 path: es/2006_q3_09-* - split: 2006_q4_11 path: es/2006_q4_11-* - split: 2006_q4_12 path: es/2006_q4_12-* - split: 2007_q1_01 path: es/2007_q1_01-* - split: 2007_q1_02 path: es/2007_q1_02-* - split: 2007_q1_03 path: es/2007_q1_03-* - split: 2007_q2_04 path: es/2007_q2_04-* - split: 2007_q2_05 path: es/2007_q2_05-* - split: 2007_q2_06 path: es/2007_q2_06-* - split: 2007_q3_07 path: es/2007_q3_07-* - split: 2007_q3_08 path: es/2007_q3_08-* - split: 2007_q3_09 path: es/2007_q3_09-* - split: 2007_q4_10 path: es/2007_q4_10-* - split: 2007_q4_11 path: es/2007_q4_11-* - split: 2007_q4_12 path: es/2007_q4_12-* - split: 2008_q1_01 path: es/2008_q1_01-* - split: 2008_q1_02 path: es/2008_q1_02-* - split: 2008_q1_03 path: es/2008_q1_03-* - split: 2008_q2_04 path: es/2008_q2_04-* - split: 2008_q2_05 path: es/2008_q2_05-* - split: 2008_q2_06 path: es/2008_q2_06-* - split: 2008_q3_07 path: es/2008_q3_07-* - split: 2008_q3_08 path: es/2008_q3_08-* - split: 2008_q3_09 path: es/2008_q3_09-* - split: 2008_q4_10 path: es/2008_q4_10-* - split: 2008_q4_11 path: es/2008_q4_11-* - split: 2008_q4_12 path: es/2008_q4_12-* - split: 2009_q1_01 path: es/2009_q1_01-* - split: 2009_q1_02 path: es/2009_q1_02-* - split: 2009_q1_03 path: es/2009_q1_03-* - split: 2009_q2_04 path: es/2009_q2_04-* - split: 2009_q2_05 path: es/2009_q2_05-* - split: 2009_q2_06 path: es/2009_q2_06-* - split: 2009_q3_07 path: es/2009_q3_07-* - split: 2009_q3_08 path: es/2009_q3_08-* - split: 2009_q3_09 path: es/2009_q3_09-* - split: 2009_q4_10 path: es/2009_q4_10-* - split: 2009_q4_11 path: es/2009_q4_11-* - split: 2009_q4_12 path: es/2009_q4_12-* - split: 2010_q1_01 path: es/2010_q1_01-* - split: 2010_q1_02 path: es/2010_q1_02-* - split: 2010_q1_03 path: es/2010_q1_03-* - split: 2011_q1_02 path: es/2011_q1_02-* - split: 2010_q2_04 path: es/2010_q2_04-* - split: 2010_q2_05 path: es/2010_q2_05-* - split: 2010_q2_06 path: es/2010_q2_06-* - split: 2010_q3_07 path: es/2010_q3_07-* - split: 2010_q3_08 path: es/2010_q3_08-* - split: 2010_q3_09 path: es/2010_q3_09-* - split: 2010_q4_10 path: es/2010_q4_10-* - split: 2010_q4_11 path: es/2010_q4_11-* - split: 2010_q4_12 path: es/2010_q4_12-* - split: 2011_q1_01 path: es/2011_q1_01-* - split: 2013_q2_04 path: es/2013_q2_04-* - split: 2011_q1_03 path: es/2011_q1_03-* - split: 2011_q2_04 path: es/2011_q2_04-* - split: 2011_q2_05 path: es/2011_q2_05-* - split: 2011_q2_06 path: es/2011_q2_06-* - split: 2011_q3_07 path: es/2011_q3_07-* - split: 2011_q3_08 path: es/2011_q3_08-* - split: 2011_q3_09 path: es/2011_q3_09-* - split: 2011_q4_10 path: es/2011_q4_10-* - split: 2011_q4_11 path: es/2011_q4_11-* - split: 2011_q4_12 path: es/2011_q4_12-* - split: 2012_q1_01 path: es/2012_q1_01-* - split: 2012_q1_02 path: es/2012_q1_02-* - split: 2012_q1_03 path: es/2012_q1_03-* - split: 2012_q2_04 path: es/2012_q2_04-* - split: 2012_q2_05 path: es/2012_q2_05-* - split: 2012_q2_06 path: es/2012_q2_06-* - split: 2012_q3_07 path: es/2012_q3_07-* - split: 2012_q3_08 path: es/2012_q3_08-* - split: 2012_q3_09 path: es/2012_q3_09-* - split: 2012_q4_10 path: es/2012_q4_10-* - split: 2012_q4_11 path: es/2012_q4_11-* - split: 2012_q4_12 path: es/2012_q4_12-* - split: 2013_q1_01 path: es/2013_q1_01-* - split: 2013_q1_02 path: es/2013_q1_02-* - split: 2013_q1_03 path: es/2013_q1_03-* - split: 2013_q2_05 path: es/2013_q2_05-* - split: 2013_q2_06 path: es/2013_q2_06-* - split: 2013_q3_07 path: es/2013_q3_07-* - split: 2013_q3_08 path: es/2013_q3_08-* - split: 2013_q3_09 path: es/2013_q3_09-* - split: 2013_q4_11 path: es/2013_q4_11-* - split: 2013_q4_10 path: es/2013_q4_10-* - split: 2013_q4_12 path: es/2013_q4_12-* - split: 2014_q1_01 path: es/2014_q1_01-* - split: 2014_q1_02 path: es/2014_q1_02-* - split: 2024_q1_02 path: es/2024_q1_02-* - split: 2014_q1_03 path: es/2014_q1_03-* - split: 2014_q2_04 path: es/2014_q2_04-* - split: 2014_q2_05 path: es/2014_q2_05-* - split: 2014_q2_06 path: es/2014_q2_06-* - split: 2014_q3_07 path: es/2014_q3_07-* - split: 2014_q3_08 path: es/2014_q3_08-* - split: 2014_q3_09 path: es/2014_q3_09-* - split: 2014_q4_10 path: es/2014_q4_10-* - split: 2014_q4_11 path: es/2014_q4_11-* - split: 2014_q4_12 path: es/2014_q4_12-* - split: 2015_q1_01 path: es/2015_q1_01-* - split: 2015_q1_02 path: es/2015_q1_02-* - split: 2015_q1_03 path: es/2015_q1_03-* - split: 2015_q2_04 path: es/2015_q2_04-* - split: 2015_q2_05 path: es/2015_q2_05-* - split: 2015_q2_06 path: es/2015_q2_06-* - split: 2015_q3_07 path: es/2015_q3_07-* - split: 2015_q3_08 path: es/2015_q3_08-* - split: 2015_q3_09 path: es/2015_q3_09-* - split: 2015_q4_10 path: es/2015_q4_10-* - split: 2015_q4_11 path: es/2015_q4_11-* - split: 2015_q4_12 path: es/2015_q4_12-* - split: 2016_q1_01 path: es/2016_q1_01-* - split: 2016_q1_02 path: es/2016_q1_02-* - split: 2016_q1_03 path: es/2016_q1_03-* - split: 2016_q2_04 path: es/2016_q2_04-* - split: 2016_q2_05 path: es/2016_q2_05-* - split: 2016_q2_06 path: es/2016_q2_06-* - split: 2016_q3_07 path: es/2016_q3_07-* - split: 2016_q3_08 path: es/2016_q3_08-* - split: 2016_q3_09 path: es/2016_q3_09-* - split: 2016_q4_10 path: es/2016_q4_10-* - split: 2016_q4_11 path: es/2016_q4_11-* - split: 2016_q4_12 path: es/2016_q4_12-* - split: 2017_q1_01 path: es/2017_q1_01-* - split: 2017_q1_02 path: es/2017_q1_02-* - split: 2017_q1_03 path: es/2017_q1_03-* - split: 2017_q2_04 path: es/2017_q2_04-* - split: 2017_q2_05 path: es/2017_q2_05-* - split: 2017_q2_06 path: es/2017_q2_06-* - split: 2017_q3_07 path: es/2017_q3_07-* - split: 2017_q3_08 path: es/2017_q3_08-* - split: 2017_q3_09 path: es/2017_q3_09-* - split: 2017_q4_10 path: es/2017_q4_10-* - split: 2017_q4_11 path: es/2017_q4_11-* - split: 2017_q4_12 path: es/2017_q4_12-* - split: 2018_q1_01 path: es/2018_q1_01-* - split: 2018_q1_02 path: es/2018_q1_02-* - split: 2018_q1_03 path: es/2018_q1_03-* - split: 2018_q2_04 path: es/2018_q2_04-* - split: 2018_q2_05 path: es/2018_q2_05-* - split: 2018_q2_06 path: es/2018_q2_06-* - split: 2018_q3_07 path: es/2018_q3_07-* - split: 2018_q3_08 path: es/2018_q3_08-* - split: 2018_q3_09 path: es/2018_q3_09-* - split: 2018_q4_10 path: es/2018_q4_10-* - split: 2018_q4_11 path: es/2018_q4_11-* - split: 2018_q4_12 path: es/2018_q4_12-* - split: 2019_q1_01 path: es/2019_q1_01-* - split: 2019_q1_02 path: es/2019_q1_02-* - split: 2019_q1_03 path: es/2019_q1_03-* - split: 2019_q2_04 path: es/2019_q2_04-* - split: 2019_q2_05 path: es/2019_q2_05-* - split: 2019_q2_06 path: es/2019_q2_06-* - split: 2019_q3_07 path: es/2019_q3_07-* - split: 2019_q3_08 path: es/2019_q3_08-* - split: 2019_q3_09 path: es/2019_q3_09-* - split: 2019_q4_10 path: es/2019_q4_10-* - split: 2019_q4_11 path: es/2019_q4_11-* - split: 2019_q4_12 path: es/2019_q4_12-* - split: 2020_q1_01 path: es/2020_q1_01-* - split: 2020_q1_02 path: es/2020_q1_02-* - split: 2020_q1_03 path: es/2020_q1_03-* - split: 2020_q2_04 path: es/2020_q2_04-* - split: 2020_q2_05 path: es/2020_q2_05-* - split: 2020_q2_06 path: es/2020_q2_06-* - split: 2020_q3_07 path: es/2020_q3_07-* - split: 2020_q3_08 path: es/2020_q3_08-* - split: 2020_q3_09 path: es/2020_q3_09-* - split: 2020_q4_10 path: es/2020_q4_10-* - split: 2020_q4_11 path: es/2020_q4_11-* - split: 2020_q4_12 path: es/2020_q4_12-* - split: 2021_q1_01 path: es/2021_q1_01-* - split: 2021_q1_02 path: es/2021_q1_02-* - split: 2021_q1_03 path: es/2021_q1_03-* - split: 2021_q2_04 path: es/2021_q2_04-* - split: 2021_q2_05 path: es/2021_q2_05-* - split: 2021_q2_06 path: es/2021_q2_06-* - split: 2021_q3_07 path: es/2021_q3_07-* - split: 2021_q3_08 path: es/2021_q3_08-* - split: 2021_q3_09 path: es/2021_q3_09-* - split: 2021_q4_10 path: es/2021_q4_10-* - split: 2021_q4_11 path: es/2021_q4_11-* - split: 2021_q4_12 path: es/2021_q4_12-* - split: 2022_q1_01 path: es/2022_q1_01-* - split: 2022_q1_02 path: es/2022_q1_02-* - split: 2022_q1_03 path: es/2022_q1_03-* - split: 2022_q2_04 path: es/2022_q2_04-* - split: 2022_q2_05 path: es/2022_q2_05-* - split: 2022_q2_06 path: es/2022_q2_06-* - split: 2022_q3_07 path: es/2022_q3_07-* - split: 2022_q3_08 path: es/2022_q3_08-* - split: 2022_q3_09 path: es/2022_q3_09-* - split: 2022_q4_10 path: es/2022_q4_10-* - split: 2022_q4_11 path: es/2022_q4_11-* - split: 2022_q4_12 path: es/2022_q4_12-* - split: 2023_q1_01 path: es/2023_q1_01-* - split: 2023_q1_02 path: es/2023_q1_02-* - split: 2023_q1_03 path: es/2023_q1_03-* - split: 2023_q2_04 path: es/2023_q2_04-* - split: 2023_q2_05 path: es/2023_q2_05-* - split: 2023_q2_06 path: es/2023_q2_06-* - split: 2023_q3_07 path: es/2023_q3_07-* - split: 2023_q3_08 path: es/2023_q3_08-* - split: 2023_q3_09 path: es/2023_q3_09-* - split: 2023_q4_10 path: es/2023_q4_10-* - split: 2023_q4_11 path: es/2023_q4_11-* - split: 2023_q4_12 path: es/2023_q4_12-* - split: 2024_q1_01 path: es/2024_q1_01-* - split: '2004' path: es/2004_* - split: '2005' path: es/2005_* - split: '2006' path: es/2006_* - split: '2007' path: es/2007_* - split: '2008' path: es/2008_* - split: '2009' path: es/2009_* - split: '2010' path: es/2010_* - split: '2011' path: es/2011_* - split: '2012' path: es/2012_* - split: '2013' path: es/2013_* - split: '2014' path: es/2014_* - split: '2015' path: es/2015_* - split: '2016' path: es/2016_* - split: '2017' path: es/2017_* - split: '2018' path: es/2018_* - split: '2019' path: es/2019_* - split: '2020' path: es/2020_* - split: '2021' path: es/2021_* - split: '2022' path: es/2022_* - split: '2023' path: es/2023_* - split: '2024' path: es/2024_* - split: 2005_q2 path: es/2005_q2_* - split: 2016_q2 path: es/2016_q2_* - split: 2017_q1 path: es/2017_q1_* - split: 2010_q4 path: es/2010_q4_* - split: 2021_q1 path: es/2021_q1_* - split: 2014_q4 path: es/2014_q4_* - split: 2015_q3 path: es/2015_q3_* - split: 2019_q3 path: es/2019_q3_* - split: 2005_q4 path: es/2005_q4_* - split: 2016_q4 path: es/2016_q4_* - split: 2017_q3 path: es/2017_q3_* - split: 2021_q3 path: es/2021_q3_* - split: 2006_q2 path: es/2006_q2_* - split: 2024_q1 path: es/2024_q1_* - split: 2011_q1 path: es/2011_q1_* - split: 2022_q1 path: es/2022_q1_* - split: 2008_q2 path: es/2008_q2_* - split: 2012_q2 path: es/2012_q2_* - split: 2023_q2 path: es/2023_q2_* - split: 2013_q1 path: es/2013_q1_* - split: 2006_q4 path: es/2006_q4_* - split: 2011_q3 path: es/2011_q3_* - split: 2022_q3 path: es/2022_q3_* - split: 2008_q4 path: es/2008_q4_* - split: 2012_q4 path: es/2012_q4_* - split: 2014_q1 path: es/2014_q1_* - split: 2013_q3 path: es/2013_q3_* - split: 2023_q4 path: es/2023_q4_* - split: 2007_q1 path: es/2007_q1_* - split: 2018_q1 path: es/2018_q1_* - split: 2015_q2 path: es/2015_q2_* - split: 2019_q2 path: es/2019_q2_* - split: 2009_q1 path: es/2009_q1_* - split: 2020_q1 path: es/2020_q1_* - split: 2017_q2 path: es/2017_q2_* - split: 2007_q3 path: es/2007_q3_* - split: 2018_q3 path: es/2018_q3_* - split: 2021_q2 path: es/2021_q2_* - split: 2015_q4 path: es/2015_q4_* - split: 2019_q4 path: es/2019_q4_* - split: 2009_q3 path: es/2009_q3_* - split: 2020_q3 path: es/2020_q3_* - split: 2021_q4 path: es/2021_q4_* - split: 2010_q1 path: es/2010_q1_* - split: 2011_q2 path: es/2011_q2_* - split: 2022_q2 path: es/2022_q2_* - split: 2005_q1 path: es/2005_q1_* - split: 2016_q1 path: es/2016_q1_* - split: 2010_q3 path: es/2010_q3_* - split: 2013_q2 path: es/2013_q2_* - split: 2014_q3 path: es/2014_q3_* - split: 2011_q4 path: es/2011_q4_* - split: 2022_q4 path: es/2022_q4_* - split: 2005_q3 path: es/2005_q3_* - split: 2016_q3 path: es/2016_q3_* - split: 2013_q4 path: es/2013_q4_* - split: 2019_q1 path: es/2019_q1_* - split: 2006_q1 path: es/2006_q1_* - split: 2004_q1 path: es/2004_q1_* - split: 2007_q2 path: es/2007_q2_* - split: 2017_q4 path: es/2017_q4_* - split: 2008_q1 path: es/2008_q1_* - split: 2018_q2 path: es/2018_q2_* - split: 2012_q1 path: es/2012_q1_* - split: 2023_q1 path: es/2023_q1_* - split: 2006_q3 path: es/2006_q3_* - split: 2009_q2 path: es/2009_q2_* - split: 2020_q2 path: es/2020_q2_* - split: 2007_q4 path: es/2007_q4_* - split: 2018_q4 path: es/2018_q4_* - split: 2008_q3 path: es/2008_q3_* - split: 2012_q3 path: es/2012_q3_* - split: 2023_q3 path: es/2023_q3_* - split: 2009_q4 path: es/2009_q4_* - split: 2020_q4 path: es/2020_q4_* - split: 2010_q2 path: es/2010_q2_* - split: 2014_q2 path: es/2014_q2_* - split: 2015_q1 path: es/2015_q1_* - config_name: fr data_files: - split: 2005_q1_01 path: fr/2005_q1_01-* - split: 2005_q1_02 path: fr/2005_q1_02-* - split: 2005_q1_03 path: fr/2005_q1_03-* - split: 2005_q2_04 path: fr/2005_q2_04-* - split: 2005_q2_05 path: fr/2005_q2_05-* - split: 2005_q2_06 path: fr/2005_q2_06-* - split: 2005_q3_07 path: fr/2005_q3_07-* - split: 2005_q3_08 path: fr/2005_q3_08-* - split: 2005_q3_09 path: fr/2005_q3_09-* - split: 2005_q4_10 path: fr/2005_q4_10-* - split: 2005_q4_11 path: fr/2005_q4_11-* - split: 2005_q4_12 path: fr/2005_q4_12-* - split: 2006_q1_01 path: fr/2006_q1_01-* - split: 2006_q1_02 path: fr/2006_q1_02-* - split: 2006_q1_03 path: fr/2006_q1_03-* - split: 2006_q2_04 path: fr/2006_q2_04-* - split: 2006_q2_05 path: fr/2006_q2_05-* - split: 2006_q2_06 path: fr/2006_q2_06-* - split: 2006_q3_07 path: fr/2006_q3_07-* - split: 2006_q3_08 path: fr/2006_q3_08-* - split: 2006_q3_09 path: fr/2006_q3_09-* - split: 2006_q4_10 path: fr/2006_q4_10-* - split: 2006_q4_11 path: fr/2006_q4_11-* - split: 2006_q4_12 path: fr/2006_q4_12-* - split: 2007_q1_01 path: fr/2007_q1_01-* - split: 2007_q1_02 path: fr/2007_q1_02-* - split: 2007_q1_03 path: fr/2007_q1_03-* - split: 2007_q2_04 path: fr/2007_q2_04-* - split: 2007_q2_05 path: fr/2007_q2_05-* - split: no_date path: fr/no_date-* - split: 2007_q2_06 path: fr/2007_q2_06-* - split: 2007_q3_07 path: fr/2007_q3_07-* - split: 2007_q3_08 path: fr/2007_q3_08-* - split: 2007_q3_09 path: fr/2007_q3_09-* - split: 2007_q4_10 path: fr/2007_q4_10-* - split: 2007_q4_11 path: fr/2007_q4_11-* - split: 2007_q4_12 path: fr/2007_q4_12-* - split: 2008_q1_01 path: fr/2008_q1_01-* - split: 2008_q1_02 path: fr/2008_q1_02-* - split: 2008_q1_03 path: fr/2008_q1_03-* - split: 2008_q2_04 path: fr/2008_q2_04-* - split: 2008_q2_05 path: fr/2008_q2_05-* - split: 2008_q2_06 path: fr/2008_q2_06-* - split: 2008_q3_07 path: fr/2008_q3_07-* - split: 2008_q3_08 path: fr/2008_q3_08-* - split: 2008_q3_09 path: fr/2008_q3_09-* - split: 2008_q4_10 path: fr/2008_q4_10-* - split: 2008_q4_11 path: fr/2008_q4_11-* - split: 2008_q4_12 path: fr/2008_q4_12-* - split: 2009_q1_01 path: fr/2009_q1_01-* - split: 2009_q1_02 path: fr/2009_q1_02-* - split: 2009_q1_03 path: fr/2009_q1_03-* - split: 2009_q2_04 path: fr/2009_q2_04-* - split: 2009_q2_05 path: fr/2009_q2_05-* - split: 2009_q2_06 path: fr/2009_q2_06-* - split: 2009_q3_07 path: fr/2009_q3_07-* - split: 2009_q3_08 path: fr/2009_q3_08-* - split: 2009_q3_09 path: fr/2009_q3_09-* - split: 2011_q2_04 path: fr/2011_q2_04-* - split: 2009_q4_10 path: fr/2009_q4_10-* - split: 2009_q4_11 path: fr/2009_q4_11-* - split: 2009_q4_12 path: fr/2009_q4_12-* - split: 2010_q1_01 path: fr/2010_q1_01-* - split: 2010_q1_02 path: fr/2010_q1_02-* - split: 2010_q1_03 path: fr/2010_q1_03-* - split: 2010_q2_04 path: fr/2010_q2_04-* - split: 2010_q2_05 path: fr/2010_q2_05-* - split: 2010_q2_06 path: fr/2010_q2_06-* - split: 2010_q3_07 path: fr/2010_q3_07-* - split: 2010_q3_08 path: fr/2010_q3_08-* - split: 2010_q3_09 path: fr/2010_q3_09-* - split: 2010_q4_10 path: fr/2010_q4_10-* - split: 2010_q4_11 path: fr/2010_q4_11-* - split: 2010_q4_12 path: fr/2010_q4_12-* - split: 2011_q1_01 path: fr/2011_q1_01-* - split: 2011_q1_02 path: fr/2011_q1_02-* - split: 2011_q1_03 path: fr/2011_q1_03-* - split: 2011_q2_05 path: fr/2011_q2_05-* - split: 2011_q2_06 path: fr/2011_q2_06-* - split: 2011_q3_07 path: fr/2011_q3_07-* - split: 2011_q3_08 path: fr/2011_q3_08-* - split: 2011_q3_09 path: fr/2011_q3_09-* - split: 2011_q4_10 path: fr/2011_q4_10-* - split: 2011_q4_11 path: fr/2011_q4_11-* - split: 2011_q4_12 path: fr/2011_q4_12-* - split: 2012_q1_01 path: fr/2012_q1_01-* - split: 2012_q1_02 path: fr/2012_q1_02-* - split: 2012_q1_03 path: fr/2012_q1_03-* - split: 2012_q2_04 path: fr/2012_q2_04-* - split: 2012_q2_05 path: fr/2012_q2_05-* - split: 2012_q2_06 path: fr/2012_q2_06-* - split: 2012_q3_07 path: fr/2012_q3_07-* - split: 2012_q3_08 path: fr/2012_q3_08-* - split: 2024_q2_04 path: fr/2024_q2_04-* - split: 2012_q3_09 path: fr/2012_q3_09-* - split: 2012_q4_10 path: fr/2012_q4_10-* - split: 2012_q4_11 path: fr/2012_q4_11-* - split: 2012_q4_12 path: fr/2012_q4_12-* - split: 2013_q1_01 path: fr/2013_q1_01-* - split: 2013_q1_02 path: fr/2013_q1_02-* - split: 2013_q1_03 path: fr/2013_q1_03-* - split: 2013_q2_04 path: fr/2013_q2_04-* - split: 2013_q2_05 path: fr/2013_q2_05-* - split: 2013_q2_06 path: fr/2013_q2_06-* - split: 2013_q3_07 path: fr/2013_q3_07-* - split: 2013_q3_08 path: fr/2013_q3_08-* - split: 2013_q3_09 path: fr/2013_q3_09-* - split: 2013_q4_10 path: fr/2013_q4_10-* - split: 2013_q4_11 path: fr/2013_q4_11-* - split: 2013_q4_12 path: fr/2013_q4_12-* - split: 2014_q1_01 path: fr/2014_q1_01-* - split: 2014_q1_02 path: fr/2014_q1_02-* - split: 2014_q1_03 path: fr/2014_q1_03-* - split: 2024_q1_02 path: fr/2024_q1_02-* - split: 2014_q2_04 path: fr/2014_q2_04-* - split: 2014_q2_05 path: fr/2014_q2_05-* - split: 2014_q2_06 path: fr/2014_q2_06-* - split: 2014_q3_07 path: fr/2014_q3_07-* - split: 2014_q3_08 path: fr/2014_q3_08-* - split: 2014_q3_09 path: fr/2014_q3_09-* - split: 2014_q4_10 path: fr/2014_q4_10-* - split: 2014_q4_11 path: fr/2014_q4_11-* - split: 2014_q4_12 path: fr/2014_q4_12-* - split: 2015_q1_01 path: fr/2015_q1_01-* - split: 2015_q1_02 path: fr/2015_q1_02-* - split: 2015_q1_03 path: fr/2015_q1_03-* - split: 2015_q3_09 path: fr/2015_q3_09-* - split: 2015_q2_04 path: fr/2015_q2_04-* - split: 2015_q2_05 path: fr/2015_q2_05-* - split: 2015_q2_06 path: fr/2015_q2_06-* - split: 2016_q3_08 path: fr/2016_q3_08-* - split: 2015_q3_07 path: fr/2015_q3_07-* - split: 2015_q3_08 path: fr/2015_q3_08-* - split: 2015_q4_10 path: fr/2015_q4_10-* - split: 2015_q4_11 path: fr/2015_q4_11-* - split: 2015_q4_12 path: fr/2015_q4_12-* - split: 2016_q1_01 path: fr/2016_q1_01-* - split: 2016_q1_02 path: fr/2016_q1_02-* - split: 2016_q1_03 path: fr/2016_q1_03-* - split: 2016_q2_04 path: fr/2016_q2_04-* - split: 2016_q2_05 path: fr/2016_q2_05-* - split: 2016_q2_06 path: fr/2016_q2_06-* - split: 2016_q3_07 path: fr/2016_q3_07-* - split: 2016_q3_09 path: fr/2016_q3_09-* - split: 2020_q1_01 path: fr/2020_q1_01-* - split: 2016_q4_10 path: fr/2016_q4_10-* - split: 2016_q4_11 path: fr/2016_q4_11-* - split: 2016_q4_12 path: fr/2016_q4_12-* - split: 2017_q1_01 path: fr/2017_q1_01-* - split: 2017_q1_02 path: fr/2017_q1_02-* - split: 2017_q1_03 path: fr/2017_q1_03-* - split: 2017_q2_04 path: fr/2017_q2_04-* - split: 2017_q2_05 path: fr/2017_q2_05-* - split: 2017_q2_06 path: fr/2017_q2_06-* - split: 2017_q3_07 path: fr/2017_q3_07-* - split: 2017_q3_08 path: fr/2017_q3_08-* - split: 2017_q3_09 path: fr/2017_q3_09-* - split: 2017_q4_10 path: fr/2017_q4_10-* - split: 2017_q4_11 path: fr/2017_q4_11-* - split: 2017_q4_12 path: fr/2017_q4_12-* - split: 2018_q1_01 path: fr/2018_q1_01-* - split: 2018_q1_02 path: fr/2018_q1_02-* - split: 2018_q1_03 path: fr/2018_q1_03-* - split: 2018_q2_04 path: fr/2018_q2_04-* - split: 2018_q2_05 path: fr/2018_q2_05-* - split: 2018_q2_06 path: fr/2018_q2_06-* - split: 2018_q3_07 path: fr/2018_q3_07-* - split: 2018_q3_08 path: fr/2018_q3_08-* - split: 2018_q3_09 path: fr/2018_q3_09-* - split: 2018_q4_10 path: fr/2018_q4_10-* - split: 2018_q4_11 path: fr/2018_q4_11-* - split: 2018_q4_12 path: fr/2018_q4_12-* - split: 2019_q1_01 path: fr/2019_q1_01-* - split: 2019_q1_02 path: fr/2019_q1_02-* - split: 2019_q1_03 path: fr/2019_q1_03-* - split: 2019_q2_04 path: fr/2019_q2_04-* - split: 2019_q2_05 path: fr/2019_q2_05-* - split: 2019_q2_06 path: fr/2019_q2_06-* - split: 2019_q3_07 path: fr/2019_q3_07-* - split: 2019_q3_08 path: fr/2019_q3_08-* - split: 2019_q3_09 path: fr/2019_q3_09-* - split: 2019_q4_10 path: fr/2019_q4_10-* - split: 2019_q4_11 path: fr/2019_q4_11-* - split: 2019_q4_12 path: fr/2019_q4_12-* - split: 2020_q1_02 path: fr/2020_q1_02-* - split: 2020_q1_03 path: fr/2020_q1_03-* - split: 2020_q3_09 path: fr/2020_q3_09-* - split: 2020_q2_04 path: fr/2020_q2_04-* - split: 2020_q2_05 path: fr/2020_q2_05-* - split: 2020_q2_06 path: fr/2020_q2_06-* - split: 2020_q3_07 path: fr/2020_q3_07-* - split: 2020_q3_08 path: fr/2020_q3_08-* - split: 2020_q4_10 path: fr/2020_q4_10-* - split: 2020_q4_11 path: fr/2020_q4_11-* - split: 2020_q4_12 path: fr/2020_q4_12-* - split: 2021_q1_01 path: fr/2021_q1_01-* - split: 2021_q1_02 path: fr/2021_q1_02-* - split: 2021_q1_03 path: fr/2021_q1_03-* - split: 2021_q2_04 path: fr/2021_q2_04-* - split: 2021_q2_05 path: fr/2021_q2_05-* - split: 2021_q2_06 path: fr/2021_q2_06-* - split: 2021_q3_07 path: fr/2021_q3_07-* - split: 2021_q3_08 path: fr/2021_q3_08-* - split: 2021_q3_09 path: fr/2021_q3_09-* - split: 2021_q4_10 path: fr/2021_q4_10-* - split: 2021_q4_11 path: fr/2021_q4_11-* - split: 2021_q4_12 path: fr/2021_q4_12-* - split: 2022_q1_01 path: fr/2022_q1_01-* - split: 2022_q1_02 path: fr/2022_q1_02-* - split: 2022_q1_03 path: fr/2022_q1_03-* - split: 2022_q2_05 path: fr/2022_q2_05-* - split: 2022_q2_04 path: fr/2022_q2_04-* - split: 2022_q2_06 path: fr/2022_q2_06-* - split: 2022_q3_07 path: fr/2022_q3_07-* - split: 2022_q3_08 path: fr/2022_q3_08-* - split: 2022_q3_09 path: fr/2022_q3_09-* - split: 2022_q4_12 path: fr/2022_q4_12-* - split: 2022_q4_10 path: fr/2022_q4_10-* - split: 2022_q4_11 path: fr/2022_q4_11-* - split: 2023_q1_01 path: fr/2023_q1_01-* - split: 2023_q1_03 path: fr/2023_q1_03-* - split: 2023_q1_02 path: fr/2023_q1_02-* - split: 2023_q2_04 path: fr/2023_q2_04-* - split: 2023_q2_05 path: fr/2023_q2_05-* - split: 2023_q2_06 path: fr/2023_q2_06-* - split: 2023_q3_07 path: fr/2023_q3_07-* - split: 2023_q3_08 path: fr/2023_q3_08-* - split: 2023_q3_09 path: fr/2023_q3_09-* - split: 2023_q4_10 path: fr/2023_q4_10-* - split: 2023_q4_11 path: fr/2023_q4_11-* - split: 2023_q4_12 path: fr/2023_q4_12-* - split: 2024_q1_01 path: fr/2024_q1_01-* - split: '2005' path: fr/2005_* - split: '2006' path: fr/2006_* - split: '2007' path: fr/2007_* - split: '2008' path: fr/2008_* - split: '2009' path: fr/2009_* - split: '2010' path: fr/2010_* - split: '2011' path: fr/2011_* - split: '2012' path: fr/2012_* - split: '2013' path: fr/2013_* - split: '2014' path: fr/2014_* - split: '2015' path: fr/2015_* - split: '2016' path: fr/2016_* - split: '2017' path: fr/2017_* - split: '2018' path: fr/2018_* - split: '2019' path: fr/2019_* - split: '2020' path: fr/2020_* - split: '2021' path: fr/2021_* - split: '2022' path: fr/2022_* - split: '2023' path: fr/2023_* - split: '2024' path: fr/2024_* - split: 2005_q2 path: fr/2005_q2_* - split: 2016_q2 path: fr/2016_q2_* - split: 2017_q1 path: fr/2017_q1_* - split: 2010_q4 path: fr/2010_q4_* - split: 2021_q1 path: fr/2021_q1_* - split: 2014_q4 path: fr/2014_q4_* - split: 2015_q3 path: fr/2015_q3_* - split: 2019_q3 path: fr/2019_q3_* - split: 2005_q4 path: fr/2005_q4_* - split: 2016_q4 path: fr/2016_q4_* - split: 2017_q3 path: fr/2017_q3_* - split: 2021_q3 path: fr/2021_q3_* - split: 2006_q2 path: fr/2006_q2_* - split: 2024_q1 path: fr/2024_q1_* - split: 2011_q1 path: fr/2011_q1_* - split: 2022_q1 path: fr/2022_q1_* - split: 2008_q2 path: fr/2008_q2_* - split: 2012_q2 path: fr/2012_q2_* - split: 2023_q2 path: fr/2023_q2_* - split: 2013_q1 path: fr/2013_q1_* - split: 2006_q4 path: fr/2006_q4_* - split: 2011_q3 path: fr/2011_q3_* - split: 2022_q3 path: fr/2022_q3_* - split: 2008_q4 path: fr/2008_q4_* - split: 2012_q4 path: fr/2012_q4_* - split: 2014_q1 path: fr/2014_q1_* - split: 2013_q3 path: fr/2013_q3_* - split: 2023_q4 path: fr/2023_q4_* - split: 2007_q1 path: fr/2007_q1_* - split: 2018_q1 path: fr/2018_q1_* - split: 2015_q2 path: fr/2015_q2_* - split: 2019_q2 path: fr/2019_q2_* - split: 2009_q1 path: fr/2009_q1_* - split: 2020_q1 path: fr/2020_q1_* - split: 2017_q2 path: fr/2017_q2_* - split: 2007_q3 path: fr/2007_q3_* - split: 2018_q3 path: fr/2018_q3_* - split: 2021_q2 path: fr/2021_q2_* - split: 2015_q4 path: fr/2015_q4_* - split: 2019_q4 path: fr/2019_q4_* - split: 2009_q3 path: fr/2009_q3_* - split: 2020_q3 path: fr/2020_q3_* - split: 2021_q4 path: fr/2021_q4_* - split: 2010_q1 path: fr/2010_q1_* - split: 2011_q2 path: fr/2011_q2_* - split: 2022_q2 path: fr/2022_q2_* - split: 2005_q1 path: fr/2005_q1_* - split: 2016_q1 path: fr/2016_q1_* - split: 2010_q3 path: fr/2010_q3_* - split: 2013_q2 path: fr/2013_q2_* - split: 2014_q3 path: fr/2014_q3_* - split: 2011_q4 path: fr/2011_q4_* - split: 2022_q4 path: fr/2022_q4_* - split: 2005_q3 path: fr/2005_q3_* - split: 2016_q3 path: fr/2016_q3_* - split: 2013_q4 path: fr/2013_q4_* - split: 2019_q1 path: fr/2019_q1_* - split: 2006_q1 path: fr/2006_q1_* - split: 2007_q2 path: fr/2007_q2_* - split: 2017_q4 path: fr/2017_q4_* - split: 2008_q1 path: fr/2008_q1_* - split: 2018_q2 path: fr/2018_q2_* - split: 2012_q1 path: fr/2012_q1_* - split: 2023_q1 path: fr/2023_q1_* - split: 2006_q3 path: fr/2006_q3_* - split: 2009_q2 path: fr/2009_q2_* - split: 2020_q2 path: fr/2020_q2_* - split: 2024_q2 path: fr/2024_q2_* - split: 2007_q4 path: fr/2007_q4_* - split: 2018_q4 path: fr/2018_q4_* - split: 2008_q3 path: fr/2008_q3_* - split: 2012_q3 path: fr/2012_q3_* - split: 2023_q3 path: fr/2023_q3_* - split: 2009_q4 path: fr/2009_q4_* - split: 2020_q4 path: fr/2020_q4_* - split: 2010_q2 path: fr/2010_q2_* - split: 2014_q2 path: fr/2014_q2_* - split: 2015_q1 path: fr/2015_q1_* - config_name: it data_files: - split: 2005_q1_03 path: it/2005_q1_03-* - split: 2005_q2_04 path: it/2005_q2_04-* - split: no_date path: it/no_date-* - split: 2005_q2_05 path: it/2005_q2_05-* - split: 2006_q1_02 path: it/2006_q1_02-* - split: 2005_q2_06 path: it/2005_q2_06-* - split: 2005_q3_07 path: it/2005_q3_07-* - split: 2005_q3_09 path: it/2005_q3_09-* - split: 2005_q4_10 path: it/2005_q4_10-* - split: 2005_q3_08 path: it/2005_q3_08-* - split: 2005_q4_11 path: it/2005_q4_11-* - split: 2005_q4_12 path: it/2005_q4_12-* - split: 2006_q1_01 path: it/2006_q1_01-* - split: 2006_q2_04 path: it/2006_q2_04-* - split: 2006_q2_05 path: it/2006_q2_05-* - split: 2005_q1_01 path: it/2005_q1_01-* - split: 2006_q1_03 path: it/2006_q1_03-* - split: 2007_q3_08 path: it/2007_q3_08-* - split: 2007_q4_10 path: it/2007_q4_10-* - split: 2006_q2_06 path: it/2006_q2_06-* - split: 2006_q3_09 path: it/2006_q3_09-* - split: 2006_q4_10 path: it/2006_q4_10-* - split: 2006_q4_11 path: it/2006_q4_11-* - split: 2006_q4_12 path: it/2006_q4_12-* - split: 2007_q1_01 path: it/2007_q1_01-* - split: 2007_q1_02 path: it/2007_q1_02-* - split: 2007_q1_03 path: it/2007_q1_03-* - split: 2007_q2_04 path: it/2007_q2_04-* - split: 2006_q3_07 path: it/2006_q3_07-* - split: 2007_q2_05 path: it/2007_q2_05-* - split: 2007_q2_06 path: it/2007_q2_06-* - split: 2006_q3_08 path: it/2006_q3_08-* - split: 2007_q3_07 path: it/2007_q3_07-* - split: 2007_q3_09 path: it/2007_q3_09-* - split: 2008_q1_03 path: it/2008_q1_03-* - split: 2008_q1_02 path: it/2008_q1_02-* - split: 2007_q4_11 path: it/2007_q4_11-* - split: 2007_q4_12 path: it/2007_q4_12-* - split: 2008_q1_01 path: it/2008_q1_01-* - split: 2008_q2_06 path: it/2008_q2_06-* - split: 2008_q2_04 path: it/2008_q2_04-* - split: 2008_q2_05 path: it/2008_q2_05-* - split: 2008_q3_07 path: it/2008_q3_07-* - split: 2009_q3_08 path: it/2009_q3_08-* - split: 2008_q3_08 path: it/2008_q3_08-* - split: 2008_q3_09 path: it/2008_q3_09-* - split: 2008_q4_10 path: it/2008_q4_10-* - split: 2008_q4_11 path: it/2008_q4_11-* - split: 2008_q4_12 path: it/2008_q4_12-* - split: 2009_q1_01 path: it/2009_q1_01-* - split: 2009_q1_03 path: it/2009_q1_03-* - split: 2009_q1_02 path: it/2009_q1_02-* - split: 2012_q3_08 path: it/2012_q3_08-* - split: 2009_q2_04 path: it/2009_q2_04-* - split: 2009_q2_05 path: it/2009_q2_05-* - split: 2009_q2_06 path: it/2009_q2_06-* - split: 2009_q3_07 path: it/2009_q3_07-* - split: 2009_q3_09 path: it/2009_q3_09-* - split: 2009_q4_12 path: it/2009_q4_12-* - split: 2009_q4_10 path: it/2009_q4_10-* - split: 2009_q4_11 path: it/2009_q4_11-* - split: 2010_q1_01 path: it/2010_q1_01-* - split: 2010_q1_02 path: it/2010_q1_02-* - split: 2010_q1_03 path: it/2010_q1_03-* - split: 2010_q2_04 path: it/2010_q2_04-* - split: 2010_q2_05 path: it/2010_q2_05-* - split: 2010_q2_06 path: it/2010_q2_06-* - split: 2010_q3_07 path: it/2010_q3_07-* - split: 2010_q3_08 path: it/2010_q3_08-* - split: 2010_q3_09 path: it/2010_q3_09-* - split: 2010_q4_10 path: it/2010_q4_10-* - split: 2010_q4_11 path: it/2010_q4_11-* - split: 2010_q4_12 path: it/2010_q4_12-* - split: 2011_q1_01 path: it/2011_q1_01-* - split: 2011_q1_02 path: it/2011_q1_02-* - split: 2011_q1_03 path: it/2011_q1_03-* - split: 2011_q2_04 path: it/2011_q2_04-* - split: 2011_q2_05 path: it/2011_q2_05-* - split: 2011_q2_06 path: it/2011_q2_06-* - split: 2011_q3_07 path: it/2011_q3_07-* - split: 2011_q3_08 path: it/2011_q3_08-* - split: 2011_q3_09 path: it/2011_q3_09-* - split: 2011_q4_10 path: it/2011_q4_10-* - split: 2011_q4_11 path: it/2011_q4_11-* - split: 2011_q4_12 path: it/2011_q4_12-* - split: 2012_q1_01 path: it/2012_q1_01-* - split: 2012_q1_02 path: it/2012_q1_02-* - split: 2012_q1_03 path: it/2012_q1_03-* - 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split: '2009' path: it/2009_* - split: '2010' path: it/2010_* - split: '2011' path: it/2011_* - split: '2012' path: it/2012_* - split: '2013' path: it/2013_* - split: '2014' path: it/2014_* - split: '2015' path: it/2015_* - split: '2016' path: it/2016_* - split: '2017' path: it/2017_* - split: '2018' path: it/2018_* - split: '2019' path: it/2019_* - split: '2020' path: it/2020_* - split: '2021' path: it/2021_* - split: '2022' path: it/2022_* - split: '2023' path: it/2023_* - split: '2024' path: it/2024_* - split: 2005_q2 path: it/2005_q2_* - split: 2016_q2 path: it/2016_q2_* - split: 2017_q1 path: it/2017_q1_* - split: 2010_q4 path: it/2010_q4_* - split: 2021_q1 path: it/2021_q1_* - split: 2014_q4 path: it/2014_q4_* - split: 2015_q3 path: it/2015_q3_* - split: 2019_q3 path: it/2019_q3_* - split: 2005_q4 path: it/2005_q4_* - split: 2016_q4 path: it/2016_q4_* - split: 2017_q3 path: it/2017_q3_* - split: 2021_q3 path: it/2021_q3_* - split: 2006_q2 path: it/2006_q2_* - split: 2024_q1 path: it/2024_q1_* - 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split: 2021_q4 path: it/2021_q4_* - split: 2010_q1 path: it/2010_q1_* - split: 2011_q2 path: it/2011_q2_* - split: 2022_q2 path: it/2022_q2_* - split: 2005_q1 path: it/2005_q1_* - split: 2016_q1 path: it/2016_q1_* - split: 2010_q3 path: it/2010_q3_* - split: 2013_q2 path: it/2013_q2_* - split: 2014_q3 path: it/2014_q3_* - split: 2011_q4 path: it/2011_q4_* - split: 2022_q4 path: it/2022_q4_* - split: 2005_q3 path: it/2005_q3_* - split: 2016_q3 path: it/2016_q3_* - split: 2013_q4 path: it/2013_q4_* - split: 2019_q1 path: it/2019_q1_* - split: 2006_q1 path: it/2006_q1_* - split: 2007_q2 path: it/2007_q2_* - split: 2017_q4 path: it/2017_q4_* - split: 2008_q1 path: it/2008_q1_* - split: 2018_q2 path: it/2018_q2_* - split: 2012_q1 path: it/2012_q1_* - split: 2023_q1 path: it/2023_q1_* - split: 2006_q3 path: it/2006_q3_* - split: 2009_q2 path: it/2009_q2_* - split: 2020_q2 path: it/2020_q2_* - split: 2007_q4 path: it/2007_q4_* - split: 2018_q4 path: it/2018_q4_* - split: 2008_q3 path: it/2008_q3_* - split: 2012_q3 path: it/2012_q3_* - split: 2023_q3 path: it/2023_q3_* - split: 2009_q4 path: it/2009_q4_* - split: 2020_q4 path: it/2020_q4_* - split: 2010_q2 path: it/2010_q2_* - split: 2014_q2 path: it/2014_q2_* - split: 2015_q1 path: it/2015_q1_* --- # Wikinews The dataset contains news articles from Wikinews in different languages. Each article is associated with metadata like title, url, and date. The articles grouped into data splits by the article month, quarter, and year (the date is one mentioned in the article text, changes might have been after, see revision timestamp). The dataset config name defines the language. ## Usage ```python from datasets import load_dataset # all English news from 2008 ds = load_dataset("malteos/wikinews", config_name="en", split="2008") # all German news from January 2017 ds = load_dataset("malteos/wikinews", config_name="de", split="2017_q1_01") ``` ## Languages - en - es - fr - it - de ## License All text created after September 25, 2005 available under the terms of the [Creative Commons Attribution 2.5 License](https://creativecommons.org/licenses/by/2.5/).
mask-distilled-one-sec-cv12/chunk_240	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1453185512 num_examples: 285386 download_size: 1484150414 dataset_size: 1453185512 --- # Dataset Card for "chunk_240" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
linges0103/datasetfortrain	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 42812 num_examples: 59 download_size: 22507 dataset_size: 42812 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "datasetfortrain" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
abdoutony207/en_ar_dt	--- license: other ---
lazaroq11/billqa	--- dataset_info: features: - name: text dtype: string - name: additional_info dtype: string splits: - name: train num_bytes: 240602641 num_examples: 9846 download_size: 9341153 dataset_size: 240602641 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "billqa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Technoculture/MedpromptCoT	--- dataset_info: features: - name: question dtype: string - name: options dtype: string - name: reasoning dtype: string - name: answer dtype: string splits: - name: train num_bytes: 506301 num_examples: 676 download_size: 287262 dataset_size: 506301 configs: - config_name: default data_files: - split: train path: data/train-* license: mit language: - en --- # Dataset Card for "MedpromptCoT" ### Model used: gpt-3.5-turbo ## Dataset Mixture used for generating CoT \| Dataset Name \| Original Size(Rows) \| Rows used \| \|----------------------------------------------------\|---------------\|-------\| \| openlifescienceai/medmcqa \| 183k \| 0.5k \| \| GBaker/MedQA-USMLE-4-options \| 10.2k \| 0.5k \| Total Size: 1k Size after selecting only correct CoT: 0.676k ## How to Run the Script: To rerun the script, follow these steps: 1. Environment Setup: - Ensure Python environment with required dependencies (specified in requirements.txt) is set up. - Install necessary libraries using pip install -r requirements.txt. 2. Arguments - Choose the desired language model using the --model argument. - If using OpenAI's models (gpt-3.5-turbo or gpt-4-turbo-preview), set the OPENAI_API_KEY environment variable. For using Together models, set the TOGETHER_API_KEY and TOGETHER_API_BASE environment variables. - Specify the LLM client(openai, together or huggingface) using the --llm_client_type argument. - Specify the dataset name via the --dataset argument. - Define the output file location via the --output_file argument. - Specify the Hugging Face dataset repository for uploading the new dataset conisting of the COTs using the --huggingface_repo argument. - Specify the checkpointing number using the --checkpointing_steps. - Specify the count of examples to be taken from the dataset using --count. Run the script using python generate_medprompt_cot.py [arguments]. ### For full details please go through the generate_medprompt_cot.py file.
tyzhu/squad_qa_baseline_v5_full_recite_ans_sent_last_permute_rerun	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 2996506.0 num_examples: 2385 - name: validation num_bytes: 395889 num_examples: 300 download_size: 842977 dataset_size: 3392395.0 --- # Dataset Card for "squad_qa_baseline_v5_full_recite_ans_sent_last_permute_rerun" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
distilled-one-sec-cv12-each-chunk-uniq/chunk_172	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1072367324.0 num_examples: 208957 download_size: 1089947138 dataset_size: 1072367324.0 --- # Dataset Card for "chunk_172" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_ChaoticNeutrals__Prima-LelantaclesV7-experimental-7b	--- pretty_name: Evaluation run of ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b](https://huggingface.co/ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ChaoticNeutrals__Prima-LelantaclesV7-experimental-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-14T07:48:27.172404](https://huggingface.co/datasets/open-llm-leaderboard/details_ChaoticNeutrals__Prima-LelantaclesV7-experimental-7b/blob/main/results_2024-03-14T07-48-27.172404.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\": 0.6495664452929546,\n\ \ \"acc_stderr\": 0.03218441389606728,\n \"acc_norm\": 0.6487168204006772,\n\ \ \"acc_norm_stderr\": 0.03286122397355617,\n \"mc1\": 0.5826193390452876,\n\ \ \"mc1_stderr\": 0.017262891063272164,\n \"mc2\": 0.7461832399602438,\n\ \ \"mc2_stderr\": 0.01424474030426709\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.7081911262798635,\n \"acc_stderr\": 0.013284525292403511,\n\ \ \"acc_norm\": 0.7286689419795221,\n \"acc_norm_stderr\": 0.012993807727545796\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7162915753833897,\n\ \ \"acc_stderr\": 0.0044987571944934,\n \"acc_norm\": 0.8871738697470624,\n\ \ \"acc_norm_stderr\": 0.0031573355082588415\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7302631578947368,\n \"acc_stderr\": 0.03611780560284898,\n\ \ \"acc_norm\": 0.7302631578947368,\n \"acc_norm_stderr\": 0.03611780560284898\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933712,\n\ \ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933712\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_computer_science\|5\"\ : {\n \"acc\": 0.56,\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\"\ : 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n\ \ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness\|hendrycksTest-college_medicine\|5\"\ : {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.0368122963339432,\n\ \ \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.0368122963339432\n\ \ },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.38235294117647056,\n\ \ \"acc_stderr\": 0.04835503696107224,\n \"acc_norm\": 0.38235294117647056,\n\ \ \"acc_norm_stderr\": 0.04835503696107224\n },\n \"harness\|hendrycksTest-computer_security\|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.5659574468085107,\n\ \ \"acc_stderr\": 0.032400380867927465,\n \"acc_norm\": 0.5659574468085107,\n\ \ \"acc_norm_stderr\": 0.032400380867927465\n },\n \"harness\|hendrycksTest-econometrics\|5\"\ : {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.0470070803355104,\n\ \ \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.0470070803355104\n\ \ },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\"\ : 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"\ acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.41005291005291006,\n \"acc_stderr\": 0.02533120243894443,\n \"\ acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.02533120243894443\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.49206349206349204,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\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\": 0.7838709677419354,\n\ \ \"acc_stderr\": 0.023415293433568525,\n \"acc_norm\": 0.7838709677419354,\n\ \ \"acc_norm_stderr\": 0.023415293433568525\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\ \ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\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\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644237,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644237\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473086,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473086\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886793,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886793\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461763,\n \"\ acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461763\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\"\ : {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926924,\n\ \ \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926924\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\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7592592592592593,\n\ \ \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.7592592592592593,\n\ \ \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\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\": 0.7572815533980582,\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\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \ \ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8186462324393359,\n\ \ \"acc_stderr\": 0.013778693778464085,\n \"acc_norm\": 0.8186462324393359,\n\ \ \"acc_norm_stderr\": 0.013778693778464085\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.02394851290546836,\n\ \ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.02394851290546836\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4424581005586592,\n\ \ \"acc_stderr\": 0.016611393687268584,\n \"acc_norm\": 0.4424581005586592,\n\ \ \"acc_norm_stderr\": 0.016611393687268584\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n\ \ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7202572347266881,\n\ \ \"acc_stderr\": 0.02549425935069491,\n \"acc_norm\": 0.7202572347266881,\n\ \ \"acc_norm_stderr\": 0.02549425935069491\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460842,\n\ \ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460842\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\": 0.46479791395045633,\n \"acc_stderr\": 0.012738547371303956,\n\ \ \"acc_norm\": 0.46479791395045633,\n \"acc_norm_stderr\": 0.012738547371303956\n\ \ },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\"\ : 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462923,\n \"\ acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462923\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \ \ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128448,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128448\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.026508590656233268,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.026508590656233268\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5826193390452876,\n\ \ \"mc1_stderr\": 0.017262891063272164,\n \"mc2\": 0.7461832399602438,\n\ \ \"mc2_stderr\": 0.01424474030426709\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8476716653512234,\n \"acc_stderr\": 0.0100992082460656\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6929492039423806,\n \ \ \"acc_stderr\": 0.012705685723131712\n }\n}\n```" repo_url: https://huggingface.co/ChaoticNeutrals/Prima-LelantaclesV7-experimental-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_14T07_48_27.172404 path: - '/details_harness\|arc:challenge\|25_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-14T07-48-27.172404.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|gsm8k\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hellaswag\|10_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-14T07-48-27.172404.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-14T07-48-27.172404.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '/details_harness\|winogrande\|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-14T07-48-27.172404.parquet' - config_name: results data_files: - split: 2024_03_14T07_48_27.172404 path: - results_2024-03-14T07-48-27.172404.parquet - split: latest path: - results_2024-03-14T07-48-27.172404.parquet --- # Dataset Card for Evaluation run of ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b](https://huggingface.co/ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ChaoticNeutrals__Prima-LelantaclesV7-experimental-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-14T07:48:27.172404](https://huggingface.co/datasets/open-llm-leaderboard/details_ChaoticNeutrals__Prima-LelantaclesV7-experimental-7b/blob/main/results_2024-03-14T07-48-27.172404.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": 0.6495664452929546, "acc_stderr": 0.03218441389606728, "acc_norm": 0.6487168204006772, "acc_norm_stderr": 0.03286122397355617, "mc1": 0.5826193390452876, "mc1_stderr": 0.017262891063272164, "mc2": 0.7461832399602438, "mc2_stderr": 0.01424474030426709 }, "harness\|arc:challenge\|25": { "acc": 0.7081911262798635, "acc_stderr": 0.013284525292403511, "acc_norm": 0.7286689419795221, "acc_norm_stderr": 0.012993807727545796 }, "harness\|hellaswag\|10": { "acc": 0.7162915753833897, "acc_stderr": 0.0044987571944934, "acc_norm": 0.8871738697470624, "acc_norm_stderr": 0.0031573355082588415 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7302631578947368, "acc_stderr": 0.03611780560284898, "acc_norm": 0.7302631578947368, "acc_norm_stderr": 0.03611780560284898 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7094339622641509, "acc_stderr": 0.02794321998933712, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.02794321998933712 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107224, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107224 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5659574468085107, "acc_stderr": 0.032400380867927465, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.032400380867927465 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4824561403508772, "acc_stderr": 0.0470070803355104, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.0470070803355104 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.41005291005291006, "acc_stderr": 0.02533120243894443, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.02533120243894443 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.49206349206349204, "acc_stderr": 0.044715725362943486, "acc_norm": 0.49206349206349204, "acc_norm_stderr": 0.044715725362943486 }, "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": 0.7838709677419354, "acc_stderr": 0.023415293433568525, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.023415293433568525 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "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": 0.7636363636363637, "acc_stderr": 0.03317505930009182, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009182 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8963730569948186, "acc_stderr": 0.021995311963644237, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.021995311963644237 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563973, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563973 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.32592592592592595, "acc_stderr": 0.028578348365473086, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.028578348365473086 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886793, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886793 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242742, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242742 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8403669724770643, "acc_stderr": 0.015703498348461763, "acc_norm": 0.8403669724770643, "acc_norm_stderr": 0.015703498348461763 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5, "acc_stderr": 0.034099716973523674, "acc_norm": 0.5, "acc_norm_stderr": 0.034099716973523674 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8382352941176471, "acc_stderr": 0.025845017986926924, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.025845017986926924 }, "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": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624714, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624714 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228732, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228732 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7592592592592593, "acc_stderr": 0.04133119440243839, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243839 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "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": 0.7572815533980582, "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": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8186462324393359, "acc_stderr": 0.013778693778464085, "acc_norm": 0.8186462324393359, "acc_norm_stderr": 0.013778693778464085 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7283236994219653, "acc_stderr": 0.02394851290546836, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.02394851290546836 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4424581005586592, "acc_stderr": 0.016611393687268584, "acc_norm": 0.4424581005586592, "acc_norm_stderr": 0.016611393687268584 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7202572347266881, "acc_stderr": 0.02549425935069491, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.02549425935069491 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7345679012345679, "acc_stderr": 0.024569223600460842, "acc_norm": 0.7345679012345679, "acc_norm_stderr": 0.024569223600460842 }, "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": 0.46479791395045633, "acc_stderr": 0.012738547371303956, "acc_norm": 0.46479791395045633, "acc_norm_stderr": 0.012738547371303956 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462923, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462923 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806315, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806315 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.726530612244898, "acc_stderr": 0.028535560337128448, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.028535560337128448 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8308457711442786, "acc_stderr": 0.026508590656233268, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.026508590656233268 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5826193390452876, "mc1_stderr": 0.017262891063272164, "mc2": 0.7461832399602438, "mc2_stderr": 0.01424474030426709 }, "harness\|winogrande\|5": { "acc": 0.8476716653512234, "acc_stderr": 0.0100992082460656 }, "harness\|gsm8k\|5": { "acc": 0.6929492039423806, "acc_stderr": 0.012705685723131712 } } ``` ## 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 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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.). 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Sunbird/salt	--- dataset_info: - config_name: multispeaker-ach features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 1789773755 num_examples: 4811 - name: dev num_bytes: 37429640 num_examples: 101 - name: test num_bytes: 36224395 num_examples: 96 download_size: 861112801 dataset_size: 1863427790 - config_name: multispeaker-eng features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 1490684144 num_examples: 4797 - name: dev num_bytes: 30879913 num_examples: 100 - name: test num_bytes: 32136197 num_examples: 96 download_size: 746376946 dataset_size: 1553700254 - config_name: multispeaker-lgg features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 2346309650 num_examples: 4768 - name: dev num_bytes: 49044863 num_examples: 101 - name: test num_bytes: 49347397 num_examples: 96 download_size: 1191834787 dataset_size: 2444701910 - config_name: multispeaker-lug features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 2000647332 num_examples: 5016 - name: dev num_bytes: 38741382 num_examples: 103 - name: test num_bytes: 39746716 num_examples: 97 download_size: 1010619540 dataset_size: 2079135430 - config_name: multispeaker-nyn features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 2097997736 num_examples: 4812 - name: dev num_bytes: 42040138 num_examples: 101 - name: test num_bytes: 45063129 num_examples: 96 download_size: 1426293640 dataset_size: 2185101003 - config_name: multispeaker-teo features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 1980187546 num_examples: 4839 - name: dev num_bytes: 38906909 num_examples: 99 - name: test num_bytes: 40474249 num_examples: 96 download_size: 992185148 dataset_size: 2059568704 - config_name: studio-acholi features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 1347658634 num_examples: 4801 - name: dev num_bytes: 27757030 num_examples: 101 - name: test num_bytes: 26447325 num_examples: 96 download_size: 698234854 dataset_size: 1401862989 - config_name: studio-ateso features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 2308097503 num_examples: 4564 - name: dev num_bytes: 49170958 num_examples: 96 - name: test num_bytes: 47400438 num_examples: 92 download_size: 977293946 dataset_size: 2404668899 - config_name: studio-english features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 851109381 num_examples: 2411 - name: dev num_bytes: 17784430 num_examples: 50 - name: test num_bytes: 15322757 num_examples: 42 download_size: 435775221 dataset_size: 884216568 - config_name: studio-luganda features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 880656730 num_examples: 2395 - name: dev num_bytes: 18853020 num_examples: 50 - name: test num_bytes: 16076901 num_examples: 43 download_size: 455441369 dataset_size: 915586651 - config_name: studio-runyankole features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 39234984 num_examples: 94 - name: dev num_bytes: 1666059 num_examples: 4 - name: test num_bytes: 947547 num_examples: 2 download_size: 20592402 dataset_size: 41848590 - config_name: text-all features: - name: id dtype: int64 - name: teo_text dtype: string - name: swa_text dtype: string - name: eng_text dtype: string - name: nyn_text dtype: string - name: ibo_text dtype: string - name: ach_text dtype: string - name: lgg_text dtype: string - name: lug_text dtype: string splits: - name: train num_bytes: 11763775 num_examples: 23947 - name: dev num_bytes: 242587 num_examples: 496 - name: test num_bytes: 253968 num_examples: 500 download_size: 7228279 dataset_size: 12260330 configs: - config_name: multispeaker-ach data_files: - split: train path: multispeaker-ach/train-* - split: dev path: multispeaker-ach/dev-* - split: test path: multispeaker-ach/test-* - config_name: multispeaker-eng data_files: - split: train path: multispeaker-eng/train-* - split: dev path: multispeaker-eng/dev-* - split: test path: multispeaker-eng/test-* - config_name: multispeaker-lgg data_files: - split: train path: multispeaker-lgg/train-* - split: dev path: multispeaker-lgg/dev-* - split: test path: multispeaker-lgg/test-* - config_name: multispeaker-lug data_files: - split: train path: multispeaker-lug/train-* - split: dev path: multispeaker-lug/dev-* - split: test path: multispeaker-lug/test-* - config_name: multispeaker-nyn data_files: - split: train path: multispeaker-nyn/train-* - split: dev path: multispeaker-nyn/dev-* - split: test path: multispeaker-nyn/test-* - config_name: multispeaker-teo data_files: - split: train path: multispeaker-teo/train-* - split: dev path: multispeaker-teo/dev-* - split: test path: multispeaker-teo/test-* - config_name: studio-acholi data_files: - split: train path: studio-acholi/train-* - split: dev path: studio-acholi/dev-* - split: test path: studio-acholi/test-* - config_name: studio-ateso data_files: - split: train path: studio-ateso/train-* - split: dev path: studio-ateso/dev-* - split: test path: studio-ateso/test-* - config_name: studio-english data_files: - split: train path: studio-english/train-* - split: dev path: studio-english/dev-* - split: test path: studio-english/test-* - config_name: studio-luganda data_files: - split: train path: studio-luganda/train-* - split: dev path: studio-luganda/dev-* - split: test path: studio-luganda/test-* - config_name: studio-runyankole data_files: - split: train path: studio-runyankole/train-* - split: dev path: studio-runyankole/dev-* - split: test path: studio-runyankole/test-* - config_name: text-all data_files: - split: train path: text-all/train-* - split: dev path: text-all/dev-* - split: test path: text-all/test-* ---
felipesampaio/darwin2dataset	--- license: openrail ---
witchling22/tokenized_T5_base	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: source dtype: string - name: source_labels dtype: string - name: rouge_scores dtype: string - name: paper_id dtype: string - name: target dtype: string - name: full_source_text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 17340567 num_examples: 1992 - name: test num_bytes: 5620222 num_examples: 618 - name: validation num_bytes: 5534448 num_examples: 619 download_size: 6371599 dataset_size: 28495237 --- # Dataset Card for "tokenized_T5_base" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NebulaSense/Legal_Clause_Instructions	--- license: cc-by-nc-4.0 ---
wangxingjun778/test_123	--- license: apache-2.0 --- ## 1. ONLY FOR TEST ## 2. ONLY FOR TEST
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/629bee15	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 176 num_examples: 10 download_size: 1341 dataset_size: 176 --- # Dataset Card for "629bee15" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
AdapterOcean/code_instructions_standardized_cluster_8_std	--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 32500014 num_examples: 26748 download_size: 15520517 dataset_size: 32500014 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "code_instructions_standardized_cluster_8_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
EleutherAI/naturenet	--- dataset_info: features: - name: img dtype: image - name: label dtype: class_label: names: '0': amphibian '1': bird '2': dog '3': feline '4': fish '5': flower '6': horse '7': primate '8': rodent '9': snake splits: - name: train num_bytes: 2195586500.24 num_examples: 490000 - name: test num_bytes: 45820817.76 num_examples: 10000 download_size: 2188877286 dataset_size: 2241407318.0 --- # Dataset Card for "naturenet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
wyx-ucl/SUM-DATASET-BASED-EDGAR-CORPUS	--- license: other ---
PJMixers/oasst2_dpo_metharme_english	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: lang dtype: string - name: parent_id dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 32775306 num_examples: 12353 - name: validation num_bytes: 1717916 num_examples: 668 download_size: 18679121 dataset_size: 34493222 source_datasets: - OpenAssistant/oasst2 tags: - dpo - rlhf - human-feedback - reward - preference - pairwise - pair pretty_name: Open Assistant 2 DPO (Metharme) size_categories: - 10K<n<100K --- Similar to [monology/oasst2_dpo](https://huggingface.co/datasets/monology/oasst2_dpo), except this uses Metharme tags instead. Only samples marked english were kept.
datasets-examples/doc-image-6	--- size_categories: - n<1K --- # [doc] image dataset 6 This dataset contains 4 jpeg files in the `train/images/` subdirectory, along with a `train/metadata.csv` file that provides the data for other columns. The metadata file contains relative paths to the images.
open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla	--- pretty_name: Evaluation run of l3utterfly/open-llama-3b-v2-layla dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [l3utterfly/open-llama-3b-v2-layla](https://huggingface.co/l3utterfly/open-llama-3b-v2-layla)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T08:49:03.131155](https://huggingface.co/datasets/open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla/blob/main/results_2023-09-17T08-49-03.131155.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.011954697986577181,\n\ \ \"em_stderr\": 0.0011130056898859086,\n \"f1\": 0.07875629194630916,\n\ \ \"f1_stderr\": 0.0018920865515620476,\n \"acc\": 0.3194349118852447,\n\ \ \"acc_stderr\": 0.008202509803690292\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.011954697986577181,\n \"em_stderr\": 0.0011130056898859086,\n\ \ \"f1\": 0.07875629194630916,\n \"f1_stderr\": 0.0018920865515620476\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.01061410159211524,\n \ \ \"acc_stderr\": 0.0028227133223877035\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.6282557221783741,\n \"acc_stderr\": 0.013582306284992879\n\ \ }\n}\n```" repo_url: https://huggingface.co/l3utterfly/open-llama-3b-v2-layla 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_08_18T14_37_31.844402 path: - '/details_harness\|arc:challenge\|25_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-18T14:37:31.844402.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T08_49_03.131155 path: - '/details_harness\|drop\|3_2023-09-17T08-49-03.131155.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-17T08-49-03.131155.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T08_49_03.131155 path: - '/details_harness\|gsm8k\|5_2023-09-17T08-49-03.131155.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-17T08-49-03.131155.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hellaswag\|10_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-18T14:37:31.844402.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_18T14_37_31.844402 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-18T14:37:31.844402.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T08_49_03.131155 path: - '/details_harness\|winogrande\|5_2023-09-17T08-49-03.131155.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-17T08-49-03.131155.parquet' - config_name: results data_files: - split: 2023_08_18T14_37_31.844402 path: - results_2023-08-18T14:37:31.844402.parquet - split: 2023_09_17T08_49_03.131155 path: - results_2023-09-17T08-49-03.131155.parquet - split: latest path: - results_2023-09-17T08-49-03.131155.parquet --- # Dataset Card for Evaluation run of l3utterfly/open-llama-3b-v2-layla ## Dataset Description - Homepage: - Repository: https://huggingface.co/l3utterfly/open-llama-3b-v2-layla - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [l3utterfly/open-llama-3b-v2-layla](https://huggingface.co/l3utterfly/open-llama-3b-v2-layla) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T08:49:03.131155](https://huggingface.co/datasets/open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla/blob/main/results_2023-09-17T08-49-03.131155.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.011954697986577181, "em_stderr": 0.0011130056898859086, "f1": 0.07875629194630916, "f1_stderr": 0.0018920865515620476, "acc": 0.3194349118852447, "acc_stderr": 0.008202509803690292 }, "harness\|drop\|3": { "em": 0.011954697986577181, "em_stderr": 0.0011130056898859086, "f1": 0.07875629194630916, "f1_stderr": 0.0018920865515620476 }, "harness\|gsm8k\|5": { "acc": 0.01061410159211524, "acc_stderr": 0.0028227133223877035 }, "harness\|winogrande\|5": { "acc": 0.6282557221783741, "acc_stderr": 0.013582306284992879 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
sanchit-gandhi/librispeech_asr_dummy_pseudo_labelled	--- dataset_info: config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: whisper_transcript sequence: int64 splits: - name: validation num_bytes: 9700520.0 num_examples: 73 download_size: 9198584 dataset_size: 9700520.0 configs: - config_name: clean data_files: - split: validation path: clean/validation-* ---
adamjweintraut/kwsylgen	--- dataset_info: features: - name: midi_id dtype: string - name: song_title dtype: string - name: lyrics dtype: string - name: genre dtype: string - name: lyric_summary_bartv2 dtype: string - name: topic dtype: string - name: clean_lyrics dtype: string - name: lyric_chunk_n dtype: int64 - name: prev_clean_lyrics dtype: string - name: sylls dtype: int64 - name: keywords sequence: string - name: orig dtype: string - name: target dtype: string splits: - name: train num_bytes: 358889793.8653989 num_examples: 178354 - name: test num_bytes: 47312159.64738172 num_examples: 23126 - name: valid num_bytes: 47185445.65799565 num_examples: 23683 download_size: 21206048 dataset_size: 453387399.17077625 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---
EleutherAI/headqa	--- license: other ---
CyberHarem/shiina_mahiru_otonarinotenshisamaniitsunomanikadameningennisareteitaken	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Shiina Mahiru/椎名真昼 (Otonari no Tenshi-sama ni Itsunomanika Dame Ningen ni Sareteita Ken) This is the dataset of Shiina Mahiru/椎名真昼 (Otonari no Tenshi-sama ni Itsunomanika Dame Ningen ni Sareteita Ken), containing 463 images and their tags. The core tags of this character are `long_hair, blonde_hair, yellow_eyes, brown_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 463 \| 399.79 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shiina_mahiru_otonarinotenshisamaniitsunomanikadameningennisareteitaken/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 463 \| 399.64 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shiina_mahiru_otonarinotenshisamaniitsunomanikadameningennisareteitaken/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 830 \| 656.88 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shiina_mahiru_otonarinotenshisamaniitsunomanikadameningennisareteitaken/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/shiina_mahiru_otonarinotenshisamaniitsunomanikadameningennisareteitaken', 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 \| 11 \| ![](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, open_mouth, portrait, solo, close-up, looking_at_viewer, :d, :o \| \| 1 \| 10 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, closed_mouth, portrait, solo, blush, looking_at_viewer, smile, close-up \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, portrait, shirt, solo, blush, indoors, closed_mouth, collarbone, looking_at_viewer \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, collared_shirt, open_mouth, solo, white_shirt, blush, indoors, upper_body, curtains, portrait \| \| 4 \| 9 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, closed_mouth, solo, blush, portrait, profile, white_shirt, collared_shirt, from_side \| \| 5 \| 6 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, closed_mouth, collared_shirt, indoors, long_sleeves, looking_at_viewer, smile, solo, upper_body, white_shirt, dutch_angle, holding, apron, whisk \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, ponytail, portrait, sidelocks, solo, closed_mouth, shirt, smile, collarbone, anime_coloring, apron, grey_background, simple_background \| \| 7 \| 7 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, indoors, ponytail, sidelocks, smile, solo, blush, collarbone, open_mouth, purple_shirt, very_long_hair, yellow_apron, long_sleeves, standing, white_skirt \| \| 8 \| 9 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, sidelocks, solo, braid, collarbone, white_shirt, open_mouth, upper_body, blue_ribbon, blunt_bangs, single_hair_bun \| \| 9 \| 24 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, school_uniform, white_shirt, collared_shirt, solo, blazer, red_bowtie, closed_mouth, upper_body, blue_jacket, blurry_background, looking_at_viewer, outdoors, blush \| \| 10 \| 6 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| 1girl, hair_scrunchie, ponytail, sidelocks, blue_scrunchie, closed_mouth, solo, blush, indoors, pink_shirt, profile, upper_body \| \| 11 \| 7 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| 1girl, solo_focus, upper_body, 2girls, closed_mouth, collared_shirt, looking_at_viewer, white_shirt, brown_hair, collarbone, blush, pajamas, simple_background, smile \| \| 12 \| 5 \| ![](samples/12/clu12-sample0.png) \| ![](samples/12/clu12-sample1.png) \| ![](samples/12/clu12-sample2.png) \| ![](samples/12/clu12-sample3.png) \| ![](samples/12/clu12-sample4.png) \| 1girl, blazer, brown_skirt, long_sleeves, plaid_skirt, school_uniform, very_long_hair, 2boys, blue_jacket, shoes, smile, solo_focus, white_shirt, 1boy, head_out_of_frame, pants, pleated_skirt, red_bowtie \| \| 13 \| 7 \| ![](samples/13/clu13-sample0.png) \| ![](samples/13/clu13-sample1.png) \| ![](samples/13/clu13-sample2.png) \| ![](samples/13/clu13-sample3.png) \| ![](samples/13/clu13-sample4.png) \| 1girl, braid, cherry_blossoms, hair_bow, long_sleeves, looking_at_viewer, outdoors, smile, solo, upper_body, white_shirt, closed_mouth, petals, tree, from_side, looking_back, flower, very_long_hair, floating_hair \| \| 14 \| 5 \| ![](samples/14/clu14-sample0.png) \| ![](samples/14/clu14-sample1.png) \| ![](samples/14/clu14-sample2.png) \| ![](samples/14/clu14-sample3.png) \| ![](samples/14/clu14-sample4.png) \| 1girl, blanket, pillow, under_covers, blush, closed_mouth, indoors, looking_at_viewer, on_bed, on_back, solo_focus \| \| 15 \| 8 \| ![](samples/15/clu15-sample0.png) \| ![](samples/15/clu15-sample1.png) \| ![](samples/15/clu15-sample2.png) \| ![](samples/15/clu15-sample3.png) \| ![](samples/15/clu15-sample4.png) \| 1girl, pink_kimono, sidelocks, closed_mouth, hair_flower, blush, floral_print, looking_at_viewer, obi, upper_body, long_sleeves, solo_focus \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| open_mouth \| portrait \| solo \| close-up \| looking_at_viewer \| :d \| :o \| closed_mouth \| smile \| shirt \| indoors \| collarbone \| collared_shirt \| white_shirt \| upper_body \| curtains \| profile \| from_side \| long_sleeves \| dutch_angle \| holding \| apron \| whisk \| ponytail \| sidelocks \| anime_coloring \| grey_background \| simple_background \| purple_shirt \| very_long_hair \| yellow_apron \| standing \| white_skirt \| braid \| blue_ribbon \| blunt_bangs \| single_hair_bun \| school_uniform \| blazer \| red_bowtie \| blue_jacket \| blurry_background \| outdoors \| hair_scrunchie \| blue_scrunchie \| pink_shirt \| solo_focus \| 2girls \| brown_hair \| pajamas \| brown_skirt \| plaid_skirt \| 2boys \| shoes \| 1boy \| head_out_of_frame \| pants \| pleated_skirt \| cherry_blossoms \| hair_bow \| petals \| tree \| looking_back \| flower \| floating_hair \| blanket \| pillow \| under_covers \| on_bed \| on_back \| pink_kimono \| hair_flower \| floral_print \| obi \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:--------\|:-------------\|:-----------\|:-------\|:-----------\|:--------------------\|:-----\|:-----\|:---------------\|:--------\|:--------\|:----------\|:-------------\|:-----------------\|:--------------\|:-------------\|:-----------\|:----------\|:------------\|:---------------\|:--------------\|:----------\|:--------\|:--------\|:-----------\|:------------\|:-----------------\|:------------------\|:--------------------\|:---------------\|:-----------------\|:---------------\|:-----------\|:--------------\|:--------\|:--------------\|:--------------\|:------------------\|:-----------------\|:---------\|:-------------\|:--------------\|:--------------------\|:-----------\|:-----------------\|:-----------------\|:-------------\|:-------------\|:---------\|:-------------\|:----------\|:--------------\|:--------------\|:--------\|:--------\|:-------\|:--------------------\|:--------\|:----------------\|:------------------\|:-----------\|:---------\|:-------\|:---------------\|:---------\|:----------------\|:----------\|:---------\|:---------------\|:---------\|:----------\|:--------------\|:--------------\|:---------------\|:------\| \| 0 \| 11 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 10 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| \| X \| X \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| \| X \| X \| \| X \| \| \| X \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 9 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| \| X \| X \| \| \| \| \| X \| \| \| \| \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 6 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| \| \| X \| \| X \| \| \| X \| X \| \| X \| \| X \| X \| X \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| \| X \| X \| \| \| \| \| X \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 7 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| X \| \| X \| \| \| \| \| \| X \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| X \| X \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 9 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| X \| \| X \| \| \| \| \| \| \| \| \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 24 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| X \| \| \| X \| \| X \| \| \| X \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 6 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| X \| X \| \| \| X \| \| \| \| \| X \| \| \| X \| \| \| \| X \| \| X \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 11 \| 7 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| X \| X \| \| \| \| \| X \| \| \| X \| X \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 12 \| 5 \| ![](samples/12/clu12-sample0.png) \| ![](samples/12/clu12-sample1.png) \| ![](samples/12/clu12-sample2.png) \| ![](samples/12/clu12-sample3.png) \| ![](samples/12/clu12-sample4.png) \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 13 \| 7 \| ![](samples/13/clu13-sample0.png) \| ![](samples/13/clu13-sample1.png) \| ![](samples/13/clu13-sample2.png) \| ![](samples/13/clu13-sample3.png) \| ![](samples/13/clu13-sample4.png) \| X \| \| \| \| X \| \| X \| \| \| X \| X \| \| \| \| \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| 14 \| 5 \| ![](samples/14/clu14-sample0.png) \| ![](samples/14/clu14-sample1.png) \| ![](samples/14/clu14-sample2.png) \| ![](samples/14/clu14-sample3.png) \| ![](samples/14/clu14-sample4.png) \| X \| X \| \| \| \| \| X \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| 15 \| 8 \| ![](samples/15/clu15-sample0.png) \| ![](samples/15/clu15-sample1.png) \| ![](samples/15/clu15-sample2.png) \| ![](samples/15/clu15-sample3.png) \| ![](samples/15/clu15-sample4.png) \| X \| X \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \|
projecte-aina/vilaquad	--- annotations_creators: - expert-generated language_creators: - found language: - ca license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: VilaQuAD size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa --- # Dataset Card for VilaQuAD ## 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://doi.org/10.5281/zenodo.4562337 - Paper: [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903) - Point of Contact: langtech@bsc.es ### Dataset Summary VilaQuAD, An extractive QA dataset for Catalan, from [VilaWeb](https://www.vilaweb.cat/) newswire text. This dataset contains 2095 of Catalan language news articles along with 1 to 5 questions referring to each fragment (or context). VilaQuad articles are extracted from the daily [VilaWeb](https://www.vilaweb.cat/) and used under [CC-BY-NC-SA-ND](https://creativecommons.org/licenses/by-nc-nd/3.0/deed.ca) licence. This dataset can be used to build extractive-QA and Language Models. ### Supported Tasks and Leaderboards Extractive-QA, Language Model. ### Languages The dataset is in Catalan (`ca-ES`). ## Dataset Structure ### Data Instances ``` { 'id': 'P_556_C_556_Q1', 'title': "El Macba posa en qüestió l'eufòria amnèsica dels anys vuitanta a l'estat espanyol", 'context': "El Macba ha obert una nova exposició, 'Gelatina dura. Històries escamotejades dels 80', dedicada a revisar el discurs hegemònic que es va instaurar en aquella dècada a l'estat espanyol, concretament des del començament de la transició, el 1977, fins a la fita de Barcelona 92. És una mirada en clau espanyola, però també centralista, perquè més enllà dels esdeveniments ocorreguts a Catalunya i els artistes que els van combatre, pràcticament només s'hi mostren fets polítics i culturals generats des de Madrid. No es parla del País Basc, per exemple. Però, dit això, l'exposició revisa aquesta dècada de la història recent tot qüestionant un triomfalisme homogeneïtzador, que ja se sap que va arrasar una gran quantitat de sectors crítics i radicals de l'àmbit social, polític i cultural. Com diu la comissària, Teresa Grandas, de l'equip del Macba: 'El relat oficial dels anys vuitanta a l'estat espanyol va prioritzar la necessitat per damunt de la raó i va consolidar una mirada que privilegiava el futur abans que l'anàlisi del passat recent, obviant qualsevol consideració crítica respecte de la filiació amb el poder franquista.", 'question': 'Com es diu la nova exposició que ha obert el Macba?', 'answers': [ { 'text': "'Gelatina dura. Històries escamotejades dels 80'", 'answer_start': 38 } ] } ``` ### Data Fields Follows [Rajpurkar, Pranav et al., (2016)](http://arxiv.org/abs/1606.05250) for SQuAD v1 datasets. - `id` (str): Unique ID assigned to the question. - `title` (str): Title of the VilaWeb article. - `context` (str): VilaWeb section text. - `question` (str): Question. - `answers` (list): List of answers to the question, each containing: - `text` (str): Span text answering to the question. - `answer_start` Starting offset of the span text answering to the question. ### Data Splits - train.json: 1295 contexts, 3882 questions - dev.json: 400 contexts, 1200 questions - test.json: 400 contexts, 1200 questions ## Dataset Creation ### Curation Rationale We created this dataset to contribute to the development of language models in Catalan, a low-resource language. ### Source Data - [VilaWeb site](https://www.vilaweb.cat/) #### Initial Data Collection and Normalization The source data are scraped articles from archives of Catalan newspaper website [Vilaweb](https://www.vilaweb.cat). From a the online edition of the newspaper [VilaWeb](https://www.vilaweb.cat), 2095 articles were randomnly selected. These headlines were also used to create a Textual Entailment dataset. For the extractive QA dataset, creation of between 1 and 5 questions for each news context was commissioned, following an adaptation of the guidelines from SQuAD 1.0 ([Rajpurkar, Pranav et al. (2016)](http://arxiv.org/abs/1606.05250)). In total, 6282 pairs of a question and an extracted fragment that contains the answer were created. For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines. We also created [another QA dataset with wikipedia](https://huggingface.co/datasets/projecte-aina/viquiquad) to ensure thematic and stylistic variety. #### Who are the source language producers? CA Professional journalists from the Catalan newspaper [VilaWeb](https://www.vilaweb.cat/). ### Annotations #### Annotation process We comissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQuAD 1.0 ([Rajpurkar, Pranav et al. (2016)](http://arxiv.org/abs/1606.05250)). #### Who are the annotators? Annotation was commissioned to an specialized company that hired a team of native language speakers. ### Personal and Sensitive Information No personal or sensitive information included. ## Considerations for Using the Data ### Social Impact of Dataset We hope this dataset contributes to the development of language models in Catalan, a low-resource language. ### Discussion of Biases [N/A] ### Other Known Limitations [N/A] ## Additional Information ### Dataset Curators Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es) This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/en/inici/index.html) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/). ### Licensing Information This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International License</a>. ### Citation Information ``` @inproceedings{armengol-estape-etal-2021-multilingual, title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan", author = "Armengol-Estap{\'e}, Jordi and Carrino, Casimiro Pio and Rodriguez-Penagos, Carlos and de Gibert Bonet, Ona and Armentano-Oller, Carme and Gonzalez-Agirre, Aitor and Melero, Maite and Villegas, Marta", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.437", doi = "10.18653/v1/2021.findings-acl.437", pages = "4933--4946", } ``` [DOI](https://doi.org/10.5281/zenodo.4562337) ### Contributions [N/A]
AdapterOcean/Open_Platypus_standardized_cluster_8_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 3469650 num_examples: 3068 download_size: 1775180 dataset_size: 3469650 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Open_Platypus_standardized_cluster_8_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pravsels/manim_3b1b_code	--- dataset_info: features: - name: file_path dtype: string - name: content dtype: string splits: - name: train num_bytes: 887937 num_examples: 123 download_size: 336530 dataset_size: 887937 configs: - config_name: default data_files: - split: train path: data/train-* ---
AsphyXIA/Baarat-Kan-Summarization	--- dataset_info: features: - name: input dtype: string - name: target dtype: string splits: - name: train num_bytes: 97489736 num_examples: 140890 download_size: 40315874 dataset_size: 97489736 configs: - config_name: default data_files: - split: train path: data/train-* ---

youyu0105/llm-MIDI4

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 570535 num_examples: 335 download_size: 131987 dataset_size: 570535 --- # Dataset Card for "llm-MIDI4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/gr_mg36_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of gr_mg36/GrMG36/MG36 (Girls' Frontline) This is the dataset of gr_mg36/GrMG36/MG36 (Girls' Frontline), containing 26 images and their tags. The core tags of this character are `blue_eyes, blonde_hair, hair_ornament, bangs, long_hair, hair_over_one_eye, hairclip, breasts, ahoge, heterochromia, yellow_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 26 | 32.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mg36_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 26 | 16.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mg36_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 51 | 30.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mg36_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 26 | 26.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mg36_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 51 | 45.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/gr_mg36_girlsfrontline/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/gr_mg36_girlsfrontline', 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 | 26 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, black_gloves, closed_mouth, collarbone, white_background, bare_shoulders, fingerless_gloves, simple_background, socks, weapon | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | black_gloves | closed_mouth | collarbone | white_background | bare_shoulders | fingerless_gloves | simple_background | socks | weapon | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:---------------|:---------------|:-------------|:-------------------|:-----------------|:--------------------|:--------------------|:--------|:---------| | 0 | 26 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X |

japanese-asr/whisper_transcriptions.reazonspeech.all_17

--- dataset_info: config_name: all features: - name: name dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: transcription dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 30517145303.0 num_examples: 268223 download_size: 30279684051 dataset_size: 30517145303.0 configs: - config_name: all data_files: - split: train path: all/train-* ---

poorguys/TW-Kai_2_aoyagireisyosimo2_all_512

--- dataset_info: features: - name: char dtype: string - name: unicode dtype: string - name: images dtype: image - name: target_images dtype: image - name: stroke dtype: int32 - name: strokes_sequence sequence: int32 - name: components sequence: int32 - name: jyutping dtype: string splits: - name: train num_bytes: 438261774.25 num_examples: 6351 - name: test num_bytes: 2409980857.25 num_examples: 69931 download_size: 1959823420 dataset_size: 2848242631.5 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647

--- dataset_info: features: - name: id dtype: int64 - name: image dtype: image - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string - name: scores sequence: float64 splits: - name: fewshot_0_bs_16 num_bytes: 84811910.125 num_examples: 5647 - name: fewshot_1_bs_16 num_bytes: 86719029.125 num_examples: 5647 - name: fewshot_3_bs_16 num_bytes: 90542558.125 num_examples: 5647 - name: fewshot_5_bs_16 num_bytes: 94354619.125 num_examples: 5647 - name: fewshot_8_bs_16 num_bytes: 100058064.125 num_examples: 5647 download_size: 418819193 dataset_size: 456486180.625 --- # Dataset Card for "Caltech101_not_background_test_facebook_opt_1.3b_Visclues_ns_5647" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_CausalLM__7B

--- pretty_name: Evaluation run of CausalLM/7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [CausalLM/7B](https://huggingface.co/CausalLM/7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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_CausalLM__7B_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-19T10:15:27.073071](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__7B_public/blob/main/results_2023-11-19T10-15-27.073071.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\": 0.6094831324044202,\n\ \ \"acc_stderr\": 0.0327856640395233,\n \"acc_norm\": 0.6180866854509012,\n\ \ \"acc_norm_stderr\": 0.03347186592408746,\n \"mc1\": 0.3537331701346389,\n\ \ \"mc1_stderr\": 0.016737814358846147,\n \"mc2\": 0.5012670346064317,\n\ \ \"mc2_stderr\": 0.015282424019072406,\n \"em\": 0.3381921140939597,\n\ \ \"em_stderr\": 0.0048449283464877275,\n \"f1\": 0.4114880453020153,\n\ \ \"f1_stderr\": 0.00471092648573539\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.47013651877133106,\n \"acc_stderr\": 0.014585305840007102,\n\ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.014611390804670088\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5603465445130452,\n\ \ \"acc_stderr\": 0.004953305461311753,\n \"acc_norm\": 0.7457677753435571,\n\ \ \"acc_norm_stderr\": 0.00434538861452003\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5407407407407407,\n\ \ \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.5407407407407407,\n\ \ \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \ \ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n\ \ \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \ \ \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.02783491252754407,\n\ \ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.02783491252754407\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.56,\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\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7167630057803468,\n\ \ \"acc_stderr\": 0.034355680560478746,\n \"acc_norm\": 0.7167630057803468,\n\ \ \"acc_norm_stderr\": 0.034355680560478746\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n\ \ \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\ \ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.40350877192982454,\n\ \ \"acc_stderr\": 0.046151869625837026,\n \"acc_norm\": 0.40350877192982454,\n\ \ \"acc_norm_stderr\": 0.046151869625837026\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n\ \ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4444444444444444,\n \"acc_stderr\": 0.025591857761382175,\n \"\ acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.025591857761382175\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.04444444444444449,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.04444444444444449\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7516129032258064,\n\ \ \"acc_stderr\": 0.024580028921481003,\n \"acc_norm\": 0.7516129032258064,\n\ \ \"acc_norm_stderr\": 0.024580028921481003\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511657,\n\ \ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511657\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8181818181818182,\n \"acc_stderr\": 0.027479603010538808,\n \"\ acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.027479603010538808\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8549222797927462,\n \"acc_stderr\": 0.025416343096306443,\n\ \ \"acc_norm\": 0.8549222797927462,\n \"acc_norm_stderr\": 0.025416343096306443\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5974358974358974,\n \"acc_stderr\": 0.024864995159767755,\n\ \ \"acc_norm\": 0.5974358974358974,\n \"acc_norm_stderr\": 0.024864995159767755\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815635,\n \ \ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815635\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6008403361344538,\n \"acc_stderr\": 0.031811100324139266,\n\ \ \"acc_norm\": 0.6008403361344538,\n \"acc_norm_stderr\": 0.031811100324139266\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.039837983066598075,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.039837983066598075\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8220183486238533,\n \"acc_stderr\": 0.01639943636661291,\n \"\ acc_norm\": 0.8220183486238533,\n \"acc_norm_stderr\": 0.01639943636661291\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501947,\n \"\ acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501947\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \ \ \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6591928251121076,\n\ \ \"acc_stderr\": 0.0318114974705536,\n \"acc_norm\": 0.6591928251121076,\n\ \ \"acc_norm_stderr\": 0.0318114974705536\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.04010358942462203,\n\ \ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.04010358942462203\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\": 0.7314814814814815,\n\ \ \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n\ \ \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6932515337423313,\n \"acc_stderr\": 0.036230899157241474,\n\ \ \"acc_norm\": 0.6932515337423313,\n \"acc_norm_stderr\": 0.036230899157241474\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8376068376068376,\n\ \ \"acc_stderr\": 0.02416161812798774,\n \"acc_norm\": 0.8376068376068376,\n\ \ \"acc_norm_stderr\": 0.02416161812798774\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\": 0.80970625798212,\n\ \ \"acc_stderr\": 0.014036945850381387,\n \"acc_norm\": 0.80970625798212,\n\ \ \"acc_norm_stderr\": 0.014036945850381387\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6560693641618497,\n \"acc_stderr\": 0.025574123786546655,\n\ \ \"acc_norm\": 0.6560693641618497,\n \"acc_norm_stderr\": 0.025574123786546655\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2759776536312849,\n\ \ \"acc_stderr\": 0.01495010300247536,\n \"acc_norm\": 0.2759776536312849,\n\ \ \"acc_norm_stderr\": 0.01495010300247536\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.026787453111906497,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.026787453111906497\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.684887459807074,\n\ \ \"acc_stderr\": 0.02638527370346449,\n \"acc_norm\": 0.684887459807074,\n\ \ \"acc_norm_stderr\": 0.02638527370346449\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.691358024691358,\n \"acc_stderr\": 0.025702640260603746,\n\ \ \"acc_norm\": 0.691358024691358,\n \"acc_norm_stderr\": 0.025702640260603746\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.4219858156028369,\n \"acc_stderr\": 0.029462189233370593,\n \ \ \"acc_norm\": 0.4219858156028369,\n \"acc_norm_stderr\": 0.029462189233370593\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4954367666232073,\n\ \ \"acc_stderr\": 0.012769704263117526,\n \"acc_norm\": 0.4954367666232073,\n\ \ \"acc_norm_stderr\": 0.012769704263117526\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6360294117647058,\n \"acc_stderr\": 0.02922719246003203,\n\ \ \"acc_norm\": 0.6360294117647058,\n \"acc_norm_stderr\": 0.02922719246003203\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6160130718954249,\n \"acc_stderr\": 0.01967580813528152,\n \ \ \"acc_norm\": 0.6160130718954249,\n \"acc_norm_stderr\": 0.01967580813528152\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\": 0.726530612244898,\n \"acc_stderr\": 0.02853556033712845,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.02853556033712845\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\ \ \"acc_stderr\": 0.02519692987482705,\n \"acc_norm\": 0.8507462686567164,\n\ \ \"acc_norm_stderr\": 0.02519692987482705\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\": 0.46987951807228917,\n\ \ \"acc_stderr\": 0.03885425420866766,\n \"acc_norm\": 0.46987951807228917,\n\ \ \"acc_norm_stderr\": 0.03885425420866766\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.031267817146631786,\n\ \ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.031267817146631786\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3537331701346389,\n\ \ \"mc1_stderr\": 0.016737814358846147,\n \"mc2\": 0.5012670346064317,\n\ \ \"mc2_stderr\": 0.015282424019072406\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.696921862667719,\n \"acc_stderr\": 0.012916727462634458\n\ \ },\n \"harness|drop|3\": {\n \"em\": 0.3381921140939597,\n \ \ \"em_stderr\": 0.0048449283464877275,\n \"f1\": 0.4114880453020153,\n\ \ \"f1_stderr\": 0.00471092648573539\n },\n \"harness|gsm8k|5\": {\n\ \ \"acc\": 0.22971948445792267,\n \"acc_stderr\": 0.011586857544997503\n\ \ }\n}\n```" repo_url: https://huggingface.co/CausalLM/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: 2023_11_19T10_15_27.073071 path: - '**/details_harness|arc:challenge|25_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-11-19T10-15-27.073071.parquet' - config_name: harness_drop_3 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|drop|3_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|drop|3_2023-11-19T10-15-27.073071.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|gsm8k|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hellaswag|10_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-management|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-11-19T10-15-27.073071.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-management|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-virology|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T10-15-27.073071.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|truthfulqa:mc|0_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-11-19T10-15-27.073071.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_19T10_15_27.073071 path: - '**/details_harness|winogrande|5_2023-11-19T10-15-27.073071.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-11-19T10-15-27.073071.parquet' - config_name: results data_files: - split: 2023_11_19T10_15_27.073071 path: - results_2023-11-19T10-15-27.073071.parquet - split: latest path: - results_2023-11-19T10-15-27.073071.parquet --- # Dataset Card for Evaluation run of CausalLM/7B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/CausalLM/7B - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [CausalLM/7B](https://huggingface.co/CausalLM/7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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_CausalLM__7B_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-19T10:15:27.073071](https://huggingface.co/datasets/open-llm-leaderboard/details_CausalLM__7B_public/blob/main/results_2023-11-19T10-15-27.073071.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": 0.6094831324044202, "acc_stderr": 0.0327856640395233, "acc_norm": 0.6180866854509012, "acc_norm_stderr": 0.03347186592408746, "mc1": 0.3537331701346389, "mc1_stderr": 0.016737814358846147, "mc2": 0.5012670346064317, "mc2_stderr": 0.015282424019072406, "em": 0.3381921140939597, "em_stderr": 0.0048449283464877275, "f1": 0.4114880453020153, "f1_stderr": 0.00471092648573539 }, "harness|arc:challenge|25": { "acc": 0.47013651877133106, "acc_stderr": 0.014585305840007102, "acc_norm": 0.5, "acc_norm_stderr": 0.014611390804670088 }, "harness|hellaswag|10": { "acc": 0.5603465445130452, "acc_stderr": 0.004953305461311753, "acc_norm": 0.7457677753435571, "acc_norm_stderr": 0.00434538861452003 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.26, "acc_stderr": 0.0440844002276808, "acc_norm": 0.26, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5407407407407407, "acc_stderr": 0.04304979692464242, "acc_norm": 0.5407407407407407, "acc_norm_stderr": 0.04304979692464242 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.625, "acc_stderr": 0.039397364351956274, "acc_norm": 0.625, "acc_norm_stderr": 0.039397364351956274 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.02783491252754407, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.02783491252754407 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7361111111111112, "acc_stderr": 0.03685651095897532, "acc_norm": 0.7361111111111112, "acc_norm_stderr": 0.03685651095897532 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7167630057803468, "acc_stderr": 0.034355680560478746, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.034355680560478746 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.73, "acc_stderr": 0.04461960433384739, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384739 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5361702127659574, "acc_stderr": 0.032600385118357715, "acc_norm": 0.5361702127659574, "acc_norm_stderr": 0.032600385118357715 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.40350877192982454, "acc_stderr": 0.046151869625837026, "acc_norm": 0.40350877192982454, "acc_norm_stderr": 0.046151869625837026 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5241379310344828, "acc_stderr": 0.0416180850350153, "acc_norm": 0.5241379310344828, "acc_norm_stderr": 0.0416180850350153 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4444444444444444, "acc_stderr": 0.025591857761382175, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.025591857761382175 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04444444444444449, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04444444444444449 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7516129032258064, "acc_stderr": 0.024580028921481003, "acc_norm": 0.7516129032258064, "acc_norm_stderr": 0.024580028921481003 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4827586206896552, "acc_stderr": 0.035158955511657, "acc_norm": 0.4827586206896552, "acc_norm_stderr": 0.035158955511657 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009182, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009182 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8181818181818182, "acc_stderr": 0.027479603010538808, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.027479603010538808 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8549222797927462, "acc_stderr": 0.025416343096306443, "acc_norm": 0.8549222797927462, "acc_norm_stderr": 0.025416343096306443 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5974358974358974, "acc_stderr": 0.024864995159767755, "acc_norm": 0.5974358974358974, "acc_norm_stderr": 0.024864995159767755 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3074074074074074, "acc_stderr": 0.028133252578815635, "acc_norm": 0.3074074074074074, "acc_norm_stderr": 0.028133252578815635 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6008403361344538, "acc_stderr": 0.031811100324139266, "acc_norm": 0.6008403361344538, "acc_norm_stderr": 0.031811100324139266 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.39072847682119205, "acc_stderr": 0.039837983066598075, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.039837983066598075 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8220183486238533, "acc_stderr": 0.01639943636661291, "acc_norm": 0.8220183486238533, "acc_norm_stderr": 0.01639943636661291 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7549019607843137, "acc_stderr": 0.030190282453501947, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.030190282453501947 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7763713080168776, "acc_stderr": 0.027123298205229966, "acc_norm": 0.7763713080168776, "acc_norm_stderr": 0.027123298205229966 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6591928251121076, "acc_stderr": 0.0318114974705536, "acc_norm": 0.6591928251121076, "acc_norm_stderr": 0.0318114974705536 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7022900763358778, "acc_stderr": 0.04010358942462203, "acc_norm": 0.7022900763358778, "acc_norm_stderr": 0.04010358942462203 }, "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": 0.7314814814814815, "acc_stderr": 0.042844679680521934, "acc_norm": 0.7314814814814815, "acc_norm_stderr": 0.042844679680521934 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6932515337423313, "acc_stderr": 0.036230899157241474, "acc_norm": 0.6932515337423313, "acc_norm_stderr": 0.036230899157241474 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.5, "acc_stderr": 0.04745789978762494, "acc_norm": 0.5, "acc_norm_stderr": 0.04745789978762494 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8376068376068376, "acc_stderr": 0.02416161812798774, "acc_norm": 0.8376068376068376, "acc_norm_stderr": 0.02416161812798774 }, "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": 0.80970625798212, "acc_stderr": 0.014036945850381387, "acc_norm": 0.80970625798212, "acc_norm_stderr": 0.014036945850381387 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6560693641618497, "acc_stderr": 0.025574123786546655, "acc_norm": 0.6560693641618497, "acc_norm_stderr": 0.025574123786546655 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2759776536312849, "acc_stderr": 0.01495010300247536, "acc_norm": 0.2759776536312849, "acc_norm_stderr": 0.01495010300247536 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6764705882352942, "acc_stderr": 0.026787453111906497, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.026787453111906497 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.684887459807074, "acc_stderr": 0.02638527370346449, "acc_norm": 0.684887459807074, "acc_norm_stderr": 0.02638527370346449 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.691358024691358, "acc_stderr": 0.025702640260603746, "acc_norm": 0.691358024691358, "acc_norm_stderr": 0.025702640260603746 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4219858156028369, "acc_stderr": 0.029462189233370593, "acc_norm": 0.4219858156028369, "acc_norm_stderr": 0.029462189233370593 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4954367666232073, "acc_stderr": 0.012769704263117526, "acc_norm": 0.4954367666232073, "acc_norm_stderr": 0.012769704263117526 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6360294117647058, "acc_stderr": 0.02922719246003203, "acc_norm": 0.6360294117647058, "acc_norm_stderr": 0.02922719246003203 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6160130718954249, "acc_stderr": 0.01967580813528152, "acc_norm": 0.6160130718954249, "acc_norm_stderr": 0.01967580813528152 }, "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": 0.726530612244898, "acc_stderr": 0.02853556033712845, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.02853556033712845 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8507462686567164, "acc_stderr": 0.02519692987482705, "acc_norm": 0.8507462686567164, "acc_norm_stderr": 0.02519692987482705 }, "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": 0.46987951807228917, "acc_stderr": 0.03885425420866766, "acc_norm": 0.46987951807228917, "acc_norm_stderr": 0.03885425420866766 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7894736842105263, "acc_stderr": 0.031267817146631786, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.031267817146631786 }, "harness|truthfulqa:mc|0": { "mc1": 0.3537331701346389, "mc1_stderr": 0.016737814358846147, "mc2": 0.5012670346064317, "mc2_stderr": 0.015282424019072406 }, "harness|winogrande|5": { "acc": 0.696921862667719, "acc_stderr": 0.012916727462634458 }, "harness|drop|3": { "em": 0.3381921140939597, "em_stderr": 0.0048449283464877275, "f1": 0.4114880453020153, "f1_stderr": 0.00471092648573539 }, "harness|gsm8k|5": { "acc": 0.22971948445792267, "acc_stderr": 0.011586857544997503 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

ewhfef/mix_cpt

--- dataset_info: features: - name: prompt dtype: string splits: - name: train num_bytes: 426850969 num_examples: 29635 download_size: 195835115 dataset_size: 426850969 configs: - config_name: default data_files: - split: train path: data/train-* --- A mix of part of datasets: pubmed, pubmed_qa and alpaca

Otherwa/GenAi-Public-Response

--- license: openrail language: - en tags: - code - legal - finance - biology - chemistry - music - art - medical - climate size_categories: - n<1K ---

tyzhu/squad_qa_no_id_v5_full_recite_ans_sent_no_permute_rerun

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 7992127.884496851 num_examples: 4778 - name: validation num_bytes: 402971 num_examples: 300 download_size: 1428626 dataset_size: 8395098.88449685 --- # Dataset Card for "squad_qa_no_id_v5_full_recite_ans_sent_no_permute_rerun" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

irds/car_v1.5_trec-y1_manual

--- pretty_name: '`car/v1.5/trec-y1/manual`' viewer: false source_datasets: ['irds/car_v1.5'] task_categories: - text-retrieval --- # Dataset Card for `car/v1.5/trec-y1/manual` The `car/v1.5/trec-y1/manual` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/car#car/v1.5/trec-y1/manual). # Data This dataset provides: - `qrels`: (relevance assessments); count=29,571 - For `docs`, use [`irds/car_v1.5`](https://huggingface.co/datasets/irds/car_v1.5) ## Usage ```python from datasets import load_dataset qrels = load_dataset('irds/car_v1.5_trec-y1_manual', '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 ``` @inproceedings{Dietz2017TrecCar, title={TREC Complex Answer Retrieval Overview.}, author={Dietz, Laura and Verma, Manisha and Radlinski, Filip and Craswell, Nick}, booktitle={TREC}, year={2017} } @article{Dietz2017Car, title={{TREC CAR}: A Data Set for Complex Answer Retrieval}, author={Laura Dietz and Ben Gamari}, year={2017}, note={Version 1.5}, url={http://trec-car.cs.unh.edu} } ```

vigneshgs7/Boundary_detection_Doc_2

--- dataset_info: features: - name: name dtype: string - name: uuid dtype: string - name: status dtype: string - name: image dtype: image - name: label.annotations list: - name: id dtype: int32 - name: category_id dtype: int32 - name: label.segmentation_bitmap dtype: image splits: - name: train num_bytes: 4375553760.0 num_examples: 88 download_size: 286343850 dataset_size: 4375553760.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

qubitbucket/embeddings-tutorial

--- license: apache-2.0 ---

Nxrd/michaoficial

--- license: openrail ---

fairlabs/aihub-nmt-dataset-2022-07

--- dataset_info: features: - name: en dtype: string - name: ko dtype: string splits: - name: train num_bytes: 447177389 num_examples: 1200144 - name: validation num_bytes: 44752356.769347675 num_examples: 120018 - name: test num_bytes: 11186411.230652321 num_examples: 30000 download_size: 326090070 dataset_size: 503116157.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

warzin/covers

--- license: other license_name: seila license_link: LICENSE ---

sushvij/generativeaisample

--- license: openrail language: - en pretty_name: gai ---

heliosprime/twitter_dataset_1713203122

--- 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: 28747 num_examples: 78 download_size: 23120 dataset_size: 28747 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713203122" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nakkhatra/trial_bn

--- license: cc0-1.0 ---

ylacombe/librispeech_asr_tags

--- dataset_info: - config_name: clean features: - name: file dtype: string - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: float64 - name: phonemes dtype: string - name: gender dtype: string splits: - name: train.100 num_bytes: 17998991 num_examples: 28539 - name: train.360 num_bytes: 65429327 num_examples: 104014 - name: validation num_bytes: 1238969 num_examples: 2703 - name: test num_bytes: 1205066 num_examples: 2620 download_size: 40197691 dataset_size: 85872353 - config_name: other features: - name: file dtype: string - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: utterance_pitch_mean dtype: float32 - name: utterance_pitch_std dtype: float32 - name: snr dtype: float64 - name: c50 dtype: float64 - name: speaking_rate dtype: float64 - name: phonemes dtype: string splits: - name: train.500 num_bytes: 87768115 num_examples: 148688 - name: validation num_bytes: 1196395 num_examples: 2864 - name: test num_bytes: 1228421 num_examples: 2939 download_size: 42452591 dataset_size: 90192931 configs: - config_name: clean data_files: - split: train.100 path: clean/train.100-* - split: train.360 path: clean/train.360-* - split: validation path: clean/validation-* - split: test path: clean/test-* - config_name: other data_files: - split: train.500 path: other/train.500-* - split: validation path: other/validation-* - split: test path: other/test-* ---

Prince3069/Speedbolt

--- license: apache-2.0 ---

alpayariyak/SkunkData-Corpus-Clusters

--- configs: - config_name: default data_files: - split: config32 path: data/config32-* 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 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: config32 num_bytes: 35728713 num_examples: 58432 download_size: 14314061 dataset_size: 35728713 --- # Dataset Card for "SkunkData-Corpus-Clusters" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_liminerity__Blur-7b-v1.2

--- pretty_name: Evaluation run of liminerity/Blur-7b-v1.2 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [liminerity/Blur-7b-v1.2](https://huggingface.co/liminerity/Blur-7b-v1.2) 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_liminerity__Blur-7b-v1.2\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-18T13:00:27.961191](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__Blur-7b-v1.2/blob/main/results_2024-01-18T13-00-27.961191.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\": 0.6357129950975389,\n\ \ \"acc_stderr\": 0.03262066192131251,\n \"acc_norm\": 0.6382762311799055,\n\ \ \"acc_norm_stderr\": 0.03328259277014658,\n \"mc1\": 0.43451652386780903,\n\ \ \"mc1_stderr\": 0.017352738749259564,\n \"mc2\": 0.6030326315591199,\n\ \ \"mc2_stderr\": 0.015260409379504259\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6254266211604096,\n \"acc_stderr\": 0.01414419347189345,\n\ \ \"acc_norm\": 0.6535836177474402,\n \"acc_norm_stderr\": 0.013905011180063223\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6528579964150567,\n\ \ \"acc_stderr\": 0.00475088440109516,\n \"acc_norm\": 0.8387771360286795,\n\ \ \"acc_norm_stderr\": 0.0036698484004877773\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5851851851851851,\n\ \ \"acc_stderr\": 0.04256193767901408,\n \"acc_norm\": 0.5851851851851851,\n\ \ \"acc_norm_stderr\": 0.04256193767901408\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\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\ \ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7013888888888888,\n\ \ \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n\ \ \"acc_norm_stderr\": 0.03827052357950756\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\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\"\ : 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\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\": 0.6184971098265896,\n\ \ \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.6184971098265896,\n\ \ \"acc_norm_stderr\": 0.03703851193099521\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\":\ \ 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\": 0.74,\n\ \ \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\ \ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\ \ \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.45614035087719296,\n\ \ \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n\ \ \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778405,\n \"\ acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778405\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\ \ \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.40476190476190477,\n\ \ \"acc_norm_stderr\": 0.04390259265377562\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7612903225806451,\n\ \ \"acc_stderr\": 0.02425107126220884,\n \"acc_norm\": 0.7612903225806451,\n\ \ \"acc_norm_stderr\": 0.02425107126220884\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.541871921182266,\n \"acc_stderr\": 0.03505630140785741,\n\ \ \"acc_norm\": 0.541871921182266,\n \"acc_norm_stderr\": 0.03505630140785741\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\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\"\ : 0.803030303030303,\n \"acc_stderr\": 0.02833560973246336,\n \"acc_norm\"\ : 0.803030303030303,\n \"acc_norm_stderr\": 0.02833560973246336\n },\n\ \ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \ \ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758723,\n\ \ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758723\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6512820512820513,\n \"acc_stderr\": 0.02416278028401772,\n \ \ \"acc_norm\": 0.6512820512820513,\n \"acc_norm_stderr\": 0.02416278028401772\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \ \ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.02995382389188704,\n \ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.02995382389188704\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\ acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8091743119266055,\n \"acc_stderr\": 0.016847676400091095,\n \"\ acc_norm\": 0.8091743119266055,\n \"acc_norm_stderr\": 0.016847676400091095\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\ acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"\ acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7679324894514767,\n \"acc_stderr\": 0.027479744550808514,\n \ \ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.027479744550808514\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\ \ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\ \ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7404580152671756,\n \"acc_stderr\": 0.03844876139785271,\n\ \ \"acc_norm\": 0.7404580152671756,\n \"acc_norm_stderr\": 0.03844876139785271\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\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7361963190184049,\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\": 0.5,\n\ \ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\ \ \"acc_stderr\": 0.023086635086841403,\n \"acc_norm\": 0.8547008547008547,\n\ \ \"acc_norm_stderr\": 0.023086635086841403\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\": 0.8186462324393359,\n\ \ \"acc_stderr\": 0.013778693778464073,\n \"acc_norm\": 0.8186462324393359,\n\ \ \"acc_norm_stderr\": 0.013778693778464073\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.0246853168672578,\n\ \ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.0246853168672578\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4782122905027933,\n\ \ \"acc_stderr\": 0.016706617522176136,\n \"acc_norm\": 0.4782122905027933,\n\ \ \"acc_norm_stderr\": 0.016706617522176136\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.02609016250427905,\n\ \ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.02609016250427905\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\ \ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\ \ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7006172839506173,\n \"acc_stderr\": 0.025483115601195448,\n\ \ \"acc_norm\": 0.7006172839506173,\n \"acc_norm_stderr\": 0.025483115601195448\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.43617021276595747,\n \"acc_stderr\": 0.02958345203628407,\n \ \ \"acc_norm\": 0.43617021276595747,\n \"acc_norm_stderr\": 0.02958345203628407\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4556714471968709,\n\ \ \"acc_stderr\": 0.012719949543032207,\n \"acc_norm\": 0.4556714471968709,\n\ \ \"acc_norm_stderr\": 0.012719949543032207\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.0290294228156814,\n\ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.0290294228156814\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6290849673202614,\n \"acc_stderr\": 0.019542101564854128,\n \ \ \"acc_norm\": 0.6290849673202614,\n \"acc_norm_stderr\": 0.019542101564854128\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.0289205832206756,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.0289205832206756\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \ \ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.02796678585916089,\n\ \ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.02796678585916089\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.43451652386780903,\n\ \ \"mc1_stderr\": 0.017352738749259564,\n \"mc2\": 0.6030326315591199,\n\ \ \"mc2_stderr\": 0.015260409379504259\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8058405682715075,\n \"acc_stderr\": 0.01111698339239267\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5284306292645944,\n \ \ \"acc_stderr\": 0.013750202076584422\n }\n}\n```" repo_url: https://huggingface.co/liminerity/Blur-7b-v1.2 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_18T13_00_27.961191 path: - '**/details_harness|arc:challenge|25_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-18T13-00-27.961191.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|gsm8k|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hellaswag|10_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-18T13-00-27.961191.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-management|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T13-00-27.961191.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|truthfulqa:mc|0_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-18T13-00-27.961191.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_18T13_00_27.961191 path: - '**/details_harness|winogrande|5_2024-01-18T13-00-27.961191.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-18T13-00-27.961191.parquet' - config_name: results data_files: - split: 2024_01_18T13_00_27.961191 path: - results_2024-01-18T13-00-27.961191.parquet - split: latest path: - results_2024-01-18T13-00-27.961191.parquet --- # Dataset Card for Evaluation run of liminerity/Blur-7b-v1.2  Dataset automatically created during the evaluation run of model [liminerity/Blur-7b-v1.2](https://huggingface.co/liminerity/Blur-7b-v1.2) 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_liminerity__Blur-7b-v1.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-18T13:00:27.961191](https://huggingface.co/datasets/open-llm-leaderboard/details_liminerity__Blur-7b-v1.2/blob/main/results_2024-01-18T13-00-27.961191.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": 0.6357129950975389, "acc_stderr": 0.03262066192131251, "acc_norm": 0.6382762311799055, "acc_norm_stderr": 0.03328259277014658, "mc1": 0.43451652386780903, "mc1_stderr": 0.017352738749259564, "mc2": 0.6030326315591199, "mc2_stderr": 0.015260409379504259 }, "harness|arc:challenge|25": { "acc": 0.6254266211604096, "acc_stderr": 0.01414419347189345, "acc_norm": 0.6535836177474402, "acc_norm_stderr": 0.013905011180063223 }, "harness|hellaswag|10": { "acc": 0.6528579964150567, "acc_stderr": 0.00475088440109516, "acc_norm": 0.8387771360286795, "acc_norm_stderr": 0.0036698484004877773 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5851851851851851, "acc_stderr": 0.04256193767901408, "acc_norm": 0.5851851851851851, "acc_norm_stderr": 0.04256193767901408 }, "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": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7056603773584905, "acc_stderr": 0.02804918631569525, "acc_norm": 0.7056603773584905, "acc_norm_stderr": 0.02804918631569525 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7013888888888888, "acc_stderr": 0.03827052357950756, "acc_norm": 0.7013888888888888, "acc_norm_stderr": 0.03827052357950756 }, "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": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "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": 0.6184971098265896, "acc_stderr": 0.03703851193099521, "acc_norm": 0.6184971098265896, "acc_norm_stderr": 0.03703851193099521 }, "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": 0.74, "acc_stderr": 0.044084400227680794, "acc_norm": 0.74, "acc_norm_stderr": 0.044084400227680794 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5702127659574469, "acc_stderr": 0.03236214467715564, "acc_norm": 0.5702127659574469, "acc_norm_stderr": 0.03236214467715564 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.45614035087719296, "acc_stderr": 0.046854730419077895, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.046854730419077895 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.593103448275862, "acc_stderr": 0.04093793981266236, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266236 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41534391534391535, "acc_stderr": 0.025379524910778405, "acc_norm": 0.41534391534391535, "acc_norm_stderr": 0.025379524910778405 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.40476190476190477, "acc_stderr": 0.04390259265377562, "acc_norm": 0.40476190476190477, "acc_norm_stderr": 0.04390259265377562 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7612903225806451, "acc_stderr": 0.02425107126220884, "acc_norm": 0.7612903225806451, "acc_norm_stderr": 0.02425107126220884 }, 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"acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7142857142857143, "acc_stderr": 0.0289205832206756, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.0289205832206756 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness|hendrycksTest-virology|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8421052631578947, "acc_stderr": 0.02796678585916089, "acc_norm": 0.8421052631578947, "acc_norm_stderr": 0.02796678585916089 }, "harness|truthfulqa:mc|0": { "mc1": 0.43451652386780903, "mc1_stderr": 0.017352738749259564, "mc2": 0.6030326315591199, "mc2_stderr": 0.015260409379504259 }, "harness|winogrande|5": { "acc": 0.8058405682715075, "acc_stderr": 0.01111698339239267 }, "harness|gsm8k|5": { "acc": 0.5284306292645944, "acc_stderr": 0.013750202076584422 } } ``` ## 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]

pphuc25/bailamvan

--- dataset_info: features: - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 9514569 num_examples: 888 download_size: 4680823 dataset_size: 9514569 --- # Dataset Card for "bailamvan" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

hecgo067/adv-ele

--- dataset_info: features: - name: ADV dtype: string - name: ELE dtype: string splits: - name: train num_bytes: 430918.56140350876 num_examples: 1732 - name: test num_bytes: 107978.43859649122 num_examples: 434 download_size: 296740 dataset_size: 538897.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

pdxmusic/splats

--- license: apache-2.0 ---

luizapzbn/goodtriever-data

--- license: apache-2.0 --- # Goodtriever This repository contains datasets and model generations from the `Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models` paper, published as a conference paper on EMNLP 2023. [[Paper]]()[[Code]]()[[Data]](https://huggingface.co/datasets/luizapzbn/goodtriever-data) - `data`: prompts and datasets used for datastore creation. - `continual_mitigation`: clustered WILDS data and prompts - `datastore_quality`: for the experiments on how automatic labeling impacts mitigation results - `jigsaw`: main dataset, jigsaw unintended bias - `outputs`: model generations and results for all experiments from the paper. - `alpha_temperature` - `datastore_quality` - `datastore_size` - `k_neighbors` - `model_families` (and main table results) # Citation

roborovski/phi-2-labeled

--- dataset_info: features: - name: hexsha dtype: string - name: size dtype: int64 - name: ext dtype: string - name: lang dtype: string - name: max_stars_repo_path dtype: string - name: max_stars_repo_name dtype: string - name: max_stars_repo_head_hexsha dtype: string - name: max_stars_repo_licenses sequence: string - name: max_stars_count dtype: int64 - name: max_stars_repo_stars_event_min_datetime dtype: string - name: max_stars_repo_stars_event_max_datetime dtype: string - name: max_issues_repo_path dtype: string - name: max_issues_repo_name dtype: string - name: max_issues_repo_head_hexsha dtype: string - name: max_issues_repo_licenses sequence: string - name: max_issues_count dtype: int64 - name: max_issues_repo_issues_event_min_datetime dtype: string - name: max_issues_repo_issues_event_max_datetime dtype: string - name: max_forks_repo_path dtype: string - name: max_forks_repo_name dtype: string - name: max_forks_repo_head_hexsha dtype: string - name: max_forks_repo_licenses sequence: string - name: max_forks_count dtype: int64 - name: max_forks_repo_forks_event_min_datetime dtype: string - name: max_forks_repo_forks_event_max_datetime dtype: string - name: content dtype: string - name: avg_line_length dtype: float64 - name: max_line_length dtype: int64 - name: alphanum_fraction dtype: float64 - name: label dtype: int64 - name: cost dtype: float64 splits: - name: train num_bytes: 283814677 num_examples: 50000 download_size: 112938830 dataset_size: 283814677 --- # Dataset Card for "phi-1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

djfelipe/cava

--- license: openrail ---

Nexdata/4001_People_Single_Object_Multi_view_Tracking_Data

--- license: cc-by-nc-nd-4.0 --- ## Description 4,001 People Single Object Multi-view Tracking Data, the data collection site includes indoor and outdoor scenes (such as supermarket, mall and community, etc.) , where each subject appeared in at least 7 cameras. The data diversity includes different ages, different time periods, different cameras, different human body orientations and postures, different collecting scenes. It can be used for computer vision tasks such as object detection and object tracking in multi-view scenes. For more details, please refer to the link: https://www.nexdata.ai/dataset/1231?source=Huggingface ## Data size 4,001 people, about 385-2,779 images per person ## Race distribution Asian ## Gender distribution 2,052 males, 1,949 females ## Age distribution from children to the elderly ## Collecting environment including indoor and outdoor scenes (such as supermarket, mall and community, etc.) ## Data diversity different ages, different time periods, different cameras, different human body orientations and postures, different collecting scenes ## Device surveillance cameras, the image resolution is not less 1,920*1,080 ## Data format the image data format is .jpg, the annotation file format is .json ## Annotation content human body rectangular bounding boxes ## Accuracy A rectangular bounding box of human body is qualified when the deviation is not more than 3 pixels, and the qualified rate of the bounding boxes shall not be lower than 97% # Licensing Information Commercial License

Loie/VGGSound

--- task_categories: - audio-classification size_categories: - 100B<n<1T --- # VGGSound VGG-Sound is an audio-visual correspondent dataset consisting of short clips of audio sounds, extracted from videos uploaded to YouTube. - **Homepage:** https://www.robots.ox.ac.uk/~vgg/data/vggsound/ - **Paper:** https://arxiv.org/abs/2004.14368 - **Github:** https://github.com/hche11/VGGSound ## Analysis - **310+ classes:** VGG-Sound contains audios spanning a large number of challenging acoustic environments and noise characteristics of real applications. - **200,000+ videos:** All videos are captured "in the wild" with audio-visual correspondence in the sense that the sound source is visually evident. - **550+ hours:** VGG-Sound consists of both audio and video. Each segment is 10 seconds long. ![](src/data.png) ## Download We provide a csv file. For each YouTube video, we provide YouTube URLs, time stamps, audio labels and train/test split. Each line in the csv file has columns defined by here. ``` # YouTube ID, start seconds, label, train/test split. ``` And you can download VGGSound directly from this [repository](https://huggingface.co/datasets/Loie/VGGSound/tree/main). ## License The VGG-Sound dataset is available to download for commercial/research purposes under a Creative Commons Attribution 4.0 International License. The copyright remains with the original owners of the video. A complete version of the license can be found [here](https://thor.robots.ox.ac.uk/datasets/vggsound/license_vggsound.txt). ## Citation Please cite the following if you make use of the dataset. ``` @InProceedings{Chen20, author = "Honglie Chen and Weidi Xie and Andrea Vedaldi and Andrew Zisserman", title = "VGGSound: A Large-scale Audio-Visual Dataset", booktitle = "International Conference on Acoustics, Speech, and Signal Processing (ICASSP)", year = "2020", } ```

pacovaldez/stackoverflow-questions-2016

--- annotations_creators: - machine-generated language: - en language_creators: - found license: - apache-2.0 multilinguality: - monolingual pretty_name: stackoverflow_post_questions size_categories: - 1M<n<10M source_datasets: - original tags: - stackoverflow - technical questions task_categories: - text-classification task_ids: - multi-class-classification --- # Dataset Card for [Stackoverflow Post Questions] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Source Data](#source-data) - [Contributions](#contributions) ## Dataset Description Companies that sell Open-source software tools usually hire an army of Customer representatives to try to answer every question asked about their tool. The first step in this process is the prioritization of the question. The classification scale usually consists of 4 values, P0, P1, P2, and P3, with different meanings across every participant in the industry. On the other hand, every software developer in the world has dealt with Stack Overflow (SO); the amount of shared knowledge there is incomparable to any other website. Questions in SO are usually annotated and curated by thousands of people, providing metadata about the quality of the question. This dataset aims to provide an accurate prioritization for programming questions. ### Dataset Summary The dataset contains the title and body of stackoverflow questions and a label value(0,1,2,3) that was calculated using thresholds defined by SO badges. ### Languages English ## Dataset Structure title: string, body: string, label: int ### Data Splits The split is 40/40/20, where classes have been balaned to be around the same size. ## Dataset Creation The data set was extracted and labeled with the following query in BigQuery: ``` SELECT title, body, CASE WHEN score >= 100 OR favorite_count >= 100 OR view_count >= 10000 THEN 0 WHEN score >= 25 OR favorite_count >= 25 OR view_count >= 2500 THEN 1 WHEN score >= 10 OR favorite_count >= 10 OR view_count >= 1000 THEN 2 ELSE 3 END AS label FROM `bigquery-public-data`.stackoverflow.posts_questions ``` ### Source Data The data was extracted from the Big Query public dataset: `bigquery-public-data.stackoverflow.posts_questions` #### Initial Data Collection and Normalization The original dataset contained high class imbalance: label count 0 977424 1 2401534 2 3418179 3 16222990 Grand Total 23020127 The data was sampled from each class to have around the same amount of records on every class. ### Contributions Thanks to [@pacofvf](https://github.com/pacofvf) for adding this dataset.

youssef101/artelingo

--- license: other task_categories: - text-generation - text-classification - image-classification - image-to-text - text-to-image language: - en - ar - zh tags: - art - Affective Captioning - Emotions - Emotion Prediction - Image Captioning - Multilingual - Cultural - Diversity pretty_name: ArtELingo size_categories: - 10K<n<100K - 100K<n<1M - 1M<n<10M multilinguality: - multilingual source_datasets: - original --- # Dataset Card for "ArtELingo" ## 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) - [Dataset Configurations](#dataset-configurations) - [Data Fields](#data-fields) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [artelingo.org/](https://www.artelingo.org/) - **Repository:** [More Information Needed](https://github.com/Vision-CAIR/artelingo) - **Paper:** [More Information Needed](https://arxiv.org/abs/2211.10780) - **Point of Contact:** [More Information Needed](artelingo.dataset@gmail.com) ### Dataset Summary ArtELingo is a benchmark and dataset introduced in a research paper aimed at promoting work on diversity across languages and cultures. It is an extension of ArtEmis, which is a collection of 80,000 artworks from WikiArt with 450,000 emotion labels and English-only captions. ArtELingo expands this dataset by adding 790,000 annotations in Arabic and Chinese. The purpose of these additional annotations is to evaluate the performance of "cultural-transfer" in AI systems. The goal of ArtELingo is to encourage research on multilinguality and culturally-aware AI. By including annotations in multiple languages and considering cultural differences, the dataset aims to build more human-compatible AI that is sensitive to emotional nuances across various cultural contexts. The researchers believe that studying emotions in this way is crucial to understanding a significant aspect of human intelligence. ### Supported Tasks and Leaderboards We have two tasks: - [Emotion Label Prediction](https://eval.ai/web/challenges/challenge-page/2106/overview) - [Affective Image Captioning](https://eval.ai/web/challenges/challenge-page/2104/overview) Both challenges have a leaderboard on Eval.ai. Submission deadlines can be viewed from the above links. In addition, we are hosting the challenge at the ICCV23 workshop [WECIA](https://iccv23-wecia.github.io/). We have cash prizes for winners. ### Languages We have 3 languages: English, Arabic, and Chinese. For each image, we have at least 5 captions in each language. In total we have 80,000 images which are downloaded automatically with the dataset. ## Dataset Structure We show detailed information for all the configurations of the dataset. ### Dataset Configurations We have 4 Configurations: #### artelingo - **Size of downloaded dataset files:** 23 GB - **Splits:** \['train', 'test', 'val'\] - **Number of Samples per splits:** \[920K, 94.1K, 46.9K\] - **Loading Script**: ```python from datasets import load_dataset dataset = load_dataset(path="youssef101/artelingo", name='artelingo') ``` you can also provide a `splits:LIST(str)` parameter to avoid downloading the huge files for all the splits. (especially the train set :)) ```python from datasets import load_dataset dataset = load_dataset(path="youssef101/artelingo", name='artelingo', splits=['val']) ``` Notice that this deems the next dev configuration redundant. #### dev - **Size of downloaded dataset files:** 3 GB - **Splits:** \['test', 'val'\] - **Number of Samples per splits:** \[94.1K, 46.9K\] - **Loading Script**: ```python from datasets import load_dataset dataset = load_dataset(path="youssef101/artelingo", name='dev') ``` #### wecia-emo Intended for the [WECIA](https://iccv23-wecia.github.io/) emotion prediction challenge. Instances does not have the emotion or the language attributes. - **Size of downloaded dataset files:** 1.2 GB - **Splits:** \['dev'\] - **Number of Samples per splits:** \[27.9K\] - **Loading Script**: ```python from datasets import load_dataset dataset = load_dataset(path="youssef101/artelingo", name='wecia-emo') ``` #### wecia-cap Intended for the [WECIA](https://iccv23-wecia.github.io/) affective caption generation challenge. Instances does not have the text. - **Size of downloaded dataset files:** 1.2 GB - **Splits:** \['dev'\] - **Number of Samples per splits:** \[16.3K\] - **Loading Script**: ```python from datasets import load_dataset dataset = load_dataset(path="youssef101/artelingo", name='wecia-cap') ``` ### Data Fields The data fields are the same among all configs. - `uid`: a `int32` feature. A unique identifier for each instance. - `image`: a `PIL.Image` feature. The image of the artwork from the wikiart dataset. - `art_style`: a `string` feature. The art style of the artwork. Styles are a subset from the [wikiart styles](https://www.wikiart.org/en/paintings-by-style). - `painting`: a `string` feature. The name of the painting according to the wikiart dataset. - `emotion`: a `string` feature. The emotion associated with the image caption pair. - `language`: a `string` feature. The language used to write the caption. - `text`: a `string` feature. The affective caption that describes the painting under the context of the selected emotion. ## Dataset Creation ### Curation Rationale ArtELingo is a benchmark and dataset designed to promote research on diversity across languages and cultures. It builds upon ArtEmis, a collection of 80,000 artworks from WikiArt with 450,000 emotion labels and English-only captions. ArtELingo extends this dataset by adding 790,000 annotations in Arabic and Chinese, as well as 4,800 annotations in Spanish, allowing for the evaluation of "cultural-transfer" performance in AI systems. With many artworks having multiple annotations in three languages, the dataset enables the investigation of similarities and differences across linguistic and cultural contexts. Additionally, ArtELingo explores captioning tasks, demonstrating how diversity in annotations can improve the performance of baseline AI models. The hope is that ArtELingo will facilitate future research on multilinguality and culturally-aware AI. The dataset is publicly available, including standard splits and baseline models, to support and ease further research in this area. ### Source Data #### Initial Data Collection and Normalization ArtELingo uses images from the [wikiart dataset](https://www.wikiart.org/). The images are mainly artworks since they are created with the intention to have an emotional impact on the viewer. ArtELingo assumes that WikiArt is a representative sample of the cultures of interest. While WikiArt is remarkably comprehensive, it has better coverage of the West than other regions of the world based on WikiArt’s assignment of artworks to nationalities. The data was collected via Amazon Mechanical Turk, where only native speakers were allowed to annotate the images. The English, Arabic, and Chinese subsets were collected by 6377, 656, and 745 workers respectively. All workers were compensated with above minimal wage in each respective country. #### Who are the source language producers? The data comes from Human annotators who natively speak each respective language. ## Considerations for Using the Data ### Social Impact of Dataset When using the ArtELingo dataset, researchers and developers must be mindful of the potential social impact of the data. Emotions, cultural expressions, and artistic representations can be sensitive topics, and AI systems trained on such data may have implications on how they perceive and respond to users. It is crucial to ensure that the dataset's usage does not perpetuate stereotypes or biases related to specific cultures or languages. Ethical considerations should be taken into account during the development and deployment of AI models trained on ArtELingo to avoid any harmful consequences on individuals or communities. ### Discussion of Biases ArtELingo was filtered against hate speech, racism, and obvious stereotypes. However, Like any dataset, ArtELingo may contain inherent biases that could influence the performance and behavior of AI systems. These biases could arise from various sources, such as cultural differences in emotional interpretations, variations in annotator perspectives, or imbalances in the distribution of annotations across languages and cultures. Researchers should be cautious about potential biases that might impact the dataset's outcomes and address them appropriately. Transparently discussing and documenting these biases is essential to facilitate a fair understanding of the dataset's limitations and potential areas of improvement. ## Additional Information ### Dataset Curators The corpus was put together by [Youssef Mohamed](https://cemse.kaust.edu.sa/people/person/youssef-s-mohamed), [Mohamed Abdelfattah](https://people.epfl.ch/mohamed.abdelfattah/?lang=en), [Shyma Alhuwaider](https://cemse.kaust.edu.sa/aanslab/people/person/shyma-y-alhuwaider), [Feifan Li](https://www.linkedin.com/in/feifan-li-3280a6249/), [Xiangliang Zhang](https://engineering.nd.edu/faculty/xiangliang-zhang/), [Kenneth Ward Church](https://www.khoury.northeastern.edu/people/kenneth-church/) and [Mohamed Elhoseiny](https://cemse.kaust.edu.sa/people/person/mohamed-elhoseiny). ### Licensing Information Terms of Use: Before we are able to offer you access to the database, please agree to the following terms of use. After approval, you (the 'Researcher') receive permission to use the ArtELingo database (the 'Database') at King Abdullah University of Science and Technology (KAUST). In exchange for being able to join the ArtELingo community and receive such permission, Researcher hereby agrees to the following terms and conditions: [1.] The Researcher shall use the Database only for non-commercial research and educational purposes. [2.] The Universities 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 Universities, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, and Researcher's use of any copies of copyrighted 2D artworks originally uploaded to http://www.wikiart.org that the Researcher may use in connection with 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 Universities 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. [7.] The international copyright laws shall apply to all disputes under this agreement. ### Citation Information ``` @inproceedings{mohamed2022artelingo, title={ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture}, author={Mohamed, Youssef and Abdelfattah, Mohamed and Alhuwaider, Shyma and Li, Feifan and Zhang, Xiangliang and Church, Kenneth and Elhoseiny, Mohamed}, booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, pages={8770--8785}, year={2022} } ``` ### Contributions Thanks to [@youssef101](https://github.com/Mo-youssef) for adding this dataset. [@Faizan](https://faixan-khan.github.io/) for testing.

Minata/70000_method2test_tokonized_ForCausalLM

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: test num_bytes: 598079592 num_examples: 89694 download_size: 109394438 dataset_size: 598079592 --- # Dataset Card for "70000_method2test_tokonized_ForCausalLM" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

lshowway/wikipedia.reorder.osv.fr

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 886603410 num_examples: 490371 download_size: 404858868 dataset_size: 886603410 --- # Dataset Card for "wikipedia.reorder.osv.fr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

joey234/mmlu-professional_psychology

--- 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: fewshot_context_neg dtype: string splits: - name: dev num_bytes: 5774 num_examples: 5 - name: test num_bytes: 3081725 num_examples: 612 download_size: 304580 dataset_size: 3087499 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* --- # Dataset Card for "mmlu-professional_psychology" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/hanabatake_yoshiko_ahogirl

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Hanabatake Yoshiko This is the dataset of Hanabatake Yoshiko, containing 200 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 463 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 463 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 463 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 463 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

MadNLP769/isacarsm

--- license: mit ---

Jzuluaga/uwb_atcc

--- dataset_info: features: - name: id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: segment_start_time dtype: float32 - name: segment_end_time dtype: float32 - name: duration dtype: float32 splits: - name: test num_bytes: 140620332.25 num_examples: 2822 - name: train num_bytes: 608597323.625 num_examples: 11291 download_size: 711464914 dataset_size: 749217655.875 tags: - audio - automatic-speech-recognition - en-atc - en - noisy-speech-recognition - speech-recognition task_categories: - automatic-speech-recognition language: - en multilinguality: - monolingual license: - cc-by-nc-sa-4.0 --- # Dataset Card for UWB-ATCC corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages and Other Details](#languages-and-other-details) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [UWB-ATCC corpus homepage](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0) - **Repository:** [GitHub repository (used in research)](https://github.com/idiap/w2v2-air-traffic) - **Paper:** [Air traffic control communication (ATCC) speech corpora and their use for ASR and TTS development](https://link.springer.com/article/10.1007/s10579-019-09449-5) - **Paper of this research:** [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822) ### Dataset Summary The UWB-ATCC Corpus is provided provided by University of West Bohemia, Department of Cybernetics. The corpus contains recordings of communication between air traffic controllers and pilots. The speech is manually transcribed and labeled with the information about the speaker (pilot/controller, not the full identity of the person). The corpus is currently small (20 hours) but we plan to search for additional data next year. The audio data format is: 8kHz, 16bit PCM, mono. Important, from the `<id (string)>` field, you can obtain the speaker roles. For instance: - `_PI`: segment with only pilot speech - `_AT`: segment with only ATCO speech - `PIAT`: segment with both, ATCO and pilot speech ### Supported Tasks and Leaderboards - `automatic-speech-recognition`. Already adapted/fine-tuned models are available here --> [XLS-R-300m](https://huggingface.co/Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim). ### Languages and other details The text and the recordings are in English. The authors took advantage of the fact that one of their industrial partners develops complex IT solutions for several ATC authorities and airports and, as such, has access to the ATC communication recordings collected in the Czech airspace. This partner was able to secure the following data: - Ground control—communication before takeoff and after landing—19.2 h of data. - Tower control—communication during takeoff, landing and landing standby—22.5 h. - Approach control—communication during landing approach—25.5 h. - Area control—communication during overflights and cruises—71.3 h. (Not all data is released. Check their website [here](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0)) ## Dataset Structure ### Data Fields - `id (string)`: a string of recording identifier for each example, corresponding to its. - `audio (audio)`: audio data for the given ID - `text (string)`: transcript of the file already normalized. Follow these repositories for more details [w2v2-air-traffic](https://github.com/idiap/w2v2-air-traffic) and [bert-text-diarization-atc](https://github.com/idiap/bert-text-diarization-atc) - `segment_start_time (float32)`: segment start time (normally 0) - `segment_end_time (float32): segment end time - `duration (float32)`: duration of the recording, compute as segment_end_time - segment_start_time ## Additional Information ### Licensing Information The licensing status of the dataset hinges on the legal status of the [UWB-ATCC corpus](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0) creators. They used [Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) licensing. ### Citation Information Contributors who prepared, processed, normalized and uploaded the dataset in HuggingFace: ``` @article{zuluaga2022how, title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } @article{zuluaga2022bertraffic, title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others}, journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar}, year={2022} } @article{zuluaga2022atco2, title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications}, author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others}, journal={arXiv preprint arXiv:2211.04054}, year={2022} } ``` Authors of the dataset: ``` @article{vsmidl2019air, title={Air traffic control communication (ATCC) speech corpora and their use for ASR and TTS development}, author={{\v{S}}m{\'\i}dl, Lubo{\v{s}} and {\v{S}}vec, Jan and Tihelka, Daniel and Matou{\v{s}}ek, Jind{\v{r}}ich and Romportl, Jan and Ircing, Pavel}, journal={Language Resources and Evaluation}, volume={53}, number={3}, pages={449--464}, year={2019}, publisher={Springer} } ```

McSpicyWithMilo/instruction-types-0.3split

--- dataset_info: features: - name: instruction_type dtype: string - name: instruction dtype: string splits: - name: train num_bytes: 24468 num_examples: 280 - name: test num_bytes: 10561 num_examples: 120 download_size: 18875 dataset_size: 35029 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "instruction-types-0.3split" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

TanveerAman/AMI-Corpus-Text-Summarization

--- task_categories: - summarization language: - en ---

CognitiveScience/data2

--- license: mit from: https://huggingface.co/datasets/saranya132/dialog_uid_gpt2 ---

amitness/logits-mt-ar-512

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: teacher_logits sequence: sequence: float64 - name: teacher_indices sequence: sequence: int64 - name: teacher_mask_indices sequence: int64 splits: - name: train num_bytes: 17102298098.701529 num_examples: 940987 - name: test num_bytes: 3018061158.5240602 num_examples: 166057 download_size: 7348415360 dataset_size: 20120359257.22559 --- # Dataset Card for "logits-mt-ar-512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CATIE-AQ/universal_dependencies_fr_gsd_fr_prompt_pos

--- language: - fr license: cc-by-sa-4.0 size_categories: - 100K<n<1M task_categories: - token-classification tags: - pos - DFP - french prompts annotations_creators: - found language_creators: - found multilinguality: - monolingual source_datasets: - universal_dependencies_fr_gsd --- # universal_dependencies_fr_gsd_fr_prompt_pos ## Summary **universal_dependencies_fr_gsd_fr_prompt_pos** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP). It contains **343,161** rows that can be used for a part-of-speech task. The original data (without prompts) comes from the dataset [universal_dependencies](https://huggingface.co/datasets/universal_dependencies) where only the French gsd split has been kept. A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al. ## Prompts used ### List 21 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement. ``` 'Extraire les classes des mots du texte suivant : '+text, 'Extrais les classes des mots du texte suivant : '+text, 'Extrayez les classes des mots du texte suivant : '+text, 'Isoler les classes des mots du texte suivant : '+text, 'Isole les classes des mots du texte suivant : '+text, 'Isolez les classes des mots du texte suivant : '+text, 'Dégager les classes des mots dans le texte : '+text, 'Dégage les classes des mots dans le texte : '+text, 'Dégagez les classes des mots dans le texte : '+text, 'Générer les classes des mots issues du texte suivant : '+text, 'Génère les classes des mots issues du texte suivant : '+text, 'Générez les classes des mots issues du texte suivant : '+text, 'Trouver les classes des mots du texte : '+text, 'Trouve les classes des mots du texte : '+text, 'Trouvez les classes des mots du texte : '+text, 'Repérer les classes des mots présentes dans le texte suivant : '+text, 'Repère les classes des mots présentes dans le texte suivant : '+text, 'Repérez les classes des mots présentes dans le texte suivant : '+text, 'Indiquer les classes des mots du texte :'+text, 'Indique les classes des mots du texte : '+text, 'Indiquez les classes des mots du texte : '+text ``` ### Features used in the prompts In the prompt list above, `text` and `targets` have been constructed from: ``` fr_gsd = load_dataset('universal_dependencies', 'fr_gsd') # text fr_gsd['train']['tokens'] = list(map(lambda i: ' '.join(fr_gsd['train']['tokens'][i]), range(len(fr_gsd['train']['tokens'])))) # targets fr_gsd['train']['upos'] = list(map(lambda x: x.replace("[","").replace("]","").replace('17','AUX').replace('16','VERB').replace('15','INTJ').replace('14','ADV').replace('13','_').replace('12','X').replace('11','PRON').replace('10','PROPN').replace('9','CCONJ').replace('8','DET').replace('7','PART').replace('6','ADJ').replace('5','SCONJ').replace('4','SYM').replace('3','NUM').replace('2','ADP').replace('1','PUNCT').replace('0','NOUN'), map(str,fr_gsd['train']['upos']))) ``` # Splits - `train` with 303,429 samples - `valid` with 30,996 samples - `test` with 8,736 samples # How to use? ``` from datasets import load_dataset dataset = load_dataset("CATIE-AQ/universal_dependencies_fr_gsd_fr_prompt_pos") ``` # Citation ## Original data > Contributors: de Marneffe, Marie-Catherine; Guillaume, Bruno; McDonald, Ryan; Suhr, Alane; Nivre, Joakim; Grioni, Matias; Dickerson, Carly; Perrier, Guy ## This Dataset > @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023, author = { {Centre Aquitain des Technologies de l'Information et Electroniques} }, title = { DFP (Revision 1d24c09) }, year = 2023, url = { https://huggingface.co/datasets/CATIE-AQ/DFP }, doi = { 10.57967/hf/1200 }, publisher = { Hugging Face } } ## License CC BY-SA 4.0

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/52026443

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 188 num_examples: 10 download_size: 1342 dataset_size: 188 --- # Dataset Card for "52026443" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Mamakhan/Tools

--- license: openrail ---

irds/antique_train_split200-valid

--- pretty_name: '`antique/train/split200-valid`' viewer: false source_datasets: ['irds/antique'] task_categories: - text-retrieval --- # Dataset Card for `antique/train/split200-valid` The `antique/train/split200-valid` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/antique#antique/train/split200-valid). # Data This dataset provides: - `queries` (i.e., topics); count=200 - `qrels`: (relevance assessments); count=2,193 - For `docs`, use [`irds/antique`](https://huggingface.co/datasets/irds/antique) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/antique_train_split200-valid', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/antique_train_split200-valid', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ...} ``` 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 ``` @inproceedings{Hashemi2020Antique, title={ANTIQUE: A Non-Factoid Question Answering Benchmark}, author={Helia Hashemi and Mohammad Aliannejadi and Hamed Zamani and Bruce Croft}, booktitle={ECIR}, year={2020} } ```

heliosprime/twitter_dataset_1713090697

--- 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: 6144 num_examples: 15 download_size: 10882 dataset_size: 6144 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713090697" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

seonglae/resrer-nq

--- dataset_info: features: - name: document_text dtype: string - name: long_answer_candidates list: - name: end_token dtype: int64 - name: start_token dtype: int64 - name: top_level dtype: bool - name: question_text dtype: string - name: annotations list: - name: annotation_id dtype: float64 - name: long_answer struct: - name: candidate_index dtype: int64 - name: end_token dtype: int64 - name: start_token dtype: int64 - name: short_answers list: - name: end_token dtype: int64 - name: start_token dtype: int64 - name: yes_no_answer dtype: string - name: document_url dtype: string - name: example_id dtype: int64 - name: long_answer_text dtype: string - name: short_answer_text dtype: string - name: split_id dtype: string - name: answer_exist_chunk dtype: bool - name: summarization_text dtype: string splits: - name: train num_bytes: 497862929.4517183 num_examples: 55113 download_size: 121306017 dataset_size: 497862929.4517183 configs: - config_name: default data_files: - split: train path: data/train-* ---

cstech/ssd-warp-2024

--- license: deepfloyd-if-license ---

CyberHarem/kurosaki_honoka_encouragementofclimb

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Kurosaki Honoka This is the dataset of Kurosaki Honoka, containing 82 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 | 82 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 196 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | raw-stage3-eyes | 228 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. | | 384x512 | 82 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x704 | 82 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x880 | 82 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 196 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 196 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-p512-640 | 172 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. | | stage3-eyes-640 | 228 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. | | stage3-eyes-800 | 228 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |

joey234/mmlu-professional_medicine-rule-neg-prepend

--- 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: neg_prompt dtype: string splits: - name: test num_bytes: 440522 num_examples: 272 download_size: 250093 dataset_size: 440522 --- # Dataset Card for "mmlu-professional_medicine-rule-neg-prepend" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

joheras/adv-ele

--- dataset_info: features: - name: ADV dtype: string - name: ELE dtype: string splits: - name: train num_bytes: 430918.56140350876 num_examples: 1732 - name: test num_bytes: 107978.43859649122 num_examples: 434 download_size: 299002 dataset_size: 538897.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "adv-ele" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_Isotonic__Mixnueza-Chat-6x32M-MoE

--- pretty_name: Evaluation run of Isotonic/Mixnueza-Chat-6x32M-MoE dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Isotonic/Mixnueza-Chat-6x32M-MoE](https://huggingface.co/Isotonic/Mixnueza-Chat-6x32M-MoE)\ \ 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_Isotonic__Mixnueza-Chat-6x32M-MoE\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-07T02:09:55.470077](https://huggingface.co/datasets/open-llm-leaderboard/details_Isotonic__Mixnueza-Chat-6x32M-MoE/blob/main/results_2024-04-07T02-09-55.470077.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\": 0.25475467189344064,\n\ \ \"acc_stderr\": 0.030639762090785793,\n \"acc_norm\": 0.2552581810114775,\n\ \ \"acc_norm_stderr\": 0.03144935013039305,\n \"mc1\": 0.25703794369645044,\n\ \ \"mc1_stderr\": 0.015298077509485083,\n \"mc2\": 0.4727026528122458,\n\ \ \"mc2_stderr\": 0.015699277111857743\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.18088737201365188,\n \"acc_stderr\": 0.011248574467407034,\n\ \ \"acc_norm\": 0.20392491467576793,\n \"acc_norm_stderr\": 0.011774262478702256\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2629954192391954,\n\ \ \"acc_stderr\": 0.004393601887506585,\n \"acc_norm\": 0.26528579964150567,\n\ \ \"acc_norm_stderr\": 0.004405829993258718\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \ \ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.22962962962962963,\n\ \ \"acc_stderr\": 0.03633384414073461,\n \"acc_norm\": 0.22962962962962963,\n\ \ \"acc_norm_stderr\": 0.03633384414073461\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.19078947368421054,\n \"acc_stderr\": 0.031975658210325,\n\ \ \"acc_norm\": 0.19078947368421054,\n \"acc_norm_stderr\": 0.031975658210325\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.2,\n\ \ \"acc_stderr\": 0.040201512610368445,\n \"acc_norm\": 0.2,\n \ \ \"acc_norm_stderr\": 0.040201512610368445\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.025288394502891356,\n\ \ \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.025288394502891356\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2638888888888889,\n\ \ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.2638888888888889,\n\ \ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \ \ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\ : 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847415,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847415\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2023121387283237,\n\ \ \"acc_stderr\": 0.030631145539198816,\n \"acc_norm\": 0.2023121387283237,\n\ \ \"acc_norm_stderr\": 0.030631145539198816\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\":\ \ 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \"acc_norm\": 0.22,\n\ \ \"acc_norm_stderr\": 0.041633319989322695\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.251063829787234,\n \"acc_stderr\": 0.028346963777162452,\n\ \ \"acc_norm\": 0.251063829787234,\n \"acc_norm_stderr\": 0.028346963777162452\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.19298245614035087,\n\ \ \"acc_stderr\": 0.037124548537213684,\n \"acc_norm\": 0.19298245614035087,\n\ \ \"acc_norm_stderr\": 0.037124548537213684\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2896551724137931,\n \"acc_stderr\": 0.03780019230438014,\n\ \ \"acc_norm\": 0.2896551724137931,\n \"acc_norm_stderr\": 0.03780019230438014\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"\ acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15079365079365079,\n\ \ \"acc_stderr\": 0.03200686497287392,\n \"acc_norm\": 0.15079365079365079,\n\ \ \"acc_norm_stderr\": 0.03200686497287392\n },\n \"harness|hendrycksTest-global_facts|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_biology|5\": {\n \"acc\": 0.3161290322580645,\n\ \ \"acc_stderr\": 0.02645087448904277,\n \"acc_norm\": 0.3161290322580645,\n\ \ \"acc_norm_stderr\": 0.02645087448904277\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\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.21212121212121213,\n \"acc_stderr\": 0.03192271569548299,\n\ \ \"acc_norm\": 0.21212121212121213,\n \"acc_norm_stderr\": 0.03192271569548299\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.20202020202020202,\n \"acc_stderr\": 0.028606204289229872,\n \"\ acc_norm\": 0.20202020202020202,\n \"acc_norm_stderr\": 0.028606204289229872\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.27461139896373055,\n \"acc_stderr\": 0.03221024508041154,\n\ \ \"acc_norm\": 0.27461139896373055,\n \"acc_norm_stderr\": 0.03221024508041154\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.3564102564102564,\n \"acc_stderr\": 0.024283140529467295,\n\ \ \"acc_norm\": 0.3564102564102564,\n \"acc_norm_stderr\": 0.024283140529467295\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.27037037037037037,\n \"acc_stderr\": 0.027080372815145668,\n \ \ \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.027080372815145668\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.3025210084033613,\n \"acc_stderr\": 0.02983796238829193,\n \ \ \"acc_norm\": 0.3025210084033613,\n \"acc_norm_stderr\": 0.02983796238829193\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763744,\n \"\ acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763744\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.22752293577981653,\n \"acc_stderr\": 0.017974463578776502,\n \"\ acc_norm\": 0.22752293577981653,\n \"acc_norm_stderr\": 0.017974463578776502\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\ \ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.22058823529411764,\n\ \ \"acc_stderr\": 0.02910225438967409,\n \"acc_norm\": 0.22058823529411764,\n\ \ \"acc_norm_stderr\": 0.02910225438967409\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.26582278481012656,\n \"acc_stderr\": 0.028756799629658342,\n\ \ \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.028756799629658342\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.27802690582959644,\n\ \ \"acc_stderr\": 0.030069584874494047,\n \"acc_norm\": 0.27802690582959644,\n\ \ \"acc_norm_stderr\": 0.030069584874494047\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.24427480916030533,\n \"acc_stderr\": 0.037683359597287434,\n\ \ \"acc_norm\": 0.24427480916030533,\n \"acc_norm_stderr\": 0.037683359597287434\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2727272727272727,\n \"acc_stderr\": 0.04065578140908705,\n \"\ acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.04065578140908705\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.18518518518518517,\n\ \ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.18518518518518517,\n\ \ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.3128834355828221,\n \"acc_stderr\": 0.036429145782924055,\n\ \ \"acc_norm\": 0.3128834355828221,\n \"acc_norm_stderr\": 0.036429145782924055\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.19642857142857142,\n\ \ \"acc_stderr\": 0.03770970049347018,\n \"acc_norm\": 0.19642857142857142,\n\ \ \"acc_norm_stderr\": 0.03770970049347018\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\": 0.2264957264957265,\n\ \ \"acc_stderr\": 0.027421007295392926,\n \"acc_norm\": 0.2264957264957265,\n\ \ \"acc_norm_stderr\": 0.027421007295392926\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.2848020434227331,\n\ \ \"acc_stderr\": 0.01613917409652258,\n \"acc_norm\": 0.2848020434227331,\n\ \ \"acc_norm_stderr\": 0.01613917409652258\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.22832369942196531,\n \"acc_stderr\": 0.022598703804321635,\n\ \ \"acc_norm\": 0.22832369942196531,\n \"acc_norm_stderr\": 0.022598703804321635\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\": 0.28938906752411575,\n\ \ \"acc_stderr\": 0.025755865922632924,\n \"acc_norm\": 0.28938906752411575,\n\ \ \"acc_norm_stderr\": 0.025755865922632924\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2345679012345679,\n \"acc_stderr\": 0.023576881744005723,\n\ \ \"acc_norm\": 0.2345679012345679,\n \"acc_norm_stderr\": 0.023576881744005723\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.2624113475177305,\n \"acc_stderr\": 0.026244920349843007,\n \ \ \"acc_norm\": 0.2624113475177305,\n \"acc_norm_stderr\": 0.026244920349843007\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.23728813559322035,\n\ \ \"acc_stderr\": 0.010865436690780264,\n \"acc_norm\": 0.23728813559322035,\n\ \ \"acc_norm_stderr\": 0.010865436690780264\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121593,\n\ \ \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121593\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.2173202614379085,\n \"acc_stderr\": 0.016684820929148587,\n \ \ \"acc_norm\": 0.2173202614379085,\n \"acc_norm_stderr\": 0.016684820929148587\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\": 0.23265306122448978,\n \"acc_stderr\": 0.02704925791589618,\n\ \ \"acc_norm\": 0.23265306122448978,\n \"acc_norm_stderr\": 0.02704925791589618\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23880597014925373,\n\ \ \"acc_stderr\": 0.030147775935409224,\n \"acc_norm\": 0.23880597014925373,\n\ \ \"acc_norm_stderr\": 0.030147775935409224\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\": 0.3313253012048193,\n\ \ \"acc_stderr\": 0.036643147772880864,\n \"acc_norm\": 0.3313253012048193,\n\ \ \"acc_norm_stderr\": 0.036643147772880864\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.0312678171466318,\n\ \ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.0312678171466318\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.25703794369645044,\n\ \ \"mc1_stderr\": 0.015298077509485083,\n \"mc2\": 0.4727026528122458,\n\ \ \"mc2_stderr\": 0.015699277111857743\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.505130228887135,\n \"acc_stderr\": 0.01405174596179052\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n }\n}\n```" repo_url: https://huggingface.co/Isotonic/Mixnueza-Chat-6x32M-MoE leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|arc:challenge|25_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-07T02-09-55.470077.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|gsm8k|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hellaswag|10_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-07T02-09-55.470077.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-management|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-07T02-09-55.470077.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|truthfulqa:mc|0_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-07T02-09-55.470077.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_07T02_09_55.470077 path: - '**/details_harness|winogrande|5_2024-04-07T02-09-55.470077.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-07T02-09-55.470077.parquet' - config_name: results data_files: - split: 2024_04_07T02_09_55.470077 path: - results_2024-04-07T02-09-55.470077.parquet - split: latest path: - results_2024-04-07T02-09-55.470077.parquet --- # Dataset Card for Evaluation run of Isotonic/Mixnueza-Chat-6x32M-MoE  Dataset automatically created during the evaluation run of model [Isotonic/Mixnueza-Chat-6x32M-MoE](https://huggingface.co/Isotonic/Mixnueza-Chat-6x32M-MoE) 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_Isotonic__Mixnueza-Chat-6x32M-MoE", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-07T02:09:55.470077](https://huggingface.co/datasets/open-llm-leaderboard/details_Isotonic__Mixnueza-Chat-6x32M-MoE/blob/main/results_2024-04-07T02-09-55.470077.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": 0.25475467189344064, "acc_stderr": 0.030639762090785793, "acc_norm": 0.2552581810114775, "acc_norm_stderr": 0.03144935013039305, "mc1": 0.25703794369645044, "mc1_stderr": 0.015298077509485083, "mc2": 0.4727026528122458, "mc2_stderr": 0.015699277111857743 }, "harness|arc:challenge|25": { "acc": 0.18088737201365188, "acc_stderr": 0.011248574467407034, "acc_norm": 0.20392491467576793, "acc_norm_stderr": 0.011774262478702256 }, "harness|hellaswag|10": { "acc": 0.2629954192391954, "acc_stderr": 0.004393601887506585, "acc_norm": 0.26528579964150567, "acc_norm_stderr": 0.004405829993258718 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.26, "acc_stderr": 0.04408440022768081, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768081 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.22962962962962963, "acc_stderr": 0.03633384414073461, "acc_norm": 0.22962962962962963, "acc_norm_stderr": 0.03633384414073461 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.19078947368421054, "acc_stderr": 0.031975658210325, "acc_norm": 0.19078947368421054, "acc_norm_stderr": 0.031975658210325 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.2, "acc_stderr": 0.040201512610368445, "acc_norm": 0.2, "acc_norm_stderr": 0.040201512610368445 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.21509433962264152, "acc_stderr": 0.025288394502891356, "acc_norm": 0.21509433962264152, "acc_norm_stderr": 0.025288394502891356 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2638888888888889, "acc_stderr": 0.03685651095897532, "acc_norm": 0.2638888888888889, "acc_norm_stderr": 0.03685651095897532 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.27, "acc_stderr": 0.044619604333847415, "acc_norm": 0.27, "acc_norm_stderr": 0.044619604333847415 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2023121387283237, "acc_stderr": 0.030631145539198816, "acc_norm": 0.2023121387283237, "acc_norm_stderr": 0.030631145539198816 }, "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": 0.22, "acc_stderr": 0.041633319989322695, "acc_norm": 0.22, "acc_norm_stderr": 0.041633319989322695 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.251063829787234, "acc_stderr": 0.028346963777162452, "acc_norm": 0.251063829787234, "acc_norm_stderr": 0.028346963777162452 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.19298245614035087, "acc_stderr": 0.037124548537213684, "acc_norm": 0.19298245614035087, "acc_norm_stderr": 0.037124548537213684 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2896551724137931, "acc_stderr": 0.03780019230438014, "acc_norm": 0.2896551724137931, "acc_norm_stderr": 0.03780019230438014 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.25396825396825395, "acc_stderr": 0.022418042891113942, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.022418042891113942 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.15079365079365079, "acc_stderr": 0.03200686497287392, "acc_norm": 0.15079365079365079, "acc_norm_stderr": 0.03200686497287392 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.3161290322580645, "acc_stderr": 0.02645087448904277, "acc_norm": 0.3161290322580645, "acc_norm_stderr": 0.02645087448904277 }, "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": 0.32, "acc_stderr": 0.04688261722621505, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.21212121212121213, "acc_stderr": 0.03192271569548299, "acc_norm": 0.21212121212121213, "acc_norm_stderr": 0.03192271569548299 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.20202020202020202, "acc_stderr": 0.028606204289229872, "acc_norm": 0.20202020202020202, "acc_norm_stderr": 0.028606204289229872 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.27461139896373055, "acc_stderr": 0.03221024508041154, "acc_norm": 0.27461139896373055, "acc_norm_stderr": 0.03221024508041154 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.3564102564102564, "acc_stderr": 0.024283140529467295, "acc_norm": 0.3564102564102564, "acc_norm_stderr": 0.024283140529467295 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.27037037037037037, "acc_stderr": 0.027080372815145668, "acc_norm": 0.27037037037037037, "acc_norm_stderr": 0.027080372815145668 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.3025210084033613, "acc_stderr": 0.02983796238829193, "acc_norm": 0.3025210084033613, "acc_norm_stderr": 0.02983796238829193 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2781456953642384, "acc_stderr": 0.03658603262763744, "acc_norm": 0.2781456953642384, "acc_norm_stderr": 0.03658603262763744 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.22752293577981653, "acc_stderr": 0.017974463578776502, "acc_norm": 0.22752293577981653, "acc_norm_stderr": 0.017974463578776502 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.22058823529411764, "acc_stderr": 0.02910225438967409, "acc_norm": 0.22058823529411764, "acc_norm_stderr": 0.02910225438967409 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.26582278481012656, "acc_stderr": 0.028756799629658342, "acc_norm": 0.26582278481012656, "acc_norm_stderr": 0.028756799629658342 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.27802690582959644, "acc_stderr": 0.030069584874494047, "acc_norm": 0.27802690582959644, "acc_norm_stderr": 0.030069584874494047 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.24427480916030533, "acc_stderr": 0.037683359597287434, "acc_norm": 0.24427480916030533, "acc_norm_stderr": 0.037683359597287434 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2727272727272727, "acc_stderr": 0.04065578140908705, "acc_norm": 0.2727272727272727, "acc_norm_stderr": 0.04065578140908705 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.18518518518518517, "acc_stderr": 0.03755265865037181, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.03755265865037181 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.3128834355828221, "acc_stderr": 0.036429145782924055, "acc_norm": 0.3128834355828221, "acc_norm_stderr": 0.036429145782924055 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.19642857142857142, "acc_stderr": 0.03770970049347018, "acc_norm": 0.19642857142857142, "acc_norm_stderr": 0.03770970049347018 }, "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": 0.2264957264957265, "acc_stderr": 0.027421007295392926, "acc_norm": 0.2264957264957265, "acc_norm_stderr": 0.027421007295392926 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2848020434227331, "acc_stderr": 0.01613917409652258, "acc_norm": 0.2848020434227331, "acc_norm_stderr": 0.01613917409652258 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.22832369942196531, "acc_stderr": 0.022598703804321635, "acc_norm": 0.22832369942196531, "acc_norm_stderr": 0.022598703804321635 }, "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": 0.28938906752411575, "acc_stderr": 0.025755865922632924, "acc_norm": 0.28938906752411575, "acc_norm_stderr": 0.025755865922632924 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2345679012345679, "acc_stderr": 0.023576881744005723, "acc_norm": 0.2345679012345679, "acc_norm_stderr": 0.023576881744005723 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2624113475177305, "acc_stderr": 0.026244920349843007, "acc_norm": 0.2624113475177305, "acc_norm_stderr": 0.026244920349843007 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.23728813559322035, "acc_stderr": 0.010865436690780264, "acc_norm": 0.23728813559322035, "acc_norm_stderr": 0.010865436690780264 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.4485294117647059, "acc_stderr": 0.030211479609121593, "acc_norm": 0.4485294117647059, "acc_norm_stderr": 0.030211479609121593 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2173202614379085, "acc_stderr": 0.016684820929148587, "acc_norm": 0.2173202614379085, "acc_norm_stderr": 0.016684820929148587 }, "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": 0.23265306122448978, "acc_stderr": 0.02704925791589618, "acc_norm": 0.23265306122448978, "acc_norm_stderr": 0.02704925791589618 }, "harness|hendrycksTest-sociology|5": { "acc": 0.23880597014925373, "acc_stderr": 0.030147775935409224, "acc_norm": 0.23880597014925373, "acc_norm_stderr": 0.030147775935409224 }, "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": 0.3313253012048193, "acc_stderr": 0.036643147772880864, "acc_norm": 0.3313253012048193, "acc_norm_stderr": 0.036643147772880864 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.21052631578947367, "acc_stderr": 0.0312678171466318, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.0312678171466318 }, "harness|truthfulqa:mc|0": { "mc1": 0.25703794369645044, "mc1_stderr": 0.015298077509485083, "mc2": 0.4727026528122458, "mc2_stderr": 0.015699277111857743 }, "harness|winogrande|5": { "acc": 0.505130228887135, "acc_stderr": 0.01405174596179052 }, "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]

Li-Tang/cn_text

--- license: apache-2.0 ---

open-llm-leaderboard/details_cognitivecomputations__dolphin-2.8-experiment26-7b

--- pretty_name: Evaluation run of cognitivecomputations/dolphin-2.8-experiment26-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [cognitivecomputations/dolphin-2.8-experiment26-7b](https://huggingface.co/cognitivecomputations/dolphin-2.8-experiment26-7b)\ \ 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_cognitivecomputations__dolphin-2.8-experiment26-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-05T00:47:48.033781](https://huggingface.co/datasets/open-llm-leaderboard/details_cognitivecomputations__dolphin-2.8-experiment26-7b/blob/main/results_2024-03-05T00-47-48.033781.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\": 0.6259859767682346,\n\ \ \"acc_stderr\": 0.032722228933055555,\n \"acc_norm\": 0.6269857052836199,\n\ \ \"acc_norm_stderr\": 0.03338852136792568,\n \"mc1\": 0.3806609547123623,\n\ \ \"mc1_stderr\": 0.01699762787190793,\n \"mc2\": 0.5510246746247679,\n\ \ \"mc2_stderr\": 0.015278599523000265\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6040955631399317,\n \"acc_stderr\": 0.014291228393536588,\n\ \ \"acc_norm\": 0.636518771331058,\n \"acc_norm_stderr\": 0.014056207319068285\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6484763991236805,\n\ \ \"acc_stderr\": 0.004764703145680275,\n \"acc_norm\": 0.8369846644094802,\n\ \ \"acc_norm_stderr\": 0.003686247559361841\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5481481481481482,\n\ \ \"acc_stderr\": 0.04299268905480864,\n \"acc_norm\": 0.5481481481481482,\n\ \ \"acc_norm_stderr\": 0.04299268905480864\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.038947344870133176,\n\ \ \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.038947344870133176\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\ \ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \ \ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.02845015479411864,\n\ \ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.02845015479411864\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\ \ \"acc_stderr\": 0.03586879280080342,\n \"acc_norm\": 0.7569444444444444,\n\ \ \"acc_norm_stderr\": 0.03586879280080342\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.5,\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\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_medicine|5\"\ : {\n \"acc\": 0.6127167630057804,\n \"acc_stderr\": 0.03714325906302065,\n\ \ \"acc_norm\": 0.6127167630057804,\n \"acc_norm_stderr\": 0.03714325906302065\n\ \ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3235294117647059,\n\ \ \"acc_stderr\": 0.046550104113196177,\n \"acc_norm\": 0.3235294117647059,\n\ \ \"acc_norm_stderr\": 0.046550104113196177\n },\n \"harness|hendrycksTest-computer_security|5\"\ : {\n \"acc\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \ \ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\ \ \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5404255319148936,\n\ \ \"acc_stderr\": 0.03257901482099835,\n \"acc_norm\": 0.5404255319148936,\n\ \ \"acc_norm_stderr\": 0.03257901482099835\n },\n \"harness|hendrycksTest-econometrics|5\"\ : {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n\ \ \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n\ \ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\ : 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"\ acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3915343915343915,\n \"acc_stderr\": 0.025138091388851105,\n \"\ acc_norm\": 0.3915343915343915,\n \"acc_norm_stderr\": 0.025138091388851105\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04426266681379909,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04426266681379909\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\": 0.7516129032258064,\n\ \ \"acc_stderr\": 0.024580028921481006,\n \"acc_norm\": 0.7516129032258064,\n\ \ \"acc_norm_stderr\": 0.024580028921481006\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.49261083743842365,\n \"acc_stderr\": 0.03517603540361008,\n\ \ \"acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.03517603540361008\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\ \ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\ acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n\ \ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6333333333333333,\n \"acc_stderr\": 0.02443301646605246,\n \ \ \"acc_norm\": 0.6333333333333333,\n \"acc_norm_stderr\": 0.02443301646605246\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3296296296296296,\n \"acc_stderr\": 0.02866120111652458,\n \ \ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.02866120111652458\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\"\ : 0.33112582781456956,\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\"\ : 0.8330275229357799,\n \"acc_stderr\": 0.0159901548850734,\n \"acc_norm\"\ : 0.8330275229357799,\n \"acc_norm_stderr\": 0.0159901548850734\n },\n\ \ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5046296296296297,\n\ \ \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.5046296296296297,\n\ \ \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n\ \ \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601446,\n \ \ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601446\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n\ \ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\ \ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\ \ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\ acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\ \ \"acc_stderr\": 0.04133119440243838,\n \"acc_norm\": 0.7592592592592593,\n\ \ \"acc_norm_stderr\": 0.04133119440243838\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6871165644171779,\n \"acc_stderr\": 0.03642914578292406,\n\ \ \"acc_norm\": 0.6871165644171779,\n \"acc_norm_stderr\": 0.03642914578292406\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.044532548363264673,\n\ \ \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.044532548363264673\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\ \ \"acc_stderr\": 0.022509033937077805,\n \"acc_norm\": 0.8632478632478633,\n\ \ \"acc_norm_stderr\": 0.022509033937077805\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7956577266922095,\n\ \ \"acc_stderr\": 0.014419123980931895,\n \"acc_norm\": 0.7956577266922095,\n\ \ \"acc_norm_stderr\": 0.014419123980931895\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.024818350129436593,\n\ \ \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.024818350129436593\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3027932960893855,\n\ \ \"acc_stderr\": 0.015366860386397112,\n \"acc_norm\": 0.3027932960893855,\n\ \ \"acc_norm_stderr\": 0.015366860386397112\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6928104575163399,\n \"acc_stderr\": 0.026415601914388995,\n\ \ \"acc_norm\": 0.6928104575163399,\n \"acc_norm_stderr\": 0.026415601914388995\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\ \ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\ \ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6975308641975309,\n \"acc_stderr\": 0.025557653981868052,\n\ \ \"acc_norm\": 0.6975308641975309,\n \"acc_norm_stderr\": 0.025557653981868052\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.46099290780141844,\n \"acc_stderr\": 0.02973659252642444,\n \ \ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.02973659252642444\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4556714471968709,\n\ \ \"acc_stderr\": 0.012719949543032207,\n \"acc_norm\": 0.4556714471968709,\n\ \ \"acc_norm_stderr\": 0.012719949543032207\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6213235294117647,\n \"acc_stderr\": 0.02946513363977613,\n\ \ \"acc_norm\": 0.6213235294117647,\n \"acc_norm_stderr\": 0.02946513363977613\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6470588235294118,\n \"acc_stderr\": 0.019333142020797157,\n \ \ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.019333142020797157\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\": 0.6979591836734694,\n \"acc_stderr\": 0.0293936093198798,\n\ \ \"acc_norm\": 0.6979591836734694,\n \"acc_norm_stderr\": 0.0293936093198798\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\ \ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\ \ \"acc_norm_stderr\": 0.027113286753111837\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197768,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197768\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368032,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368032\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3806609547123623,\n\ \ \"mc1_stderr\": 0.01699762787190793,\n \"mc2\": 0.5510246746247679,\n\ \ \"mc2_stderr\": 0.015278599523000265\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7876874506708761,\n \"acc_stderr\": 0.011493384687249787\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6262319939347991,\n \ \ \"acc_stderr\": 0.013326342860737018\n }\n}\n```" repo_url: https://huggingface.co/cognitivecomputations/dolphin-2.8-experiment26-7b 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_02T20_37_13.780824 path: - '**/details_harness|arc:challenge|25_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|arc:challenge|25_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-05T00-47-48.033781.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|gsm8k|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|gsm8k|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hellaswag|10_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hellaswag|10_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-02T20-37-13.780824.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-05T00-47-48.033781.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-management|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-management|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-05T00-47-48.033781.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|truthfulqa:mc|0_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|truthfulqa:mc|0_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-05T00-47-48.033781.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_02T20_37_13.780824 path: - '**/details_harness|winogrande|5_2024-03-02T20-37-13.780824.parquet' - split: 2024_03_05T00_47_48.033781 path: - '**/details_harness|winogrande|5_2024-03-05T00-47-48.033781.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-05T00-47-48.033781.parquet' - config_name: results data_files: - split: 2024_03_02T20_37_13.780824 path: - results_2024-03-02T20-37-13.780824.parquet - split: 2024_03_05T00_47_48.033781 path: - results_2024-03-05T00-47-48.033781.parquet - split: latest path: - results_2024-03-05T00-47-48.033781.parquet --- # Dataset Card for Evaluation run of cognitivecomputations/dolphin-2.8-experiment26-7b  Dataset automatically created during the evaluation run of model [cognitivecomputations/dolphin-2.8-experiment26-7b](https://huggingface.co/cognitivecomputations/dolphin-2.8-experiment26-7b) 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_cognitivecomputations__dolphin-2.8-experiment26-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-05T00:47:48.033781](https://huggingface.co/datasets/open-llm-leaderboard/details_cognitivecomputations__dolphin-2.8-experiment26-7b/blob/main/results_2024-03-05T00-47-48.033781.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": 0.6259859767682346, "acc_stderr": 0.032722228933055555, "acc_norm": 0.6269857052836199, "acc_norm_stderr": 0.03338852136792568, "mc1": 0.3806609547123623, "mc1_stderr": 0.01699762787190793, "mc2": 0.5510246746247679, "mc2_stderr": 0.015278599523000265 }, "harness|arc:challenge|25": { "acc": 0.6040955631399317, "acc_stderr": 0.014291228393536588, "acc_norm": 0.636518771331058, "acc_norm_stderr": 0.014056207319068285 }, "harness|hellaswag|10": { "acc": 0.6484763991236805, "acc_stderr": 0.004764703145680275, "acc_norm": 0.8369846644094802, "acc_norm_stderr": 0.003686247559361841 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5481481481481482, "acc_stderr": 0.04299268905480864, "acc_norm": 0.5481481481481482, "acc_norm_stderr": 0.04299268905480864 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6447368421052632, "acc_stderr": 0.038947344870133176, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.038947344870133176 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.690566037735849, "acc_stderr": 0.02845015479411864, "acc_norm": 0.690566037735849, "acc_norm_stderr": 0.02845015479411864 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7569444444444444, "acc_stderr": 0.03586879280080342, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.03586879280080342 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3235294117647059, "acc_stderr": 0.046550104113196177, "acc_norm": 0.3235294117647059, "acc_norm_stderr": 0.046550104113196177 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.73, "acc_stderr": 0.0446196043338474, "acc_norm": 0.73, "acc_norm_stderr": 0.0446196043338474 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5404255319148936, "acc_stderr": 0.03257901482099835, "acc_norm": 0.5404255319148936, "acc_norm_stderr": 0.03257901482099835 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.47368421052631576, "acc_stderr": 0.046970851366478626, "acc_norm": 0.47368421052631576, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3915343915343915, "acc_stderr": 0.025138091388851105, "acc_norm": 0.3915343915343915, "acc_norm_stderr": 0.025138091388851105 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04426266681379909, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04426266681379909 }, "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": 0.7516129032258064, "acc_stderr": 0.024580028921481006, "acc_norm": 0.7516129032258064, "acc_norm_stderr": 0.024580028921481006 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.49261083743842365, "acc_stderr": 0.03517603540361008, "acc_norm": 0.49261083743842365, "acc_norm_stderr": 0.03517603540361008 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7757575757575758, "acc_stderr": 0.03256866661681102, "acc_norm": 0.7757575757575758, "acc_norm_stderr": 0.03256866661681102 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8080808080808081, "acc_stderr": 0.028057791672989017, "acc_norm": 0.8080808080808081, "acc_norm_stderr": 0.028057791672989017 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9015544041450777, "acc_stderr": 0.021500249576033456, "acc_norm": 0.9015544041450777, "acc_norm_stderr": 0.021500249576033456 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6333333333333333, "acc_stderr": 0.02443301646605246, "acc_norm": 0.6333333333333333, "acc_norm_stderr": 0.02443301646605246 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3296296296296296, "acc_stderr": 0.02866120111652458, "acc_norm": 0.3296296296296296, "acc_norm_stderr": 0.02866120111652458 }, "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": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8330275229357799, "acc_stderr": 0.0159901548850734, "acc_norm": 0.8330275229357799, "acc_norm_stderr": 0.0159901548850734 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7990196078431373, "acc_stderr": 0.028125972265654373, "acc_norm": 0.7990196078431373, "acc_norm_stderr": 0.028125972265654373 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7974683544303798, "acc_stderr": 0.026160568246601446, "acc_norm": 0.7974683544303798, "acc_norm_stderr": 0.026160568246601446 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6547085201793722, "acc_stderr": 0.03191100192835794, "acc_norm": 0.6547085201793722, "acc_norm_stderr": 0.03191100192835794 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7603305785123967, "acc_stderr": 0.03896878985070416, "acc_norm": 0.7603305785123967, "acc_norm_stderr": 0.03896878985070416 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7592592592592593, "acc_stderr": 0.04133119440243838, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243838 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6871165644171779, "acc_stderr": 0.03642914578292406, "acc_norm": 0.6871165644171779, "acc_norm_stderr": 0.03642914578292406 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.7184466019417476, "acc_stderr": 0.044532548363264673, "acc_norm": 0.7184466019417476, "acc_norm_stderr": 0.044532548363264673 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8632478632478633, "acc_stderr": 0.022509033937077805, "acc_norm": 0.8632478632478633, "acc_norm_stderr": 0.022509033937077805 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7956577266922095, "acc_stderr": 0.014419123980931895, "acc_norm": 0.7956577266922095, "acc_norm_stderr": 0.014419123980931895 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6936416184971098, "acc_stderr": 0.024818350129436593, "acc_norm": 0.6936416184971098, "acc_norm_stderr": 0.024818350129436593 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3027932960893855, "acc_stderr": 0.015366860386397112, "acc_norm": 0.3027932960893855, "acc_norm_stderr": 0.015366860386397112 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6928104575163399, "acc_stderr": 0.026415601914388995, "acc_norm": 0.6928104575163399, "acc_norm_stderr": 0.026415601914388995 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7106109324758842, "acc_stderr": 0.025755865922632945, "acc_norm": 0.7106109324758842, "acc_norm_stderr": 0.025755865922632945 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6975308641975309, "acc_stderr": 0.025557653981868052, "acc_norm": 0.6975308641975309, "acc_norm_stderr": 0.025557653981868052 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46099290780141844, "acc_stderr": 0.02973659252642444, "acc_norm": 0.46099290780141844, "acc_norm_stderr": 0.02973659252642444 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4556714471968709, "acc_stderr": 0.012719949543032207, "acc_norm": 0.4556714471968709, "acc_norm_stderr": 0.012719949543032207 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6213235294117647, "acc_stderr": 0.02946513363977613, "acc_norm": 0.6213235294117647, "acc_norm_stderr": 0.02946513363977613 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6470588235294118, "acc_stderr": 0.019333142020797157, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.019333142020797157 }, "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": 0.6979591836734694, "acc_stderr": 0.0293936093198798, "acc_norm": 0.6979591836734694, "acc_norm_stderr": 0.0293936093198798 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8208955223880597, "acc_stderr": 0.027113286753111837, "acc_norm": 0.8208955223880597, "acc_norm_stderr": 0.027113286753111837 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.03487350880197768, "acc_norm": 0.86, "acc_norm_stderr": 0.03487350880197768 }, "harness|hendrycksTest-virology|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8128654970760234, "acc_stderr": 0.029913127232368032, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.029913127232368032 }, "harness|truthfulqa:mc|0": { "mc1": 0.3806609547123623, "mc1_stderr": 0.01699762787190793, "mc2": 0.5510246746247679, "mc2_stderr": 0.015278599523000265 }, "harness|winogrande|5": { "acc": 0.7876874506708761, "acc_stderr": 0.011493384687249787 }, "harness|gsm8k|5": { "acc": 0.6262319939347991, "acc_stderr": 0.013326342860737018 } } ``` ## 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]

CyberHarem/hazuki_ren_lovelivesuperstar

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of hazuki_ren/葉月恋 (Love Live! Superstar!!) This is the dataset of hazuki_ren/葉月恋 (Love Live! Superstar!!), containing 474 images and their tags. The core tags of this character are `black_hair, long_hair, yellow_eyes, bangs, ponytail, high_ponytail, bow, breasts, ribbon, hair_bow, shiny_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 474 | 638.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hazuki_ren_lovelivesuperstar/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 474 | 331.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hazuki_ren_lovelivesuperstar/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1112 | 718.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hazuki_ren_lovelivesuperstar/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 474 | 546.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hazuki_ren_lovelivesuperstar/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1112 | 1.08 GiB | [Download](https://huggingface.co/datasets/CyberHarem/hazuki_ren_lovelivesuperstar/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/hazuki_ren_lovelivesuperstar', 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, blue_jacket, grey_dress, long_sleeves, looking_at_viewer, neck_ribbon, open_jacket, red_ribbon, smile, solo, yuigaoka_school_uniform, birthday, blush, pinafore_dress, medium_breasts, upper_body | | 1 | 14 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blue_jacket, grey_dress, looking_at_viewer, neck_ribbon, open_jacket, pinafore_dress, red_ribbon, solo, yuigaoka_school_uniform, blush, collared_shirt, long_sleeves, smile, simple_background, white_background, closed_mouth, cowboy_shot, white_shirt | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blue_jacket, brown_footwear, closed_mouth, full_body, grey_dress, loafers, long_sleeves, looking_at_viewer, neck_ribbon, open_jacket, pinafore_dress, red_ribbon, smile, solo, standing, white_background, white_shirt, white_socks, yuigaoka_school_uniform, collared_shirt, simple_background, white_bow, arms_behind_back, blush, kneehighs, leaning_forward | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, birthday, looking_at_viewer, smile, solo, upper_body, blush, shiny | | 4 | 10 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, birthday, looking_at_viewer, smile, solo, white_gloves, blush, medium_breasts, shiny, upper_body, sleeveless, white_dress, bubble, signature | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | red_bowtie, school_uniform, 1girl, collared_shirt, solo, upper_body, blush, closed_mouth, looking_at_viewer, short_sleeves, medium_breasts, skirt, white_shirt | | 6 | 6 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, open_jacket, solo, full_body, looking_at_viewer, thigh_strap, white_footwear, white_jacket, dress, frills, skirt, detached_collar, simple_background, smile, white_background | | 7 | 7 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, blush, cleavage, collarbone, looking_at_viewer, navel, solo, medium_breasts, white_background, white_bikini, cowboy_shot, parted_lips, simple_background, smile, stomach, bare_shoulders, blue_bikini, halterneck, large_breasts | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_jacket | grey_dress | long_sleeves | looking_at_viewer | neck_ribbon | open_jacket | red_ribbon | smile | solo | yuigaoka_school_uniform | birthday | blush | pinafore_dress | medium_breasts | upper_body | collared_shirt | simple_background | white_background | closed_mouth | cowboy_shot | white_shirt | brown_footwear | full_body | loafers | standing | white_socks | white_bow | arms_behind_back | kneehighs | leaning_forward | shiny | white_gloves | sleeveless | white_dress | bubble | signature | red_bowtie | school_uniform | short_sleeves | skirt | thigh_strap | white_footwear | white_jacket | dress | frills | detached_collar | cleavage | collarbone | navel | white_bikini | parted_lips | stomach | bare_shoulders | blue_bikini | halterneck | large_breasts | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:-------------|:---------------|:--------------------|:--------------|:--------------|:-------------|:--------|:-------|:--------------------------|:-----------|:--------|:-----------------|:-----------------|:-------------|:-----------------|:--------------------|:-------------------|:---------------|:--------------|:--------------|:-----------------|:------------|:----------|:-----------|:--------------|:------------|:-------------------|:------------|:------------------|:--------|:---------------|:-------------|:--------------|:---------|:------------|:-------------|:-----------------|:----------------|:--------|:--------------|:-----------------|:---------------|:--------|:---------|:------------------|:-----------|:-------------|:--------|:---------------|:--------------|:----------|:-----------------|:--------------|:-------------|:----------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 14 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | | X | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | | X | X | | | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | | X | | | | X | X | | X | X | | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 10 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | | | X | | | | X | X | | X | X | | X | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | | X | | | | | X | | | X | | X | X | X | | | X | | X | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | 6 | 6 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | | X | | X | | X | X | | | | | | | | X | X | | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | 7 | 7 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | | | X | | | | X | X | | | X | | X | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |

thisisHJLee/vi_data_made3

--- license: apache-2.0 ---

CVasNLPExperiments/textvqa_mini_validation_google_flan_t5_xxl_mode_OCR_VQA_Q_rices_ns_10

--- dataset_info: features: - name: id dtype: int64 - name: prompt sequence: string - name: question dtype: string - name: true_label sequence: string - name: prediction dtype: string splits: - name: fewshot_0 num_bytes: 15008 num_examples: 10 download_size: 7112 dataset_size: 15008 configs: - config_name: default data_files: - split: fewshot_0 path: data/fewshot_0-* ---

gu37/In-Shop-Clothes-Segmentation

--- license: mit ---

open-llm-leaderboard/details_gagan3012__MetaModel

--- pretty_name: Evaluation run of gagan3012/MetaModel dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [gagan3012/MetaModel](https://huggingface.co/gagan3012/MetaModel) 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_gagan3012__MetaModel\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-04T14:09:43.780941](https://huggingface.co/datasets/open-llm-leaderboard/details_gagan3012__MetaModel/blob/main/results_2024-01-04T14-09-43.780941.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\": 0.6664380298886512,\n\ \ \"acc_stderr\": 0.031642195230944255,\n \"acc_norm\": 0.6671639222858992,\n\ \ \"acc_norm_stderr\": 0.03228745343467652,\n \"mc1\": 0.5691554467564259,\n\ \ \"mc1_stderr\": 0.01733527247533237,\n \"mc2\": 0.7184177934834866,\n\ \ \"mc2_stderr\": 0.014995634120330182\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6843003412969283,\n \"acc_stderr\": 0.013582571095815291,\n\ \ \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.01325001257939344\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7132045409281019,\n\ \ \"acc_stderr\": 0.004513409114983828,\n \"acc_norm\": 0.8844851623182632,\n\ \ \"acc_norm_stderr\": 0.0031898897894046684\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.743421052631579,\n \"acc_stderr\": 0.0355418036802569,\n\ \ \"acc_norm\": 0.743421052631579,\n \"acc_norm_stderr\": 0.0355418036802569\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\ \ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\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\": 0.48,\n \"acc_stderr\": 0.05021167315686781,\n \"acc_norm\"\ : 0.48,\n \"acc_norm_stderr\": 0.05021167315686781\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\ \ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\ \ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.625531914893617,\n \"acc_stderr\": 0.03163910665367291,\n\ \ \"acc_norm\": 0.625531914893617,\n \"acc_norm_stderr\": 0.03163910665367291\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.039966295748767186,\n\ \ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.039966295748767186\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.025751310131230234,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.025751310131230234\n },\n \"harness|hendrycksTest-formal_logic|5\"\ : {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.0442626668137991,\n\ \ \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.0442626668137991\n\ \ },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.35,\n\ \ \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \ \ \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_biology|5\"\ : {\n \"acc\": 0.8129032258064516,\n \"acc_stderr\": 0.022185710092252252,\n\ \ \"acc_norm\": 0.8129032258064516,\n \"acc_norm_stderr\": 0.022185710092252252\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"\ acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\ \ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"\ acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644244,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644244\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.02385479568097114,\n \ \ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.02385479568097114\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \ \ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.029344572500634332,\n\ \ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.029344572500634332\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009246,\n \"\ acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009246\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5740740740740741,\n \"acc_stderr\": 0.03372343271653062,\n \"\ acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.03372343271653062\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.02450980392156862,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.02450980392156862\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8565400843881856,\n \"acc_stderr\": 0.022818291821017012,\n \ \ \"acc_norm\": 0.8565400843881856,\n \"acc_norm_stderr\": 0.022818291821017012\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\ \ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\ \ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728743,\n\ \ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728743\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\ acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.035865947385739734,\n\ \ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.035865947385739734\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n\ \ \"acc_stderr\": 0.02280138253459753,\n \"acc_norm\": 0.8589743589743589,\n\ \ \"acc_norm_stderr\": 0.02280138253459753\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\": 0.8084291187739464,\n\ \ \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n\ \ \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7572254335260116,\n \"acc_stderr\": 0.023083658586984204,\n\ \ \"acc_norm\": 0.7572254335260116,\n \"acc_norm_stderr\": 0.023083658586984204\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39664804469273746,\n\ \ \"acc_stderr\": 0.016361354769822468,\n \"acc_norm\": 0.39664804469273746,\n\ \ \"acc_norm_stderr\": 0.016361354769822468\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\ \ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\ \ \"acc_stderr\": 0.025494259350694905,\n \"acc_norm\": 0.7202572347266881,\n\ \ \"acc_norm_stderr\": 0.025494259350694905\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.02313237623454333,\n\ \ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02313237623454333\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5035460992907801,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.5035460992907801,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.49478487614080835,\n\ \ \"acc_stderr\": 0.012769541449652547,\n \"acc_norm\": 0.49478487614080835,\n\ \ \"acc_norm_stderr\": 0.012769541449652547\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.026303648393696036,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.026303648393696036\n \ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\"\ : 0.6813725490196079,\n \"acc_stderr\": 0.018850084696468712,\n \"\ acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.018850084696468712\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\ \ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\ \ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399677,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399677\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\ \ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\ \ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \ \ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5783132530120482,\n\ \ \"acc_stderr\": 0.038444531817709175,\n \"acc_norm\": 0.5783132530120482,\n\ \ \"acc_norm_stderr\": 0.038444531817709175\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03188578017686398,\n\ \ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03188578017686398\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5691554467564259,\n\ \ \"mc1_stderr\": 0.01733527247533237,\n \"mc2\": 0.7184177934834866,\n\ \ \"mc2_stderr\": 0.014995634120330182\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8342541436464088,\n \"acc_stderr\": 0.010450899545370632\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6535253980288097,\n \ \ \"acc_stderr\": 0.013107179054313398\n }\n}\n```" repo_url: https://huggingface.co/gagan3012/MetaModel 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_04T14_09_43.780941 path: - '**/details_harness|arc:challenge|25_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-04T14-09-43.780941.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|gsm8k|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hellaswag|10_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-04T14-09-43.780941.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-management|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T14-09-43.780941.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|truthfulqa:mc|0_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-04T14-09-43.780941.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_04T14_09_43.780941 path: - '**/details_harness|winogrande|5_2024-01-04T14-09-43.780941.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-04T14-09-43.780941.parquet' - config_name: results data_files: - split: 2024_01_04T14_09_43.780941 path: - results_2024-01-04T14-09-43.780941.parquet - split: latest path: - results_2024-01-04T14-09-43.780941.parquet --- # Dataset Card for Evaluation run of gagan3012/MetaModel  Dataset automatically created during the evaluation run of model [gagan3012/MetaModel](https://huggingface.co/gagan3012/MetaModel) 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_gagan3012__MetaModel", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-04T14:09:43.780941](https://huggingface.co/datasets/open-llm-leaderboard/details_gagan3012__MetaModel/blob/main/results_2024-01-04T14-09-43.780941.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": 0.6664380298886512, "acc_stderr": 0.031642195230944255, "acc_norm": 0.6671639222858992, "acc_norm_stderr": 0.03228745343467652, "mc1": 0.5691554467564259, "mc1_stderr": 0.01733527247533237, "mc2": 0.7184177934834866, "mc2_stderr": 0.014995634120330182 }, "harness|arc:challenge|25": { "acc": 0.6843003412969283, "acc_stderr": 0.013582571095815291, "acc_norm": 0.7107508532423208, "acc_norm_stderr": 0.01325001257939344 }, "harness|hellaswag|10": { "acc": 0.7132045409281019, "acc_stderr": 0.004513409114983828, "acc_norm": 0.8844851623182632, "acc_norm_stderr": 0.0031898897894046684 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.743421052631579, "acc_stderr": 0.0355418036802569, "acc_norm": 0.743421052631579, "acc_norm_stderr": 0.0355418036802569 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "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": 0.48, "acc_stderr": 0.05021167315686781, "acc_norm": 0.48, "acc_norm_stderr": 0.05021167315686781 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.625531914893617, "acc_stderr": 0.03163910665367291, "acc_norm": 0.625531914893617, "acc_norm_stderr": 0.03163910665367291 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6413793103448275, "acc_stderr": 0.039966295748767186, "acc_norm": 0.6413793103448275, "acc_norm_stderr": 0.039966295748767186 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.5, "acc_stderr": 0.025751310131230234, "acc_norm": 0.5, "acc_norm_stderr": 0.025751310131230234 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42857142857142855, "acc_stderr": 0.0442626668137991, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.0442626668137991 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8129032258064516, "acc_stderr": 0.022185710092252252, "acc_norm": 0.8129032258064516, "acc_norm_stderr": 0.022185710092252252 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8121212121212121, "acc_stderr": 0.03050193405942914, "acc_norm": 0.8121212121212121, "acc_norm_stderr": 0.03050193405942914 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8636363636363636, "acc_stderr": 0.024450155973189835, "acc_norm": 0.8636363636363636, "acc_norm_stderr": 0.024450155973189835 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8963730569948186, "acc_stderr": 0.021995311963644244, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.021995311963644244 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6692307692307692, "acc_stderr": 0.02385479568097114, "acc_norm": 0.6692307692307692, "acc_norm_stderr": 0.02385479568097114 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.37037037037037035, "acc_stderr": 0.02944316932303154, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.02944316932303154 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7142857142857143, "acc_stderr": 0.029344572500634332, "acc_norm": 0.7142857142857143, "acc_norm_stderr": 0.029344572500634332 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8422018348623853, "acc_stderr": 0.01563002297009246, "acc_norm": 0.8422018348623853, "acc_norm_stderr": 0.01563002297009246 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5740740740740741, "acc_stderr": 0.03372343271653062, "acc_norm": 0.5740740740740741, "acc_norm_stderr": 0.03372343271653062 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8578431372549019, "acc_stderr": 0.02450980392156862, "acc_norm": 0.8578431372549019, "acc_norm_stderr": 0.02450980392156862 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8565400843881856, "acc_stderr": 0.022818291821017012, "acc_norm": 0.8565400843881856, "acc_norm_stderr": 0.022818291821017012 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.672645739910314, "acc_stderr": 0.03149384670994131, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.03149384670994131 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7557251908396947, "acc_stderr": 0.03768335959728743, "acc_norm": 0.7557251908396947, "acc_norm_stderr": 0.03768335959728743 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.8446601941747572, "acc_stderr": 0.035865947385739734, "acc_norm": 0.8446601941747572, "acc_norm_stderr": 0.035865947385739734 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8589743589743589, "acc_stderr": 0.02280138253459753, "acc_norm": 0.8589743589743589, "acc_norm_stderr": 0.02280138253459753 }, "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": 0.8084291187739464, "acc_stderr": 0.014072859310451949, "acc_norm": 0.8084291187739464, "acc_norm_stderr": 0.014072859310451949 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7572254335260116, "acc_stderr": 0.023083658586984204, "acc_norm": 0.7572254335260116, "acc_norm_stderr": 0.023083658586984204 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.39664804469273746, "acc_stderr": 0.016361354769822468, "acc_norm": 0.39664804469273746, "acc_norm_stderr": 0.016361354769822468 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7581699346405228, "acc_stderr": 0.024518195641879334, "acc_norm": 0.7581699346405228, "acc_norm_stderr": 0.024518195641879334 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7202572347266881, "acc_stderr": 0.025494259350694905, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.025494259350694905 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02313237623454333, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02313237623454333 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5035460992907801, "acc_stderr": 0.02982674915328092, "acc_norm": 0.5035460992907801, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.49478487614080835, "acc_stderr": 0.012769541449652547, "acc_norm": 0.49478487614080835, "acc_norm_stderr": 0.012769541449652547 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.75, "acc_stderr": 0.026303648393696036, "acc_norm": 0.75, "acc_norm_stderr": 0.026303648393696036 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6813725490196079, "acc_stderr": 0.018850084696468712, "acc_norm": 0.6813725490196079, "acc_norm_stderr": 0.018850084696468712 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6818181818181818, "acc_stderr": 0.04461272175910509, "acc_norm": 0.6818181818181818, "acc_norm_stderr": 0.04461272175910509 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.746938775510204, "acc_stderr": 0.027833023871399677, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.027833023871399677 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8258706467661692, "acc_stderr": 0.026814951200421603, "acc_norm": 0.8258706467661692, "acc_norm_stderr": 0.026814951200421603 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.91, "acc_stderr": 0.028762349126466125, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466125 }, "harness|hendrycksTest-virology|5": { "acc": 0.5783132530120482, "acc_stderr": 0.038444531817709175, "acc_norm": 0.5783132530120482, "acc_norm_stderr": 0.038444531817709175 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7777777777777778, "acc_stderr": 0.03188578017686398, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.03188578017686398 }, "harness|truthfulqa:mc|0": { "mc1": 0.5691554467564259, "mc1_stderr": 0.01733527247533237, "mc2": 0.7184177934834866, "mc2_stderr": 0.014995634120330182 }, "harness|winogrande|5": { "acc": 0.8342541436464088, "acc_stderr": 0.010450899545370632 }, "harness|gsm8k|5": { "acc": 0.6535253980288097, "acc_stderr": 0.013107179054313398 } } ``` ## 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]

CyberHarem/yanfei_genshin

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of yanfei/煙緋/烟绯 (Genshin Impact) This is the dataset of yanfei/煙緋/烟绯 (Genshin Impact), containing 500 images and their tags. The core tags of this character are `pink_hair, long_hair, horns, green_eyes, hair_between_eyes, red_headwear, hat, breasts, small_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 1.06 GiB | [Download](https://huggingface.co/datasets/CyberHarem/yanfei_genshin/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 878.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yanfei_genshin/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1337 | 1.80 GiB | [Download](https://huggingface.co/datasets/CyberHarem/yanfei_genshin/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/yanfei_genshin', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, bare_shoulders, black_bra, cleavage, crop_top, detached_sleeves, long_sleeves, looking_at_viewer, midriff, solo, cowboy_shot, navel, stomach, antlers, red_skirt, white_background, :d, open_mouth, simple_background, yellow_bow, standing, very_long_hair, holding, book | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, bare_shoulders, black_bra, cleavage, crop_top, detached_sleeves, long_sleeves, looking_at_viewer, midriff, smile, solo, antlers, upper_body, white_background, navel, simple_background, stomach | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, bare_shoulders, bloomers, cleavage, crop_top, detached_sleeves, long_sleeves, midriff, navel, smile, solo, standing, stomach, antlers, black_bra, cowboy_shot, black_shorts, looking_at_viewer, red_skirt, fire, simple_background, thighs, yellow_bow | | 3 | 9 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, antlers, bare_shoulders, crop_top, detached_sleeves, long_sleeves, looking_at_viewer, midriff, solo, navel, open_mouth, stomach, cleavage, red_skirt, :d | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, antlers, bare_shoulders, crop_top, detached_sleeves, long_sleeves, looking_at_viewer, open_mouth, solo, boots, midriff, red_footwear, skirt, :d, holding, staff | | 5 | 6 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, antlers, bare_shoulders, detached_sleeves, looking_at_viewer, solo, upper_body, holding_book, long_sleeves, smile, closed_mouth | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | black_bra | cleavage | crop_top | detached_sleeves | long_sleeves | looking_at_viewer | midriff | solo | cowboy_shot | navel | stomach | antlers | red_skirt | white_background | :d | open_mouth | simple_background | yellow_bow | standing | very_long_hair | holding | book | smile | upper_body | bloomers | black_shorts | fire | thighs | boots | red_footwear | skirt | staff | holding_book | closed_mouth | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:------------|:-----------|:-----------|:-------------------|:---------------|:--------------------|:----------|:-------|:--------------|:--------|:----------|:----------|:------------|:-------------------|:-----|:-------------|:--------------------|:-------------|:-----------|:-----------------|:----------|:-------|:--------|:-------------|:-----------|:---------------|:-------|:---------|:--------|:---------------|:--------|:--------|:---------------|:---------------| | 0 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | | X | X | X | | X | | | X | | | | | | X | X | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | X | X | X | | | | X | | X | X | X | X | | | | | | | | 3 | 9 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | X | X | X | X | X | X | X | | X | X | X | X | | X | X | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | | | X | X | X | X | X | X | | | | X | | | X | X | | | | | X | | | | | | | | X | X | X | X | | | | 5 | 6 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | | | | X | X | X | | X | | | | X | | | | | | | | | | | X | X | | | | | | | | | X | X |

huggingartists/elton-john

--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/elton-john" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of the generated dataset:** 1.422945 MB <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/ec76d346c4c8b057169194c1781021fd.1000x1000x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/elton-john"> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div> </a> <div style="text-align: center; font-size: 16px; font-weight: 800">Elton John</div> <a href="https://genius.com/artists/elton-john"> <div style="text-align: center; font-size: 14px;">@elton-john</div> </a> </div> ### Dataset Summary The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists. Model is available [here](https://huggingface.co/huggingartists/elton-john). ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages en ## How to use How to load this dataset directly with the datasets library: ```python from datasets import load_dataset dataset = load_dataset("huggingartists/elton-john") ``` ## Dataset Structure An example of 'train' looks as follows. ``` This example was too long and was cropped: { "text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..." } ``` ### Data Fields The data fields are the same among all splits. - `text`: a `string` feature. ### Data Splits | train |validation|test| |------:|---------:|---:| |1311| -| -| 'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code: ```python from datasets import load_dataset, Dataset, DatasetDict import numpy as np datasets = load_dataset("huggingartists/elton-john") train_percentage = 0.9 validation_percentage = 0.07 test_percentage = 0.03 train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))]) datasets = DatasetDict( { 'train': Dataset.from_dict({'text': list(train)}), 'validation': Dataset.from_dict({'text': list(validation)}), 'test': Dataset.from_dict({'text': list(test)}) } ) ``` ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{huggingartists, author={Aleksey Korshuk} year=2021 } ``` ## About *Built by Aleksey Korshuk* [![Follow](https://img.shields.io/github/followers/AlekseyKorshuk?style=social)](https://github.com/AlekseyKorshuk) [![Follow](https://img.shields.io/twitter/follow/alekseykorshuk?style=social)](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [![Follow](https://img.shields.io/badge/dynamic/json?color=blue&label=Telegram%20Channel&query=%24.result&url=https%3A%2F%2Fapi.telegram.org%2Fbot1929545866%3AAAFGhV-KKnegEcLiyYJxsc4zV6C-bdPEBtQ%2FgetChatMemberCount%3Fchat_id%3D-1001253621662&style=social&logo=telegram)](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/AlekseyKorshuk/huggingartists?style=social)](https://github.com/AlekseyKorshuk/huggingartists)

ysharma/dummy123

--- license: mit ---

CVasNLPExperiments/OxfordPets_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_3669

--- dataset_info: features: - name: id dtype: int64 - name: prompt dtype: string - name: true_label dtype: string - name: prediction dtype: string splits: - name: fewshot_0_clip_tags_ViT_L_14_LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14_clip_tags_ViT_L_14_simple_specific_rices num_bytes: 1065643 num_examples: 3669 download_size: 193852 dataset_size: 1065643 --- # Dataset Card for "OxfordPets_test_google_flan_t5_xxl_mode_A_T_SPECIFIC_ns_3669" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

skytnt/japanese-lyric

--- license: cc0-1.0 task_categories: - text-generation language: - ja tags: - music pretty_name: Japanese Lyric size_categories: - 10K<n<100K ---

HuggingFaceM4/ScienceQAImg_Modif

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: image dtype: image - name: context dtype: string - name: label dtype: class_label: names: '0': A '1': B '2': C '3': D '4': E splits: - name: train num_bytes: 204229907.55288386 num_examples: 6218 - name: validation num_bytes: 68613530.46875736 num_examples: 2097 - name: test num_bytes: 65108877.472058475 num_examples: 2017 download_size: 661814327 dataset_size: 337952315.4936997 --- # Dataset Card for "ScienceQAImg_Modif" This dataset contains the [ScienceQA benchmark](https://arxiv.org/pdf/2209.09513.pdf) where only examples with an image are kept, and where we formatted the prompt.

csaybar/CloudSEN12-nolabel

--- license: cc-by-nc-4.0 --- # **CloudSEN12 NOLABEL** ## **A Benchmark Dataset for Cloud Semantic Understanding** ![CloudSEN12 Images](https://cloudsen12.github.io/thumbnails/cloudsen12.gif) CloudSEN12 is a LARGE dataset (~1 TB) for cloud semantic understanding that consists of 49,400 image patches (IP) that are evenly spread throughout all continents except Antarctica. Each IP covers 5090 x 5090 meters and contains data from Sentinel-2 levels 1C and 2A, hand-crafted annotations of thick and thin clouds and cloud shadows, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from six cutting-edge cloud detection algorithms. CloudSEN12 is designed to support both weakly and self-/semi-supervised learning strategies by including three distinct forms of hand-crafted labeling data: high-quality, scribble and no-annotation. For more details on how we created the dataset see our paper. Ready to start using **[CloudSEN12](https://cloudsen12.github.io/)**? **[Download Dataset](https://cloudsen12.github.io/download.html)** **[Paper - Scientific Data](https://www.nature.com/articles/s41597-022-01878-2)** **[Inference on a new S2 image](https://colab.research.google.com/github/cloudsen12/examples/blob/master/example02.ipynb)** **[Enter to cloudApp](https://github.com/cloudsen12/CloudApp)** **[CloudSEN12 in Google Earth Engine](https://gee-community-catalog.org/projects/cloudsen12/)** <br> ### **Description** <br> | File | Name | Scale | Wavelength | Description | Datatype | |---------------|-----------------|--------|------------------------------|------------------------------------------------------------------------------------------------------|----------| | L1C_ & L2A_ | B1 | 0.0001 | 443.9nm (S2A) / 442.3nm (S2B)| Aerosols. | np.int16 | | | B2 | 0.0001 | 496.6nm (S2A) / 492.1nm (S2B)| Blue. | np.int16 | | | B3 | 0.0001 | 560nm (S2A) / 559nm (S2B) | Green. | np.int16 | | | B4 | 0.0001 | 664.5nm (S2A) / 665nm (S2B) | Red. | np.int16 | | | B5 | 0.0001 | 703.9nm (S2A) / 703.8nm (S2B)| Red Edge 1. | np.int16 | | | B6 | 0.0001 | 740.2nm (S2A) / 739.1nm (S2B)| Red Edge 2. | np.int16 | | | B7 | 0.0001 | 782.5nm (S2A) / 779.7nm (S2B)| Red Edge 3. | np.int16 | | | B8 | 0.0001 | 835.1nm (S2A) / 833nm (S2B) | NIR. | np.int16 | | | B8A | 0.0001 | 864.8nm (S2A) / 864nm (S2B) | Red Edge 4. | np.int16 | | | B9 | 0.0001 | 945nm (S2A) / 943.2nm (S2B) | Water vapor. | np.int16 | | | B11 | 0.0001 | 1613.7nm (S2A) / 1610.4nm (S2B)| SWIR 1. | np.int16 | | | B12 | 0.0001 | 2202.4nm (S2A) / 2185.7nm (S2B)| SWIR 2. | np.int16 | | L1C_ | B10 | 0.0001 | 1373.5nm (S2A) / 1376.9nm (S2B)| Cirrus. | np.int16 | | L2A_ | AOT | 0.001 | - | Aerosol Optical Thickness. | np.int16 | | | WVP | 0.001 | - | Water Vapor Pressure. | np.int16 | | | TCI_R | 1 | - | True Color Image, Red. | np.int16 | | | TCI_G | 1 | - | True Color Image, Green. | np.int16 | | | TCI_B | 1 | - | True Color Image, Blue. | np.int16 | | S1_ | VV | 1 | 5.405GHz | Dual-band cross-polarization, vertical transmit/horizontal receive. |np.float32| | | VH | 1 | 5.405GHz | Single co-polarization, vertical transmit/vertical receive. |np.float32| | | angle | 1 | - | Incidence angle generated by interpolating the ‘incidenceAngle’ property. |np.float32| | EXTRA_ | CDI | 0.0001 | - | Cloud Displacement Index. | np.int16 | | | Shwdirection | 0.01 | - | Azimuth. Values range from 0°- 360°. | np.int16 | | | elevation | 1 | - | Elevation in meters. Obtained from MERIT Hydro datasets. | np.int16 | | | ocurrence | 1 | - | JRC Global Surface Water. The frequency with which water was present. | np.int16 | | | LC100 | 1 | - | Copernicus land cover product. CGLS-LC100 Collection 3. | np.int16 | | | LC10 | 1 | - | ESA WorldCover 10m v100 product. | np.int16 | | LABEL_ | fmask | 1 | - | Fmask4.0 cloud masking. | np.int16 | | | QA60 | 1 | - | SEN2 Level-1C cloud mask. | np.int8 | | | s2cloudless | 1 | - | sen2cloudless results. | np.int8 | | | sen2cor | 1 | - | Scene Classification band. Obtained from SEN2 level 2A. | np.int8 | | | cd_fcnn_rgbi | 1 | - | López-Puigdollers et al. results based on RGBI bands. | np.int8 | | |cd_fcnn_rgbi_swir| 1 | - | López-Puigdollers et al. results based on RGBISWIR bands. | np.int8 | | | kappamask_L1C | 1 | - | KappaMask results using SEN2 level L1C as input. | np.int8 | | | kappamask_L2A | 1 | - | KappaMask results using SEN2 level L2A as input. | np.int8 | | | manual_hq | 1 | | High-quality pixel-wise manual annotation. | np.int8 | | | manual_sc | 1 | | Scribble manual annotation. | np.int8 | <br> ### **Label Description** | **CloudSEN12** | **KappaMask** | **Sen2Cor** | **Fmask** | **s2cloudless** | **CD-FCNN** | **QA60** | |------------------|------------------|-------------------------|-----------------|-----------------------|---------------------|--------------------| | 0 Clear | 1 Clear | 4 Vegetation | 0 Clear land | 0 Clear | 0 Clear | 0 Clear | | | | 2 Dark area pixels | 1 Clear water | | | | | | | 5 Bare Soils | 3 Snow | | | | | | | 6 Water | | | | | | | | 11 Snow | | | | | | 1 Thick cloud | 4 Cloud | 8 Cloud medium probability | 4 Cloud | 1 Cloud | 1 Cloud | 1024 Opaque cloud | | | | 9 Cloud high probability | | | | | | 2 Thin cloud | 3 Semi-transparent cloud | 10 Thin cirrus | | | | 2048 Cirrus cloud | | 3 Cloud shadow | 2 Cloud shadow | 3 Cloud shadows | 2 Cloud shadow | | | | <br> ### **np.memmap shape information** <br> **cloudfree (0\%) shape: (5880, 512, 512)** <br> **almostclear (0-25 \%) shape: (5880, 512, 512)** <br> **lowcloudy (25-45 \%) shape: (5880, 512, 512)** <br> **midcloudy (45-65 \%) shape: (5880, 512, 512)** <br> **cloudy (65 > \%) shape: (5880, 512, 512)** <br> ### **Example** <br> ```py import numpy as np # Read high-quality train cloudfree_shape = (5880, 512, 512) B4X = np.memmap('cloudfree/L1C_B04.dat', dtype='int16', mode='r', shape=cloudfree_shape) y = np.memmap('cloudfree/manual_hq.dat', dtype='int8', mode='r', shape=cloudfree_shape) # Read high-quality val almostclear_shape = (5880, 512, 512) B4X = np.memmap('almostclear/L1C_B04.dat', dtype='int16', mode='r', shape=almostclear_shape) y = np.memmap('almostclear/kappamask_L1C.dat', dtype='int8', mode='r', shape=almostclear_shape) # Read high-quality test midcloudy_shape = (5880, 512, 512) B4X = np.memmap('midcloudy/L1C_B04.dat', dtype='int16', mode='r', shape=midcloudy_shape) y = np.memmap('midcloudy/kappamask_L1C.dat', dtype='int8', mode='r', shape=midcloudy_shape) ``` <br> This work has been partially supported by the Spanish Ministry of Science and Innovation project PID2019-109026RB-I00 (MINECO-ERDF) and the Austrian Space Applications Programme within the **[SemantiX project](https://austria-in-space.at/en/projects/2019/semantix.php)**.

communityai/communityai_apt-instruct-code-micro-70k

--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 291560981.6281767 num_examples: 70000 download_size: 129436280 dataset_size: 291560981.6281767 configs: - config_name: default data_files: - split: train path: data/train-* ---

yjernite/prof_report__dalle-2__sd_21__24

--- dataset_info: features: - name: cluster_id dtype: int64 - name: cluster_size dtype: int64 - name: img_ids sequence: int64 - name: img_cluster_scores sequence: float64 splits: - name: paralegal num_bytes: 3624 num_examples: 11 - name: bartender num_bytes: 3576 num_examples: 9 - name: facilities_manager num_bytes: 3576 num_examples: 9 - name: accountant num_bytes: 3480 num_examples: 5 - name: graphic_designer num_bytes: 3600 num_examples: 10 - name: network_administrator num_bytes: 3648 num_examples: 12 - name: financial_manager num_bytes: 3504 num_examples: 6 - name: baker num_bytes: 3600 num_examples: 10 - name: security_guard num_bytes: 3528 num_examples: 7 - name: artist num_bytes: 3696 num_examples: 14 - name: author num_bytes: 3648 num_examples: 12 - name: printing_press_operator num_bytes: 3504 num_examples: 6 - name: public_relations_specialist num_bytes: 3552 num_examples: 8 - name: sheet_metal_worker num_bytes: 3504 num_examples: 6 - name: clergy num_bytes: 3600 num_examples: 10 - name: payroll_clerk num_bytes: 3624 num_examples: 11 - name: teller num_bytes: 3600 num_examples: 10 - name: real_estate_broker num_bytes: 3480 num_examples: 5 - name: customer_service_representative num_bytes: 3600 num_examples: 10 - name: painter num_bytes: 3696 num_examples: 14 - name: tractor_operator num_bytes: 3552 num_examples: 8 - name: dental_hygienist num_bytes: 3528 num_examples: 7 - name: industrial_engineer num_bytes: 3576 num_examples: 9 - name: electrician num_bytes: 3576 num_examples: 9 - name: head_cook num_bytes: 3576 num_examples: 9 - name: health_technician num_bytes: 3576 num_examples: 9 - name: carpet_installer num_bytes: 3624 num_examples: 11 - name: purchasing_agent num_bytes: 3552 num_examples: 8 - name: supervisor num_bytes: 3552 num_examples: 8 - name: civil_engineer num_bytes: 3504 num_examples: 6 - name: lawyer num_bytes: 3576 num_examples: 9 - name: language_pathologist num_bytes: 3576 num_examples: 9 - name: ceo num_bytes: 3480 num_examples: 5 - name: computer_support_specialist num_bytes: 3480 num_examples: 5 - name: postal_worker num_bytes: 3576 num_examples: 9 - name: mechanical_engineer num_bytes: 3576 num_examples: 9 - name: nursing_assistant num_bytes: 3528 num_examples: 7 - name: dentist num_bytes: 3600 num_examples: 10 - name: tutor num_bytes: 3576 num_examples: 9 - name: butcher num_bytes: 3552 num_examples: 8 - name: insurance_agent num_bytes: 3552 num_examples: 8 - name: courier num_bytes: 3552 num_examples: 8 - name: computer_programmer num_bytes: 3528 num_examples: 7 - name: truck_driver num_bytes: 3480 num_examples: 5 - name: mechanic num_bytes: 3576 num_examples: 9 - name: marketing_manager num_bytes: 3528 num_examples: 7 - name: sales_manager num_bytes: 3480 num_examples: 5 - name: correctional_officer num_bytes: 3528 num_examples: 7 - name: manager num_bytes: 3504 num_examples: 6 - name: underwriter num_bytes: 3528 num_examples: 7 - name: executive_assistant num_bytes: 3528 num_examples: 7 - name: designer num_bytes: 3576 num_examples: 9 - name: groundskeeper num_bytes: 3624 num_examples: 11 - name: mental_health_counselor num_bytes: 3600 num_examples: 10 - name: aerospace_engineer num_bytes: 3552 num_examples: 8 - name: taxi_driver num_bytes: 3552 num_examples: 8 - name: nurse num_bytes: 3504 num_examples: 6 - name: data_entry_keyer num_bytes: 3624 num_examples: 11 - name: musician num_bytes: 3624 num_examples: 11 - name: event_planner num_bytes: 3696 num_examples: 14 - name: writer num_bytes: 3576 num_examples: 9 - name: cook num_bytes: 3648 num_examples: 12 - name: welder num_bytes: 3552 num_examples: 8 - name: producer num_bytes: 3648 num_examples: 12 - name: hairdresser num_bytes: 3672 num_examples: 13 - name: farmer num_bytes: 3528 num_examples: 7 - name: construction_worker num_bytes: 3576 num_examples: 9 - name: air_conditioning_installer num_bytes: 3504 num_examples: 6 - name: electrical_engineer num_bytes: 3504 num_examples: 6 - name: occupational_therapist num_bytes: 3552 num_examples: 8 - name: career_counselor num_bytes: 3528 num_examples: 7 - name: interior_designer num_bytes: 3648 num_examples: 12 - name: jailer num_bytes: 3528 num_examples: 7 - name: office_clerk num_bytes: 3504 num_examples: 6 - name: market_research_analyst num_bytes: 3576 num_examples: 9 - name: laboratory_technician num_bytes: 3576 num_examples: 9 - name: social_assistant num_bytes: 3552 num_examples: 8 - name: medical_records_specialist num_bytes: 3624 num_examples: 11 - name: machinery_mechanic num_bytes: 3504 num_examples: 6 - name: police_officer num_bytes: 3552 num_examples: 8 - name: software_developer num_bytes: 3480 num_examples: 5 - name: clerk num_bytes: 3480 num_examples: 5 - name: salesperson num_bytes: 3552 num_examples: 8 - name: social_worker num_bytes: 3672 num_examples: 13 - name: director num_bytes: 3504 num_examples: 6 - name: fast_food_worker num_bytes: 3648 num_examples: 12 - name: singer num_bytes: 3696 num_examples: 14 - name: metal_worker num_bytes: 3576 num_examples: 9 - name: cleaner num_bytes: 3648 num_examples: 12 - name: computer_systems_analyst num_bytes: 3600 num_examples: 10 - name: dental_assistant num_bytes: 3504 num_examples: 6 - name: psychologist num_bytes: 3576 num_examples: 9 - name: machinist num_bytes: 3504 num_examples: 6 - name: therapist num_bytes: 3504 num_examples: 6 - name: veterinarian num_bytes: 3528 num_examples: 7 - name: teacher num_bytes: 3576 num_examples: 9 - name: architect num_bytes: 3552 num_examples: 8 - name: office_worker num_bytes: 3552 num_examples: 8 - name: drywall_installer num_bytes: 3552 num_examples: 8 - name: nutritionist num_bytes: 3552 num_examples: 8 - name: librarian num_bytes: 3576 num_examples: 9 - name: childcare_worker num_bytes: 3576 num_examples: 9 - name: school_bus_driver num_bytes: 3504 num_examples: 6 - name: file_clerk num_bytes: 3504 num_examples: 6 - name: logistician num_bytes: 3528 num_examples: 7 - name: scientist num_bytes: 3528 num_examples: 7 - name: teaching_assistant num_bytes: 3576 num_examples: 9 - name: radiologic_technician num_bytes: 3504 num_examples: 6 - name: manicurist num_bytes: 3600 num_examples: 10 - name: community_manager num_bytes: 3528 num_examples: 7 - name: carpenter num_bytes: 3600 num_examples: 10 - name: claims_appraiser num_bytes: 3552 num_examples: 8 - name: dispatcher num_bytes: 3576 num_examples: 9 - name: cashier num_bytes: 3600 num_examples: 10 - name: roofer num_bytes: 3504 num_examples: 6 - name: photographer num_bytes: 3600 num_examples: 10 - name: detective num_bytes: 3576 num_examples: 9 - name: financial_advisor num_bytes: 3504 num_examples: 6 - name: wholesale_buyer num_bytes: 3576 num_examples: 9 - name: it_specialist num_bytes: 3456 num_examples: 4 - name: pharmacy_technician num_bytes: 3600 num_examples: 10 - name: engineer num_bytes: 3456 num_examples: 4 - name: mover num_bytes: 3696 num_examples: 14 - name: plane_mechanic num_bytes: 3504 num_examples: 6 - name: interviewer num_bytes: 3552 num_examples: 8 - name: massage_therapist num_bytes: 3624 num_examples: 11 - name: dishwasher num_bytes: 3624 num_examples: 11 - name: fitness_instructor num_bytes: 3576 num_examples: 9 - name: credit_counselor num_bytes: 3552 num_examples: 8 - name: stocker num_bytes: 3672 num_examples: 13 - name: pharmacist num_bytes: 3504 num_examples: 6 - name: doctor num_bytes: 3552 num_examples: 8 - name: compliance_officer num_bytes: 3552 num_examples: 8 - name: aide num_bytes: 3600 num_examples: 10 - name: bus_driver num_bytes: 3552 num_examples: 8 - name: financial_analyst num_bytes: 3504 num_examples: 6 - name: receptionist num_bytes: 3624 num_examples: 11 - name: janitor num_bytes: 3576 num_examples: 9 - name: plumber num_bytes: 3528 num_examples: 7 - name: physical_therapist num_bytes: 3528 num_examples: 7 - name: inventory_clerk num_bytes: 3576 num_examples: 9 - name: firefighter num_bytes: 3528 num_examples: 7 - name: coach num_bytes: 3528 num_examples: 7 - name: maid num_bytes: 3528 num_examples: 7 - name: pilot num_bytes: 3504 num_examples: 6 - name: repair_worker num_bytes: 3576 num_examples: 9 download_size: 867106 dataset_size: 520104 --- # Dataset Card for "prof_report__dalle-2__sd_21__24" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/hisaishi_kanade_soundeuphonium

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Hisaishi Kanade/久石奏 (Sound! Euphonium) This is the dataset of Hisaishi Kanade/久石奏 (Sound! Euphonium), containing 182 images and their tags. The core tags of this character are `short_hair, black_hair, bow, hair_bow, red_eyes, red_bow`, 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 | 182 | 122.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hisaishi_kanade_soundeuphonium/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 182 | 122.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hisaishi_kanade_soundeuphonium/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 359 | 220.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hisaishi_kanade_soundeuphonium/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/hisaishi_kanade_soundeuphonium', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 19 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blue_sailor_collar, blush, green_neckerchief, kitauji_high_school_uniform, serafuku, white_shirt, indoors, solo, closed_mouth, looking_at_viewer, short_sleeves, chalkboard, holding_instrument | | 1 | 8 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blue_sailor_collar, blue_skirt, blush, chair, classroom, closed_mouth, indoors, kitauji_high_school_uniform, serafuku, sitting, white_shirt, desk, holding_instrument, pleated_skirt, short_sleeves, brown_eyes, green_neckerchief, chalkboard, looking_at_viewer, solo_focus, smile | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blue_sailor_collar, blue_skirt, blurry, blush, chair, chalkboard, classroom, desk, green_neckerchief, indoors, instrument, kitauji_high_school_uniform, pleated_skirt, serafuku, short_sleeves, solo, standing, white_shirt, open_mouth, bag, looking_at_viewer, smile, sweatdrop | | 3 | 8 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blush, brown_shirt, closed_mouth, kitauji_high_school_uniform, serafuku, solo, white_sailor_collar, indoors, smile, green_neckerchief, blurry_background, long_sleeves, looking_at_viewer | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, blurry_background, blush, brown_shirt, brown_skirt, green_neckerchief, kitauji_high_school_uniform, long_sleeves, pleated_skirt, serafuku, smile, solo, standing, white_sailor_collar, closed_mouth, indoors | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, blue_sailor_collar, blue_skirt, blush, closed_mouth, green_neckerchief, hand_up, indoors, kitauji_high_school_uniform, pleated_skirt, school_bag, serafuku, short_sleeves, solo, standing, white_shirt, window, black_bag, brown_hair, looking_at_viewer, mole_under_eye, smile, blurry_background, brown_eyes, hallway | | 6 | 8 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, blue_sailor_collar, blurry_background, blush, kitauji_high_school_uniform, outdoors, rain, serafuku, solo, white_shirt, open_mouth, green_neckerchief, parted_lips, brown_eyes, building, wet_hair | | 7 | 9 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, solo, green_jacket, blush, closed_mouth, blurry_background, looking_at_viewer, holding_instrument, long_sleeves, outdoors, shirt, track_jacket | | 8 | 17 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, blush, yellow_headwear, solo, closed_mouth, holding_instrument, short_sleeves, band_uniform, orange_headwear, shirt, red_gloves, blurry_background, hat_feather, grey_background, looking_at_viewer | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_sailor_collar | blush | green_neckerchief | kitauji_high_school_uniform | serafuku | white_shirt | indoors | solo | closed_mouth | looking_at_viewer | short_sleeves | chalkboard | holding_instrument | blue_skirt | chair | classroom | sitting | desk | pleated_skirt | brown_eyes | solo_focus | smile | blurry | instrument | standing | open_mouth | bag | sweatdrop | brown_shirt | white_sailor_collar | blurry_background | long_sleeves | brown_skirt | hand_up | school_bag | window | black_bag | brown_hair | mole_under_eye | hallway | outdoors | rain | parted_lips | building | wet_hair | green_jacket | shirt | track_jacket | yellow_headwear | band_uniform | orange_headwear | red_gloves | hat_feather | grey_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------------|:--------|:--------------------|:------------------------------|:-----------|:--------------|:----------|:-------|:---------------|:--------------------|:----------------|:-------------|:---------------------|:-------------|:--------|:------------|:----------|:-------|:----------------|:-------------|:-------------|:--------|:---------|:-------------|:-----------|:-------------|:------|:------------|:--------------|:----------------------|:--------------------|:---------------|:--------------|:----------|:-------------|:---------|:------------|:-------------|:-----------------|:----------|:-----------|:-------|:--------------|:-----------|:-----------|:---------------|:--------|:---------------|:------------------|:---------------|:------------------|:-------------|:--------------|:------------------| | 0 | 19 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 8 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | X | X | X | | X | X | X | | X | X | X | | X | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 8 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | X | X | X | | X | X | X | X | | | | | | | | | | | | X | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | X | X | X | X | | X | X | X | | | | | | | | | | X | | | X | | | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | | | X | | | | | X | X | | X | | | X | | | | | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | 6 | 8 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | X | X | X | X | X | | X | | | | | | | | | | | | X | | | | | | X | | | | | X | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | 7 | 9 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | X | | | | | | X | X | X | | | X | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | X | | | | | X | X | X | | | | | | | | 8 | 17 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | X | | | | | | X | X | X | X | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | | X | X | X | X | X | X |

autoevaluate/autoeval-eval-phpthinh__examplei-all-929d48-1748861032

--- type: predictions tags: - autotrain - evaluation datasets: - phpthinh/examplei eval_info: task: text_zero_shot_classification model: bigscience/bloom-7b1 metrics: ['f1'] dataset_name: phpthinh/examplei dataset_config: all 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: bigscience/bloom-7b1 * Dataset: phpthinh/examplei * Config: all * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.

malteos/wikinews-tmp2

--- dataset_info: - config_name: de features: - name: language dtype: string - name: wiki_page_id dtype: string - name: wiki_revision_id dtype: string - name: revision_timestamp dtype: timestamp[us, tz=UTC] - name: revision_year dtype: uint16 - name: revision_month dtype: uint16 - name: article_timestamp dtype: timestamp[us, tz=UTC] - name: article_year dtype: uint16 - name: article_month dtype: uint16 - name: url dtype: string - name: title dtype: string - name: raw_text dtype: string - name: cleaned_text dtype: string - name: categories sequence: string - name: sources sequence: string - name: dump dtype: string splits: - name: 2004_q4_12 num_bytes: 1060779 num_examples: 251 - name: 2005_q1_01 num_bytes: 402111 num_examples: 99 - name: 2005_q1_02 num_bytes: 602415 num_examples: 162 - name: 2005_q1_03 num_bytes: 845392 num_examples: 195 - name: 2005_q3_08 num_bytes: 1392393 num_examples: 360 - name: 2005_q2_04 num_bytes: 754328 num_examples: 186 - name: 2005_q2_05 num_bytes: 750409 num_examples: 179 - name: 2005_q3_07 num_bytes: 1380652 num_examples: 334 - name: 2005_q2_06 num_bytes: 993773 num_examples: 257 - name: 2005_q4_10 num_bytes: 1716394 num_examples: 410 - name: 2005_q4_11 num_bytes: 934471 num_examples: 230 - name: 2007_q1_03 num_bytes: 901035 num_examples: 175 - name: 2005_q3_09 num_bytes: 1659850 num_examples: 392 - name: 2004_q3_08 num_bytes: 7316 num_examples: 2 - name: 2005_q4_12 num_bytes: 1086986 num_examples: 268 - name: 2006_q1_01 num_bytes: 1209718 num_examples: 279 - name: 2006_q1_02 num_bytes: 819639 num_examples: 194 - name: 2006_q1_03 num_bytes: 1074845 num_examples: 247 - name: 2006_q2_06 num_bytes: 1170821 num_examples: 263 - name: 2006_q2_04 num_bytes: 978701 num_examples: 221 - name: 2006_q2_05 num_bytes: 1136732 num_examples: 271 - name: 2006_q3_07 num_bytes: 1161245 num_examples: 249 - name: 2006_q3_08 num_bytes: 1275797 num_examples: 241 - name: 2006_q3_09 num_bytes: 873844 num_examples: 157 - name: 2006_q4_10 num_bytes: 913674 num_examples: 206 - name: 2006_q4_11 num_bytes: 986117 num_examples: 193 - 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split: 2005_q1_02 path: de/2005_q1_02-* - split: 2005_q1_03 path: de/2005_q1_03-* - split: 2005_q3_08 path: de/2005_q3_08-* - split: 2005_q2_04 path: de/2005_q2_04-* - split: 2005_q2_05 path: de/2005_q2_05-* - split: 2005_q3_07 path: de/2005_q3_07-* - split: 2005_q2_06 path: de/2005_q2_06-* - split: 2005_q4_10 path: de/2005_q4_10-* - split: 2005_q4_11 path: de/2005_q4_11-* - split: 2007_q1_03 path: de/2007_q1_03-* - split: 2005_q3_09 path: de/2005_q3_09-* - split: 2004_q3_08 path: de/2004_q3_08-* - split: 2005_q4_12 path: de/2005_q4_12-* - split: 2006_q1_01 path: de/2006_q1_01-* - split: 2006_q1_02 path: de/2006_q1_02-* - split: 2006_q1_03 path: de/2006_q1_03-* - split: 2006_q2_06 path: de/2006_q2_06-* - split: 2006_q2_04 path: de/2006_q2_04-* - split: 2006_q2_05 path: de/2006_q2_05-* - split: 2006_q3_07 path: de/2006_q3_07-* - split: 2006_q3_08 path: de/2006_q3_08-* - split: 2006_q3_09 path: de/2006_q3_09-* - split: 2006_q4_10 path: de/2006_q4_10-* - split: 2006_q4_11 path: de/2006_q4_11-* - split: 2006_q4_12 path: de/2006_q4_12-* - split: 2007_q1_02 path: de/2007_q1_02-* - split: 2007_q1_01 path: de/2007_q1_01-* - split: 2007_q2_06 path: de/2007_q2_06-* - split: 2007_q2_04 path: de/2007_q2_04-* - split: 2007_q2_05 path: de/2007_q2_05-* - split: 2007_q3_07 path: de/2007_q3_07-* - split: 2007_q3_08 path: de/2007_q3_08-* - split: 2007_q3_09 path: de/2007_q3_09-* - split: 2007_q4_10 path: de/2007_q4_10-* - split: 2007_q4_11 path: de/2007_q4_11-* - split: 2007_q4_12 path: de/2007_q4_12-* - split: 2008_q1_01 path: de/2008_q1_01-* - split: 2008_q1_02 path: de/2008_q1_02-* - split: 2008_q1_03 path: de/2008_q1_03-* - split: 2008_q2_04 path: de/2008_q2_04-* - split: 2008_q2_05 path: de/2008_q2_05-* - split: 2008_q2_06 path: de/2008_q2_06-* - split: 2008_q3_07 path: de/2008_q3_07-* - split: 2008_q3_08 path: de/2008_q3_08-* - split: 2008_q3_09 path: de/2008_q3_09-* - split: 2008_q4_10 path: de/2008_q4_10-* - split: 2008_q4_11 path: de/2008_q4_11-* - split: 2008_q4_12 path: de/2008_q4_12-* - split: 2009_q1_01 path: de/2009_q1_01-* - split: 2009_q1_02 path: de/2009_q1_02-* - split: 2009_q1_03 path: de/2009_q1_03-* - split: 2009_q2_04 path: de/2009_q2_04-* - split: 2009_q2_05 path: de/2009_q2_05-* - split: 2009_q2_06 path: de/2009_q2_06-* - split: 2009_q3_07 path: de/2009_q3_07-* - split: 2009_q3_08 path: de/2009_q3_08-* - split: 2009_q3_09 path: de/2009_q3_09-* - split: 2009_q4_10 path: de/2009_q4_10-* - split: 2009_q4_11 path: de/2009_q4_11-* - split: 2009_q4_12 path: de/2009_q4_12-* - split: 2010_q1_01 path: de/2010_q1_01-* - split: 2010_q1_02 path: de/2010_q1_02-* - split: 2010_q1_03 path: de/2010_q1_03-* - split: 2010_q2_04 path: de/2010_q2_04-* - split: 2010_q2_05 path: de/2010_q2_05-* - split: 2010_q2_06 path: de/2010_q2_06-* - split: 2010_q3_07 path: de/2010_q3_07-* - split: 2010_q3_08 path: de/2010_q3_08-* - split: 2010_q3_09 path: de/2010_q3_09-* - split: 2010_q4_10 path: de/2010_q4_10-* - split: 2010_q4_11 path: de/2010_q4_11-* - split: 2010_q4_12 path: de/2010_q4_12-* - split: 2011_q1_01 path: de/2011_q1_01-* - split: 2011_q1_02 path: de/2011_q1_02-* - split: 2011_q1_03 path: de/2011_q1_03-* - split: 2011_q2_04 path: de/2011_q2_04-* - split: 2011_q2_05 path: de/2011_q2_05-* - split: 2011_q2_06 path: de/2011_q2_06-* - split: 2011_q3_07 path: de/2011_q3_07-* - split: 2011_q3_08 path: de/2011_q3_08-* - split: 2011_q3_09 path: de/2011_q3_09-* - split: 2011_q4_10 path: de/2011_q4_10-* - split: 2011_q4_11 path: de/2011_q4_11-* - split: 2011_q4_12 path: de/2011_q4_12-* - split: 2012_q1_01 path: de/2012_q1_01-* - split: 2012_q1_02 path: de/2012_q1_02-* - split: 2012_q1_03 path: de/2012_q1_03-* - split: 2012_q2_04 path: de/2012_q2_04-* - split: 2012_q2_05 path: de/2012_q2_05-* - split: 2012_q2_06 path: de/2012_q2_06-* - split: 2012_q3_07 path: de/2012_q3_07-* - split: 2012_q3_08 path: de/2012_q3_08-* - split: 2012_q3_09 path: de/2012_q3_09-* - split: 2012_q4_10 path: de/2012_q4_10-* - split: 2012_q4_11 path: de/2012_q4_11-* - split: 2012_q4_12 path: de/2012_q4_12-* - split: 2013_q1_01 path: de/2013_q1_01-* - split: no_date path: de/no_date-* - split: 2013_q1_02 path: de/2013_q1_02-* - split: 2013_q1_03 path: de/2013_q1_03-* - split: 2013_q2_04 path: de/2013_q2_04-* - split: 2013_q2_05 path: de/2013_q2_05-* - split: 2013_q2_06 path: de/2013_q2_06-* - split: 2013_q3_07 path: de/2013_q3_07-* - split: 2013_q3_09 path: de/2013_q3_09-* - split: 2013_q3_08 path: de/2013_q3_08-* - split: 2013_q4_10 path: de/2013_q4_10-* - split: 2013_q4_11 path: de/2013_q4_11-* - split: 2013_q4_12 path: de/2013_q4_12-* - split: 2014_q1_01 path: de/2014_q1_01-* - split: 2014_q1_02 path: de/2014_q1_02-* - split: 2014_q1_03 path: de/2014_q1_03-* - split: 2014_q2_04 path: de/2014_q2_04-* - split: 2014_q2_05 path: de/2014_q2_05-* - split: 2014_q2_06 path: de/2014_q2_06-* - split: 2014_q3_07 path: de/2014_q3_07-* - split: 2014_q3_08 path: de/2014_q3_08-* - split: 2014_q3_09 path: de/2014_q3_09-* - split: 2014_q4_10 path: de/2014_q4_10-* - split: 2014_q4_11 path: de/2014_q4_11-* - split: 2014_q4_12 path: de/2014_q4_12-* - split: 2015_q1_01 path: de/2015_q1_01-* - split: 2015_q1_02 path: de/2015_q1_02-* - split: 2015_q1_03 path: de/2015_q1_03-* - split: 2015_q2_04 path: de/2015_q2_04-* - split: 2015_q2_06 path: de/2015_q2_06-* - split: 2015_q2_05 path: de/2015_q2_05-* - split: 2015_q3_07 path: de/2015_q3_07-* - split: 2015_q3_08 path: de/2015_q3_08-* - split: 2015_q3_09 path: de/2015_q3_09-* - split: 2015_q4_10 path: de/2015_q4_10-* - split: 2015_q4_11 path: de/2015_q4_11-* - split: 2015_q4_12 path: de/2015_q4_12-* - split: 2016_q1_02 path: de/2016_q1_02-* - split: 2016_q1_01 path: de/2016_q1_01-* - split: 2016_q1_03 path: de/2016_q1_03-* - split: 2016_q2_05 path: de/2016_q2_05-* - split: 2016_q2_04 path: de/2016_q2_04-* - split: 2016_q2_06 path: de/2016_q2_06-* - split: 2016_q3_07 path: de/2016_q3_07-* - split: 2016_q3_08 path: de/2016_q3_08-* - split: 2016_q3_09 path: de/2016_q3_09-* - split: 2016_q4_10 path: de/2016_q4_10-* - split: 2016_q4_12 path: de/2016_q4_12-* - split: 2016_q4_11 path: de/2016_q4_11-* - split: 2017_q1_01 path: de/2017_q1_01-* - split: 2017_q1_02 path: de/2017_q1_02-* - split: 2017_q1_03 path: de/2017_q1_03-* - split: 2017_q2_04 path: de/2017_q2_04-* - split: 2017_q2_05 path: de/2017_q2_05-* - split: 2017_q2_06 path: de/2017_q2_06-* - split: 2017_q3_07 path: de/2017_q3_07-* - split: 2017_q3_08 path: de/2017_q3_08-* - split: 2017_q3_09 path: de/2017_q3_09-* - split: 2017_q4_11 path: de/2017_q4_11-* - split: 2017_q4_10 path: de/2017_q4_10-* - split: 2017_q4_12 path: de/2017_q4_12-* - split: 2018_q1_01 path: de/2018_q1_01-* - split: 2018_q1_02 path: de/2018_q1_02-* - split: 2018_q1_03 path: de/2018_q1_03-* - split: 2018_q2_04 path: de/2018_q2_04-* - split: 2018_q2_05 path: de/2018_q2_05-* - split: 2018_q2_06 path: de/2018_q2_06-* - split: 2018_q3_07 path: de/2018_q3_07-* - split: 2018_q3_08 path: de/2018_q3_08-* - split: 2018_q3_09 path: de/2018_q3_09-* - split: 2018_q4_10 path: de/2018_q4_10-* - split: 2018_q4_11 path: de/2018_q4_11-* - split: 2018_q4_12 path: de/2018_q4_12-* - split: 2019_q1_01 path: de/2019_q1_01-* - split: 2019_q1_02 path: de/2019_q1_02-* - split: 2019_q1_03 path: de/2019_q1_03-* - split: 2019_q2_04 path: de/2019_q2_04-* - split: 2019_q2_05 path: de/2019_q2_05-* - split: 2019_q2_06 path: de/2019_q2_06-* - split: 2019_q3_07 path: de/2019_q3_07-* - split: 2019_q3_08 path: de/2019_q3_08-* - split: 2019_q3_09 path: de/2019_q3_09-* - split: 2019_q4_10 path: de/2019_q4_10-* - split: 2019_q4_11 path: de/2019_q4_11-* - split: 2019_q4_12 path: de/2019_q4_12-* - split: 2020_q1_01 path: de/2020_q1_01-* - split: 2020_q1_02 path: de/2020_q1_02-* - split: 2021_q1_01 path: de/2021_q1_01-* - split: 2020_q1_03 path: de/2020_q1_03-* - split: 2020_q2_04 path: de/2020_q2_04-* - split: 2020_q2_05 path: de/2020_q2_05-* - split: 2020_q2_06 path: de/2020_q2_06-* - split: 2020_q3_07 path: de/2020_q3_07-* - split: 2020_q3_08 path: de/2020_q3_08-* - split: 2020_q4_10 path: de/2020_q4_10-* - split: 2020_q4_11 path: de/2020_q4_11-* - split: 2021_q2_06 path: de/2021_q2_06-* - split: 2021_q1_02 path: de/2021_q1_02-* - split: 2021_q1_03 path: de/2021_q1_03-* - split: 2021_q2_04 path: de/2021_q2_04-* - split: 2021_q2_05 path: de/2021_q2_05-* - split: 2021_q3_08 path: de/2021_q3_08-* - split: 2021_q3_09 path: de/2021_q3_09-* - split: 2021_q4_10 path: de/2021_q4_10-* - split: 2021_q4_11 path: de/2021_q4_11-* - split: 2021_q4_12 path: de/2021_q4_12-* - split: 2022_q1_01 path: de/2022_q1_01-* - split: 2022_q1_02 path: de/2022_q1_02-* - split: 2022_q1_03 path: de/2022_q1_03-* - split: 2022_q2_04 path: de/2022_q2_04-* - split: 2022_q2_05 path: de/2022_q2_05-* - split: 2022_q2_06 path: de/2022_q2_06-* - split: 2022_q3_07 path: de/2022_q3_07-* - split: 2022_q3_08 path: de/2022_q3_08-* - split: 2022_q3_09 path: de/2022_q3_09-* - split: 2022_q4_10 path: de/2022_q4_10-* - split: 2022_q4_11 path: de/2022_q4_11-* - split: 2022_q4_12 path: de/2022_q4_12-* - split: 2023_q1_01 path: de/2023_q1_01-* - split: 2023_q1_02 path: de/2023_q1_02-* - split: 2023_q1_03 path: de/2023_q1_03-* - split: 2023_q2_05 path: de/2023_q2_05-* - split: 2023_q2_06 path: de/2023_q2_06-* - split: 2023_q3_07 path: de/2023_q3_07-* - split: 2023_q3_09 path: de/2023_q3_09-* - split: 2023_q4_10 path: de/2023_q4_10-* - split: 2023_q4_11 path: de/2023_q4_11-* - split: 2023_q4_12 path: de/2023_q4_12-* - split: 2024_q1_01 path: de/2024_q1_01-* - split: '2004' path: de/2004_* - split: '2005' path: de/2005_* - split: '2006' path: de/2006_* - split: '2007' path: de/2007_* - split: '2008' path: de/2008_* - split: '2009' path: de/2009_* - split: '2010' path: de/2010_* - split: '2011' path: de/2011_* - split: '2012' path: de/2012_* - split: '2013' path: de/2013_* - split: '2014' path: de/2014_* - split: '2015' path: de/2015_* - split: '2016' path: de/2016_* - split: '2017' path: de/2017_* - split: '2018' path: de/2018_* - split: '2019' path: de/2019_* - split: '2020' path: de/2020_* - split: '2021' path: de/2021_* - split: '2022' path: de/2022_* - split: '2023' path: de/2023_* - split: '2024' path: de/2024_* - split: 2005_q2 path: de/2005_q2_* - split: 2016_q2 path: de/2016_q2_* - split: 2017_q1 path: de/2017_q1_* - split: 2010_q4 path: de/2010_q4_* - split: 2021_q1 path: de/2021_q1_* - split: 2014_q4 path: de/2014_q4_* - split: 2004_q3 path: de/2004_q3_* - split: 2015_q3 path: de/2015_q3_* - split: 2019_q3 path: de/2019_q3_* - split: 2005_q4 path: de/2005_q4_* - split: 2016_q4 path: de/2016_q4_* - split: 2017_q3 path: de/2017_q3_* - split: 2021_q3 path: de/2021_q3_* - split: 2006_q2 path: de/2006_q2_* - split: 2024_q1 path: de/2024_q1_* - split: 2011_q1 path: de/2011_q1_* - split: 2022_q1 path: de/2022_q1_* - split: 2008_q2 path: de/2008_q2_* - split: 2012_q2 path: de/2012_q2_* - split: 2023_q2 path: de/2023_q2_* - split: 2013_q1 path: de/2013_q1_* - split: 2006_q4 path: de/2006_q4_* - split: 2011_q3 path: de/2011_q3_* - split: 2022_q3 path: de/2022_q3_* - split: 2008_q4 path: de/2008_q4_* - split: 2012_q4 path: de/2012_q4_* - split: 2014_q1 path: de/2014_q1_* - split: 2013_q3 path: de/2013_q3_* - split: 2023_q4 path: de/2023_q4_* - split: 2007_q1 path: de/2007_q1_* - split: 2018_q1 path: de/2018_q1_* - split: 2015_q2 path: de/2015_q2_* - split: 2019_q2 path: de/2019_q2_* - split: 2009_q1 path: de/2009_q1_* - split: 2020_q1 path: de/2020_q1_* - split: 2017_q2 path: de/2017_q2_* - split: 2007_q3 path: de/2007_q3_* - split: 2018_q3 path: de/2018_q3_* - split: 2021_q2 path: de/2021_q2_* - split: 2004_q4 path: de/2004_q4_* - split: 2015_q4 path: de/2015_q4_* - split: 2019_q4 path: de/2019_q4_* - split: 2009_q3 path: de/2009_q3_* - split: 2020_q3 path: de/2020_q3_* - split: 2021_q4 path: de/2021_q4_* - split: 2010_q1 path: de/2010_q1_* - split: 2011_q2 path: de/2011_q2_* - split: 2022_q2 path: de/2022_q2_* - split: 2005_q1 path: de/2005_q1_* - split: 2016_q1 path: de/2016_q1_* - split: 2010_q3 path: de/2010_q3_* - split: 2013_q2 path: de/2013_q2_* - split: 2014_q3 path: de/2014_q3_* - split: 2011_q4 path: de/2011_q4_* - split: 2022_q4 path: de/2022_q4_* - split: 2005_q3 path: de/2005_q3_* - split: 2016_q3 path: de/2016_q3_* - split: 2013_q4 path: de/2013_q4_* - split: 2019_q1 path: de/2019_q1_* - split: 2006_q1 path: de/2006_q1_* - split: 2007_q2 path: de/2007_q2_* - split: 2017_q4 path: de/2017_q4_* - split: 2008_q1 path: de/2008_q1_* - split: 2018_q2 path: de/2018_q2_* - split: 2012_q1 path: de/2012_q1_* - split: 2023_q1 path: de/2023_q1_* - split: 2006_q3 path: de/2006_q3_* - split: 2009_q2 path: de/2009_q2_* - split: 2020_q2 path: de/2020_q2_* - split: 2007_q4 path: de/2007_q4_* - split: 2018_q4 path: de/2018_q4_* - split: 2008_q3 path: de/2008_q3_* - split: 2012_q3 path: de/2012_q3_* - split: 2023_q3 path: de/2023_q3_* - split: 2009_q4 path: de/2009_q4_* - split: 2020_q4 path: de/2020_q4_* - split: 2010_q2 path: de/2010_q2_* - split: 2014_q2 path: de/2014_q2_* - split: 2015_q1 path: de/2015_q1_* - config_name: en data_files: - split: 2004_q4_11 path: en/2004_q4_11-* - split: no_date path: en/no_date-* - split: 2004_q4_12 path: en/2004_q4_12-* - split: 2005_q1_01 path: en/2005_q1_01-* - split: 2007_q1_01 path: en/2007_q1_01-* - split: 2005_q1_02 path: en/2005_q1_02-* - split: 2005_q2_04 path: en/2005_q2_04-* - split: 2015_q3_08 path: en/2015_q3_08-* - split: 2005_q1_03 path: en/2005_q1_03-* - split: 2024_q1_03 path: en/2024_q1_03-* - split: 2024_q2_04 path: en/2024_q2_04-* - split: 2005_q2_05 path: en/2005_q2_05-* - split: 2005_q3_09 path: en/2005_q3_09-* - split: 2005_q2_06 path: en/2005_q2_06-* - split: 2005_q3_07 path: en/2005_q3_07-* - split: 2005_q3_08 path: en/2005_q3_08-* - split: 2006_q4_12 path: en/2006_q4_12-* - split: 2005_q4_10 path: en/2005_q4_10-* - split: 2005_q4_11 path: en/2005_q4_11-* - split: 2005_q4_12 path: en/2005_q4_12-* - split: 2006_q1_01 path: en/2006_q1_01-* - split: 2006_q1_03 path: en/2006_q1_03-* - split: 2006_q1_02 path: en/2006_q1_02-* - split: 2009_q1_03 path: en/2009_q1_03-* - split: 2006_q2_04 path: en/2006_q2_04-* - split: 2006_q2_05 path: en/2006_q2_05-* - split: 2006_q2_06 path: en/2006_q2_06-* - split: 2006_q3_07 path: en/2006_q3_07-* - split: 2006_q3_08 path: en/2006_q3_08-* - split: 2006_q4_10 path: en/2006_q4_10-* - split: 2006_q3_09 path: en/2006_q3_09-* - split: 2006_q4_11 path: en/2006_q4_11-* - split: 2007_q1_02 path: en/2007_q1_02-* - split: 2007_q1_03 path: en/2007_q1_03-* - split: 2007_q2_05 path: en/2007_q2_05-* - split: 2007_q2_04 path: en/2007_q2_04-* - split: 2007_q3_08 path: en/2007_q3_08-* - split: 2011_q4_10 path: en/2011_q4_10-* - split: 2008_q2_06 path: en/2008_q2_06-* - split: 2009_q4_11 path: en/2009_q4_11-* - split: 2007_q4_10 path: en/2007_q4_10-* - split: 2007_q2_06 path: en/2007_q2_06-* - split: 2007_q4_11 path: en/2007_q4_11-* - split: 2007_q3_07 path: en/2007_q3_07-* - split: 2007_q3_09 path: en/2007_q3_09-* - split: 2008_q1_01 path: en/2008_q1_01-* - split: 2007_q4_12 path: en/2007_q4_12-* - split: 2009_q1_01 path: en/2009_q1_01-* - split: 2008_q1_02 path: en/2008_q1_02-* - split: 2008_q1_03 path: en/2008_q1_03-* - split: 2008_q2_04 path: en/2008_q2_04-* - split: 2008_q3_08 path: en/2008_q3_08-* - split: 2008_q2_05 path: en/2008_q2_05-* - split: 2009_q3_08 path: en/2009_q3_08-* - split: 2008_q3_07 path: en/2008_q3_07-* - split: 2008_q3_09 path: en/2008_q3_09-* - split: 2009_q3_09 path: en/2009_q3_09-* - split: 2009_q3_07 path: en/2009_q3_07-* - split: 2008_q4_10 path: en/2008_q4_10-* - split: 2008_q4_11 path: en/2008_q4_11-* - split: 2008_q4_12 path: en/2008_q4_12-* - split: 2009_q1_02 path: en/2009_q1_02-* - split: 2009_q2_05 path: en/2009_q2_05-* - split: 2009_q2_04 path: en/2009_q2_04-* - split: 2009_q2_06 path: en/2009_q2_06-* - split: 2009_q4_10 path: en/2009_q4_10-* - split: 2009_q4_12 path: en/2009_q4_12-* - split: 2010_q1_01 path: en/2010_q1_01-* - split: 2010_q2_04 path: en/2010_q2_04-* - split: 2010_q1_02 path: en/2010_q1_02-* - split: 2010_q1_03 path: en/2010_q1_03-* - split: 2010_q4_12 path: en/2010_q4_12-* - split: 2010_q2_05 path: en/2010_q2_05-* - split: 2010_q3_09 path: en/2010_q3_09-* - split: 2010_q2_06 path: en/2010_q2_06-* - split: 2010_q3_07 path: en/2010_q3_07-* - split: 2010_q3_08 path: en/2010_q3_08-* - split: 2010_q4_10 path: en/2010_q4_10-* - split: 2010_q4_11 path: en/2010_q4_11-* - split: 2011_q4_12 path: en/2011_q4_12-* - split: 2011_q1_01 path: en/2011_q1_01-* - split: 2011_q1_02 path: en/2011_q1_02-* - split: 2011_q1_03 path: en/2011_q1_03-* - split: 2011_q2_04 path: en/2011_q2_04-* - split: 2011_q2_05 path: en/2011_q2_05-* - split: 2011_q2_06 path: en/2011_q2_06-* - split: 2011_q3_07 path: en/2011_q3_07-* - split: 2011_q3_08 path: en/2011_q3_08-* - split: 2011_q3_09 path: en/2011_q3_09-* - split: 2011_q4_11 path: en/2011_q4_11-* - split: 2012_q1_01 path: en/2012_q1_01-* - split: 2012_q2_05 path: en/2012_q2_05-* - split: 2012_q1_02 path: en/2012_q1_02-* - split: 2012_q1_03 path: en/2012_q1_03-* - split: 2012_q2_04 path: en/2012_q2_04-* - split: 2012_q2_06 path: en/2012_q2_06-* - split: 2012_q3_07 path: en/2012_q3_07-* - split: 2012_q3_08 path: en/2012_q3_08-* - split: 2012_q3_09 path: en/2012_q3_09-* - split: 2012_q4_10 path: en/2012_q4_10-* - split: 2012_q4_11 path: en/2012_q4_11-* - split: 2012_q4_12 path: en/2012_q4_12-* - split: 2013_q1_02 path: en/2013_q1_02-* - split: 2013_q1_01 path: en/2013_q1_01-* - split: 2013_q1_03 path: en/2013_q1_03-* - split: 2013_q2_04 path: en/2013_q2_04-* - split: 2013_q2_05 path: en/2013_q2_05-* - split: 2013_q2_06 path: en/2013_q2_06-* - split: 2013_q3_07 path: en/2013_q3_07-* - split: 2013_q3_08 path: en/2013_q3_08-* - split: 2013_q3_09 path: en/2013_q3_09-* - split: 2013_q4_10 path: en/2013_q4_10-* - split: 2013_q4_11 path: en/2013_q4_11-* - split: 2013_q4_12 path: en/2013_q4_12-* - split: 2014_q1_01 path: en/2014_q1_01-* - split: 2014_q1_02 path: en/2014_q1_02-* - split: 2014_q1_03 path: en/2014_q1_03-* - split: 2014_q2_04 path: en/2014_q2_04-* - split: 2014_q2_05 path: en/2014_q2_05-* - split: 2014_q2_06 path: en/2014_q2_06-* - split: 2014_q3_07 path: en/2014_q3_07-* - split: 2014_q3_08 path: en/2014_q3_08-* - split: 2014_q3_09 path: en/2014_q3_09-* - split: 2014_q4_11 path: en/2014_q4_11-* - split: 2014_q4_10 path: en/2014_q4_10-* - split: 2014_q4_12 path: en/2014_q4_12-* - split: 2015_q1_01 path: en/2015_q1_01-* - split: 2015_q1_02 path: en/2015_q1_02-* - split: 2015_q1_03 path: en/2015_q1_03-* - split: 2015_q2_04 path: en/2015_q2_04-* - split: 2015_q2_05 path: en/2015_q2_05-* - split: 2015_q2_06 path: en/2015_q2_06-* - split: 2015_q3_07 path: en/2015_q3_07-* - split: 2015_q3_09 path: en/2015_q3_09-* - split: 2015_q4_10 path: en/2015_q4_10-* - split: 2015_q4_11 path: en/2015_q4_11-* - split: 2015_q4_12 path: en/2015_q4_12-* - split: 2016_q1_01 path: en/2016_q1_01-* - split: 2016_q1_02 path: en/2016_q1_02-* - split: 2016_q1_03 path: en/2016_q1_03-* - split: 2016_q2_04 path: en/2016_q2_04-* - split: 2016_q2_05 path: en/2016_q2_05-* - split: 2016_q2_06 path: en/2016_q2_06-* - split: 2016_q3_07 path: en/2016_q3_07-* - split: 2016_q3_08 path: en/2016_q3_08-* - split: 2016_q3_09 path: en/2016_q3_09-* - split: 2016_q4_10 path: en/2016_q4_10-* - split: 2016_q4_11 path: en/2016_q4_11-* - split: 2016_q4_12 path: en/2016_q4_12-* - split: 2017_q1_01 path: en/2017_q1_01-* - split: 2017_q1_02 path: en/2017_q1_02-* - split: 2017_q1_03 path: en/2017_q1_03-* - split: 2017_q2_04 path: en/2017_q2_04-* - split: 2017_q2_05 path: en/2017_q2_05-* - split: 2018_q1_01 path: en/2018_q1_01-* - split: 2017_q2_06 path: en/2017_q2_06-* - split: 2017_q3_07 path: en/2017_q3_07-* - split: 2017_q3_08 path: en/2017_q3_08-* - split: 2017_q4_10 path: en/2017_q4_10-* - split: 2017_q3_09 path: en/2017_q3_09-* - split: 2017_q4_11 path: en/2017_q4_11-* - split: 2017_q4_12 path: en/2017_q4_12-* - split: 2018_q1_02 path: en/2018_q1_02-* - split: 2018_q1_03 path: en/2018_q1_03-* - split: 2018_q2_04 path: en/2018_q2_04-* - split: 2018_q2_05 path: en/2018_q2_05-* - split: 2018_q2_06 path: en/2018_q2_06-* - split: 2018_q3_07 path: en/2018_q3_07-* - split: 2018_q3_08 path: en/2018_q3_08-* - split: 2018_q3_09 path: en/2018_q3_09-* - split: 2018_q4_10 path: en/2018_q4_10-* - split: 2019_q1_01 path: en/2019_q1_01-* - split: 2018_q4_11 path: en/2018_q4_11-* - split: 2018_q4_12 path: en/2018_q4_12-* - split: 2019_q1_02 path: en/2019_q1_02-* - split: 2019_q1_03 path: en/2019_q1_03-* - split: 2019_q2_04 path: en/2019_q2_04-* - split: 2019_q2_05 path: en/2019_q2_05-* - split: 2019_q2_06 path: en/2019_q2_06-* - split: 2019_q3_07 path: en/2019_q3_07-* - split: 2019_q3_08 path: en/2019_q3_08-* - split: 2019_q3_09 path: en/2019_q3_09-* - split: 2019_q4_10 path: en/2019_q4_10-* - split: 2019_q4_11 path: en/2019_q4_11-* - split: 2019_q4_12 path: en/2019_q4_12-* - split: 2020_q1_01 path: en/2020_q1_01-* - split: 2020_q1_02 path: en/2020_q1_02-* - split: 2020_q1_03 path: en/2020_q1_03-* - split: 2020_q2_04 path: en/2020_q2_04-* - split: 2020_q3_08 path: en/2020_q3_08-* - split: 2020_q2_05 path: en/2020_q2_05-* - split: 2020_q2_06 path: en/2020_q2_06-* - split: 2020_q3_07 path: en/2020_q3_07-* - split: 2020_q3_09 path: en/2020_q3_09-* - split: 2020_q4_10 path: en/2020_q4_10-* - split: 2020_q4_12 path: en/2020_q4_12-* - split: 2020_q4_11 path: en/2020_q4_11-* - split: 2021_q2_04 path: en/2021_q2_04-* - split: 2021_q1_01 path: en/2021_q1_01-* - split: 2021_q1_02 path: en/2021_q1_02-* - split: 2021_q1_03 path: en/2021_q1_03-* - split: 2021_q2_05 path: en/2021_q2_05-* - split: 2021_q2_06 path: en/2021_q2_06-* - split: 2021_q3_07 path: en/2021_q3_07-* - split: 2021_q3_08 path: en/2021_q3_08-* - split: 2021_q3_09 path: en/2021_q3_09-* - split: 2021_q4_10 path: en/2021_q4_10-* - split: 2021_q4_11 path: en/2021_q4_11-* - split: 2022_q1_02 path: en/2022_q1_02-* - split: 2021_q4_12 path: en/2021_q4_12-* - split: 2022_q1_01 path: en/2022_q1_01-* - split: 2022_q1_03 path: en/2022_q1_03-* - split: 2022_q2_04 path: en/2022_q2_04-* - split: 2022_q2_05 path: en/2022_q2_05-* - split: 2022_q2_06 path: en/2022_q2_06-* - split: 2022_q3_07 path: en/2022_q3_07-* - split: 2022_q3_08 path: en/2022_q3_08-* - split: 2022_q3_09 path: en/2022_q3_09-* - split: 2022_q4_11 path: en/2022_q4_11-* - split: 2022_q4_10 path: en/2022_q4_10-* - split: 2022_q4_12 path: en/2022_q4_12-* - split: 2023_q1_01 path: en/2023_q1_01-* - split: 2023_q1_02 path: en/2023_q1_02-* - split: 2023_q1_03 path: en/2023_q1_03-* - split: 2023_q2_04 path: en/2023_q2_04-* - split: 2023_q2_05 path: en/2023_q2_05-* - split: 2023_q2_06 path: en/2023_q2_06-* - split: 2023_q3_07 path: en/2023_q3_07-* - split: 2023_q3_08 path: en/2023_q3_08-* - split: 2023_q3_09 path: en/2023_q3_09-* - split: 2023_q4_10 path: en/2023_q4_10-* - split: 2023_q4_12 path: en/2023_q4_12-* - split: 2023_q4_11 path: en/2023_q4_11-* - split: 2024_q1_01 path: en/2024_q1_01-* - split: '2004' path: en/2004_* - split: '2005' path: en/2005_* - split: '2006' path: en/2006_* - split: '2007' path: en/2007_* - split: '2008' path: en/2008_* - split: '2009' path: en/2009_* - split: '2010' path: en/2010_* - split: '2011' path: en/2011_* - split: '2012' path: en/2012_* - split: '2013' path: en/2013_* - split: '2014' path: en/2014_* - split: '2015' path: en/2015_* - split: '2016' path: en/2016_* - split: '2017' path: en/2017_* - split: '2018' path: en/2018_* - split: '2019' path: en/2019_* - split: '2020' path: en/2020_* - split: '2021' path: en/2021_* - split: '2022' path: en/2022_* - split: '2023' path: en/2023_* - split: '2024' path: en/2024_* - split: 2005_q2 path: en/2005_q2_* - split: 2016_q2 path: en/2016_q2_* - split: 2017_q1 path: en/2017_q1_* - split: 2010_q4 path: en/2010_q4_* - split: 2021_q1 path: en/2021_q1_* - split: 2014_q4 path: en/2014_q4_* - split: 2015_q3 path: en/2015_q3_* - split: 2019_q3 path: en/2019_q3_* - split: 2005_q4 path: en/2005_q4_* - split: 2016_q4 path: en/2016_q4_* - split: 2017_q3 path: en/2017_q3_* - split: 2021_q3 path: en/2021_q3_* - split: 2006_q2 path: en/2006_q2_* - split: 2024_q1 path: en/2024_q1_* - split: 2011_q1 path: en/2011_q1_* - split: 2022_q1 path: en/2022_q1_* - split: 2008_q2 path: en/2008_q2_* - split: 2012_q2 path: en/2012_q2_* - split: 2023_q2 path: en/2023_q2_* - split: 2013_q1 path: en/2013_q1_* - split: 2006_q4 path: en/2006_q4_* - split: 2011_q3 path: en/2011_q3_* - split: 2022_q3 path: en/2022_q3_* - split: 2008_q4 path: en/2008_q4_* - split: 2012_q4 path: en/2012_q4_* - split: 2014_q1 path: en/2014_q1_* - split: 2013_q3 path: en/2013_q3_* - split: 2023_q4 path: en/2023_q4_* - split: 2007_q1 path: en/2007_q1_* - split: 2018_q1 path: en/2018_q1_* - split: 2015_q2 path: en/2015_q2_* - split: 2019_q2 path: en/2019_q2_* - split: 2009_q1 path: en/2009_q1_* - split: 2020_q1 path: en/2020_q1_* - split: 2017_q2 path: en/2017_q2_* - split: 2007_q3 path: en/2007_q3_* - split: 2018_q3 path: en/2018_q3_* - split: 2021_q2 path: en/2021_q2_* - split: 2004_q4 path: en/2004_q4_* - split: 2015_q4 path: en/2015_q4_* - split: 2019_q4 path: en/2019_q4_* - split: 2009_q3 path: en/2009_q3_* - split: 2020_q3 path: en/2020_q3_* - split: 2021_q4 path: en/2021_q4_* - split: 2010_q1 path: en/2010_q1_* - split: 2011_q2 path: en/2011_q2_* - split: 2022_q2 path: en/2022_q2_* - split: 2005_q1 path: en/2005_q1_* - split: 2016_q1 path: en/2016_q1_* - split: 2010_q3 path: en/2010_q3_* - split: 2013_q2 path: en/2013_q2_* - split: 2014_q3 path: en/2014_q3_* - split: 2011_q4 path: en/2011_q4_* - split: 2022_q4 path: en/2022_q4_* - split: 2005_q3 path: en/2005_q3_* - split: 2016_q3 path: en/2016_q3_* - split: 2013_q4 path: en/2013_q4_* - split: 2019_q1 path: en/2019_q1_* - split: 2006_q1 path: en/2006_q1_* - split: 2007_q2 path: en/2007_q2_* - split: 2017_q4 path: en/2017_q4_* - split: 2008_q1 path: en/2008_q1_* - split: 2018_q2 path: en/2018_q2_* - split: 2012_q1 path: en/2012_q1_* - split: 2023_q1 path: en/2023_q1_* - split: 2006_q3 path: en/2006_q3_* - split: 2009_q2 path: en/2009_q2_* - split: 2020_q2 path: en/2020_q2_* - split: 2024_q2 path: en/2024_q2_* - split: 2007_q4 path: en/2007_q4_* - split: 2018_q4 path: en/2018_q4_* - split: 2008_q3 path: en/2008_q3_* - split: 2012_q3 path: en/2012_q3_* - split: 2023_q3 path: en/2023_q3_* - split: 2009_q4 path: en/2009_q4_* - split: 2020_q4 path: en/2020_q4_* - split: 2010_q2 path: en/2010_q2_* - split: 2014_q2 path: en/2014_q2_* - split: 2015_q1 path: en/2015_q1_* - config_name: es data_files: - split: 2005_q1_01 path: es/2005_q1_01-* - split: 2005_q1_02 path: es/2005_q1_02-* - split: 2004_q1_02 path: es/2004_q1_02-* - split: 2005_q1_03 path: es/2005_q1_03-* - split: no_date path: es/no_date-* - split: 2005_q2_04 path: es/2005_q2_04-* - split: 2005_q2_05 path: es/2005_q2_05-* - split: 2005_q2_06 path: es/2005_q2_06-* - split: 2005_q3_07 path: es/2005_q3_07-* - split: 2005_q3_08 path: es/2005_q3_08-* - split: 2005_q3_09 path: es/2005_q3_09-* - split: 2005_q4_10 path: es/2005_q4_10-* - split: 2005_q4_12 path: es/2005_q4_12-* - split: 2006_q4_10 path: es/2006_q4_10-* - split: 2005_q4_11 path: es/2005_q4_11-* - split: 2006_q1_01 path: es/2006_q1_01-* - split: 2006_q1_02 path: es/2006_q1_02-* - split: 2006_q1_03 path: es/2006_q1_03-* - split: 2006_q2_04 path: es/2006_q2_04-* - split: 2006_q2_05 path: es/2006_q2_05-* - split: 2006_q2_06 path: es/2006_q2_06-* - split: 2006_q3_07 path: es/2006_q3_07-* - split: 2006_q3_08 path: es/2006_q3_08-* - split: 2006_q3_09 path: es/2006_q3_09-* - split: 2006_q4_11 path: es/2006_q4_11-* - split: 2006_q4_12 path: es/2006_q4_12-* - split: 2007_q1_01 path: es/2007_q1_01-* - split: 2007_q1_02 path: es/2007_q1_02-* - split: 2007_q1_03 path: es/2007_q1_03-* - split: 2007_q2_04 path: es/2007_q2_04-* - split: 2007_q2_05 path: es/2007_q2_05-* - split: 2007_q2_06 path: es/2007_q2_06-* - split: 2007_q3_07 path: es/2007_q3_07-* - split: 2007_q3_08 path: es/2007_q3_08-* - split: 2007_q3_09 path: es/2007_q3_09-* - split: 2007_q4_10 path: es/2007_q4_10-* - split: 2007_q4_11 path: es/2007_q4_11-* - split: 2007_q4_12 path: es/2007_q4_12-* - split: 2008_q1_01 path: es/2008_q1_01-* - split: 2008_q1_02 path: es/2008_q1_02-* - split: 2008_q1_03 path: es/2008_q1_03-* - split: 2008_q2_04 path: es/2008_q2_04-* - split: 2008_q2_05 path: es/2008_q2_05-* - split: 2008_q2_06 path: es/2008_q2_06-* - split: 2008_q3_07 path: es/2008_q3_07-* - split: 2008_q3_08 path: es/2008_q3_08-* - split: 2008_q3_09 path: es/2008_q3_09-* - split: 2008_q4_10 path: es/2008_q4_10-* - split: 2008_q4_11 path: es/2008_q4_11-* - split: 2008_q4_12 path: es/2008_q4_12-* - split: 2009_q1_01 path: es/2009_q1_01-* - split: 2009_q1_02 path: es/2009_q1_02-* - split: 2009_q1_03 path: es/2009_q1_03-* - split: 2009_q2_04 path: es/2009_q2_04-* - split: 2009_q2_05 path: es/2009_q2_05-* - split: 2009_q2_06 path: es/2009_q2_06-* - split: 2009_q3_07 path: es/2009_q3_07-* - split: 2009_q3_08 path: es/2009_q3_08-* - split: 2009_q3_09 path: es/2009_q3_09-* - split: 2009_q4_10 path: es/2009_q4_10-* - split: 2009_q4_11 path: es/2009_q4_11-* - split: 2009_q4_12 path: es/2009_q4_12-* - split: 2010_q1_01 path: es/2010_q1_01-* - split: 2010_q1_02 path: es/2010_q1_02-* - split: 2010_q1_03 path: es/2010_q1_03-* - split: 2011_q1_02 path: es/2011_q1_02-* - split: 2010_q2_04 path: es/2010_q2_04-* - split: 2010_q2_05 path: es/2010_q2_05-* - split: 2010_q2_06 path: es/2010_q2_06-* - split: 2010_q3_07 path: es/2010_q3_07-* - split: 2010_q3_08 path: es/2010_q3_08-* - split: 2010_q3_09 path: es/2010_q3_09-* - split: 2010_q4_10 path: es/2010_q4_10-* - split: 2010_q4_11 path: es/2010_q4_11-* - split: 2010_q4_12 path: es/2010_q4_12-* - split: 2011_q1_01 path: es/2011_q1_01-* - split: 2013_q2_04 path: es/2013_q2_04-* - split: 2011_q1_03 path: es/2011_q1_03-* - split: 2011_q2_04 path: es/2011_q2_04-* - split: 2011_q2_05 path: es/2011_q2_05-* - split: 2011_q2_06 path: es/2011_q2_06-* - split: 2011_q3_07 path: es/2011_q3_07-* - split: 2011_q3_08 path: es/2011_q3_08-* - split: 2011_q3_09 path: es/2011_q3_09-* - split: 2011_q4_10 path: es/2011_q4_10-* - split: 2011_q4_11 path: es/2011_q4_11-* - split: 2011_q4_12 path: es/2011_q4_12-* - split: 2012_q1_01 path: es/2012_q1_01-* - split: 2012_q1_02 path: es/2012_q1_02-* - split: 2012_q1_03 path: es/2012_q1_03-* - split: 2012_q2_04 path: es/2012_q2_04-* - split: 2012_q2_05 path: es/2012_q2_05-* - split: 2012_q2_06 path: es/2012_q2_06-* - split: 2012_q3_07 path: es/2012_q3_07-* - split: 2012_q3_08 path: es/2012_q3_08-* - split: 2012_q3_09 path: es/2012_q3_09-* - split: 2012_q4_10 path: es/2012_q4_10-* - split: 2012_q4_11 path: es/2012_q4_11-* - split: 2012_q4_12 path: es/2012_q4_12-* - split: 2013_q1_01 path: es/2013_q1_01-* - split: 2013_q1_02 path: es/2013_q1_02-* - split: 2013_q1_03 path: es/2013_q1_03-* - split: 2013_q2_05 path: es/2013_q2_05-* - split: 2013_q2_06 path: es/2013_q2_06-* - split: 2013_q3_07 path: es/2013_q3_07-* - split: 2013_q3_08 path: es/2013_q3_08-* - split: 2013_q3_09 path: es/2013_q3_09-* - split: 2013_q4_11 path: es/2013_q4_11-* - split: 2013_q4_10 path: es/2013_q4_10-* - split: 2013_q4_12 path: es/2013_q4_12-* - split: 2014_q1_01 path: es/2014_q1_01-* - split: 2014_q1_02 path: es/2014_q1_02-* - split: 2024_q1_02 path: es/2024_q1_02-* - split: 2014_q1_03 path: es/2014_q1_03-* - split: 2014_q2_04 path: es/2014_q2_04-* - split: 2014_q2_05 path: es/2014_q2_05-* - split: 2014_q2_06 path: es/2014_q2_06-* - split: 2014_q3_07 path: es/2014_q3_07-* - split: 2014_q3_08 path: es/2014_q3_08-* - split: 2014_q3_09 path: es/2014_q3_09-* - split: 2014_q4_10 path: es/2014_q4_10-* - split: 2014_q4_11 path: es/2014_q4_11-* - split: 2014_q4_12 path: es/2014_q4_12-* - split: 2015_q1_01 path: es/2015_q1_01-* - split: 2015_q1_02 path: es/2015_q1_02-* - split: 2015_q1_03 path: es/2015_q1_03-* - split: 2015_q2_04 path: es/2015_q2_04-* - split: 2015_q2_05 path: es/2015_q2_05-* - split: 2015_q2_06 path: es/2015_q2_06-* - split: 2015_q3_07 path: es/2015_q3_07-* - split: 2015_q3_08 path: es/2015_q3_08-* - split: 2015_q3_09 path: es/2015_q3_09-* - split: 2015_q4_10 path: es/2015_q4_10-* - split: 2015_q4_11 path: es/2015_q4_11-* - split: 2015_q4_12 path: es/2015_q4_12-* - split: 2016_q1_01 path: es/2016_q1_01-* - split: 2016_q1_02 path: es/2016_q1_02-* - split: 2016_q1_03 path: es/2016_q1_03-* - split: 2016_q2_04 path: es/2016_q2_04-* - split: 2016_q2_05 path: es/2016_q2_05-* - split: 2016_q2_06 path: es/2016_q2_06-* - split: 2016_q3_07 path: es/2016_q3_07-* - split: 2016_q3_08 path: es/2016_q3_08-* - split: 2016_q3_09 path: es/2016_q3_09-* - split: 2016_q4_10 path: es/2016_q4_10-* - split: 2016_q4_11 path: es/2016_q4_11-* - split: 2016_q4_12 path: es/2016_q4_12-* - split: 2017_q1_01 path: es/2017_q1_01-* - split: 2017_q1_02 path: es/2017_q1_02-* - split: 2017_q1_03 path: es/2017_q1_03-* - split: 2017_q2_04 path: es/2017_q2_04-* - split: 2017_q2_05 path: es/2017_q2_05-* - split: 2017_q2_06 path: es/2017_q2_06-* - split: 2017_q3_07 path: es/2017_q3_07-* - split: 2017_q3_08 path: es/2017_q3_08-* - split: 2017_q3_09 path: es/2017_q3_09-* - split: 2017_q4_10 path: es/2017_q4_10-* - split: 2017_q4_11 path: es/2017_q4_11-* - split: 2017_q4_12 path: es/2017_q4_12-* - split: 2018_q1_01 path: es/2018_q1_01-* - split: 2018_q1_02 path: es/2018_q1_02-* - split: 2018_q1_03 path: es/2018_q1_03-* - split: 2018_q2_04 path: es/2018_q2_04-* - split: 2018_q2_05 path: es/2018_q2_05-* - split: 2018_q2_06 path: es/2018_q2_06-* - split: 2018_q3_07 path: es/2018_q3_07-* - split: 2018_q3_08 path: es/2018_q3_08-* - split: 2018_q3_09 path: es/2018_q3_09-* - split: 2018_q4_10 path: es/2018_q4_10-* - split: 2018_q4_11 path: es/2018_q4_11-* - split: 2018_q4_12 path: es/2018_q4_12-* - split: 2019_q1_01 path: es/2019_q1_01-* - split: 2019_q1_02 path: es/2019_q1_02-* - split: 2019_q1_03 path: es/2019_q1_03-* - split: 2019_q2_04 path: es/2019_q2_04-* - split: 2019_q2_05 path: es/2019_q2_05-* - split: 2019_q2_06 path: es/2019_q2_06-* - split: 2019_q3_07 path: es/2019_q3_07-* - split: 2019_q3_08 path: es/2019_q3_08-* - split: 2019_q3_09 path: es/2019_q3_09-* - split: 2019_q4_10 path: es/2019_q4_10-* - split: 2019_q4_11 path: es/2019_q4_11-* - split: 2019_q4_12 path: es/2019_q4_12-* - split: 2020_q1_01 path: es/2020_q1_01-* - split: 2020_q1_02 path: es/2020_q1_02-* - split: 2020_q1_03 path: es/2020_q1_03-* - split: 2020_q2_04 path: es/2020_q2_04-* - split: 2020_q2_05 path: es/2020_q2_05-* - split: 2020_q2_06 path: es/2020_q2_06-* - split: 2020_q3_07 path: es/2020_q3_07-* - split: 2020_q3_08 path: es/2020_q3_08-* - split: 2020_q3_09 path: es/2020_q3_09-* - split: 2020_q4_10 path: es/2020_q4_10-* - split: 2020_q4_11 path: es/2020_q4_11-* - split: 2020_q4_12 path: es/2020_q4_12-* - split: 2021_q1_01 path: es/2021_q1_01-* - split: 2021_q1_02 path: es/2021_q1_02-* - split: 2021_q1_03 path: es/2021_q1_03-* - split: 2021_q2_04 path: es/2021_q2_04-* - split: 2021_q2_05 path: es/2021_q2_05-* - split: 2021_q2_06 path: es/2021_q2_06-* - split: 2021_q3_07 path: es/2021_q3_07-* - split: 2021_q3_08 path: es/2021_q3_08-* - split: 2021_q3_09 path: es/2021_q3_09-* - split: 2021_q4_10 path: es/2021_q4_10-* - split: 2021_q4_11 path: es/2021_q4_11-* - split: 2021_q4_12 path: es/2021_q4_12-* - split: 2022_q1_01 path: es/2022_q1_01-* - split: 2022_q1_02 path: es/2022_q1_02-* - split: 2022_q1_03 path: es/2022_q1_03-* - split: 2022_q2_04 path: es/2022_q2_04-* - split: 2022_q2_05 path: es/2022_q2_05-* - split: 2022_q2_06 path: es/2022_q2_06-* - split: 2022_q3_07 path: es/2022_q3_07-* - split: 2022_q3_08 path: es/2022_q3_08-* - split: 2022_q3_09 path: es/2022_q3_09-* - split: 2022_q4_10 path: es/2022_q4_10-* - split: 2022_q4_11 path: es/2022_q4_11-* - split: 2022_q4_12 path: es/2022_q4_12-* - split: 2023_q1_01 path: es/2023_q1_01-* - split: 2023_q1_02 path: es/2023_q1_02-* - split: 2023_q1_03 path: es/2023_q1_03-* - split: 2023_q2_04 path: es/2023_q2_04-* - split: 2023_q2_05 path: es/2023_q2_05-* - split: 2023_q2_06 path: es/2023_q2_06-* - split: 2023_q3_07 path: es/2023_q3_07-* - split: 2023_q3_08 path: es/2023_q3_08-* - split: 2023_q3_09 path: es/2023_q3_09-* - split: 2023_q4_10 path: es/2023_q4_10-* - split: 2023_q4_11 path: es/2023_q4_11-* - split: 2023_q4_12 path: es/2023_q4_12-* - split: 2024_q1_01 path: es/2024_q1_01-* - split: '2004' path: es/2004_* - split: '2005' path: es/2005_* - split: '2006' path: es/2006_* - split: '2007' path: es/2007_* - split: '2008' path: es/2008_* - split: '2009' path: es/2009_* - split: '2010' path: es/2010_* - split: '2011' path: es/2011_* - split: '2012' path: es/2012_* - split: '2013' path: es/2013_* - split: '2014' path: es/2014_* - split: '2015' path: es/2015_* - split: '2016' path: es/2016_* - split: '2017' path: es/2017_* - split: '2018' path: es/2018_* - split: '2019' path: es/2019_* - split: '2020' path: es/2020_* - split: '2021' path: es/2021_* - split: '2022' path: es/2022_* - split: '2023' path: es/2023_* - split: '2024' path: es/2024_* - split: 2005_q2 path: es/2005_q2_* - split: 2016_q2 path: es/2016_q2_* - split: 2017_q1 path: es/2017_q1_* - split: 2010_q4 path: es/2010_q4_* - split: 2021_q1 path: es/2021_q1_* - split: 2014_q4 path: es/2014_q4_* - split: 2015_q3 path: es/2015_q3_* - split: 2019_q3 path: es/2019_q3_* - split: 2005_q4 path: es/2005_q4_* - split: 2016_q4 path: es/2016_q4_* - split: 2017_q3 path: es/2017_q3_* - split: 2021_q3 path: es/2021_q3_* - split: 2006_q2 path: es/2006_q2_* - split: 2024_q1 path: es/2024_q1_* - split: 2011_q1 path: es/2011_q1_* - split: 2022_q1 path: es/2022_q1_* - split: 2008_q2 path: es/2008_q2_* - split: 2012_q2 path: es/2012_q2_* - split: 2023_q2 path: es/2023_q2_* - split: 2013_q1 path: es/2013_q1_* - split: 2006_q4 path: es/2006_q4_* - split: 2011_q3 path: es/2011_q3_* - split: 2022_q3 path: es/2022_q3_* - split: 2008_q4 path: es/2008_q4_* - split: 2012_q4 path: es/2012_q4_* - split: 2014_q1 path: es/2014_q1_* - split: 2013_q3 path: es/2013_q3_* - split: 2023_q4 path: es/2023_q4_* - split: 2007_q1 path: es/2007_q1_* - split: 2018_q1 path: es/2018_q1_* - split: 2015_q2 path: es/2015_q2_* - split: 2019_q2 path: es/2019_q2_* - split: 2009_q1 path: es/2009_q1_* - split: 2020_q1 path: es/2020_q1_* - split: 2017_q2 path: es/2017_q2_* - split: 2007_q3 path: es/2007_q3_* - split: 2018_q3 path: es/2018_q3_* - split: 2021_q2 path: es/2021_q2_* - split: 2015_q4 path: es/2015_q4_* - split: 2019_q4 path: es/2019_q4_* - split: 2009_q3 path: es/2009_q3_* - split: 2020_q3 path: es/2020_q3_* - split: 2021_q4 path: es/2021_q4_* - split: 2010_q1 path: es/2010_q1_* - split: 2011_q2 path: es/2011_q2_* - split: 2022_q2 path: es/2022_q2_* - split: 2005_q1 path: es/2005_q1_* - split: 2016_q1 path: es/2016_q1_* - split: 2010_q3 path: es/2010_q3_* - split: 2013_q2 path: es/2013_q2_* - split: 2014_q3 path: es/2014_q3_* - split: 2011_q4 path: es/2011_q4_* - split: 2022_q4 path: es/2022_q4_* - split: 2005_q3 path: es/2005_q3_* - split: 2016_q3 path: es/2016_q3_* - split: 2013_q4 path: es/2013_q4_* - split: 2019_q1 path: es/2019_q1_* - split: 2006_q1 path: es/2006_q1_* - split: 2004_q1 path: es/2004_q1_* - split: 2007_q2 path: es/2007_q2_* - split: 2017_q4 path: es/2017_q4_* - split: 2008_q1 path: es/2008_q1_* - split: 2018_q2 path: es/2018_q2_* - split: 2012_q1 path: es/2012_q1_* - split: 2023_q1 path: es/2023_q1_* - split: 2006_q3 path: es/2006_q3_* - split: 2009_q2 path: es/2009_q2_* - split: 2020_q2 path: es/2020_q2_* - split: 2007_q4 path: es/2007_q4_* - split: 2018_q4 path: es/2018_q4_* - split: 2008_q3 path: es/2008_q3_* - split: 2012_q3 path: es/2012_q3_* - split: 2023_q3 path: es/2023_q3_* - split: 2009_q4 path: es/2009_q4_* - split: 2020_q4 path: es/2020_q4_* - split: 2010_q2 path: es/2010_q2_* - split: 2014_q2 path: es/2014_q2_* - split: 2015_q1 path: es/2015_q1_* - config_name: fr data_files: - split: 2005_q1_01 path: fr/2005_q1_01-* - split: 2005_q1_02 path: fr/2005_q1_02-* - split: 2005_q1_03 path: fr/2005_q1_03-* - split: 2005_q2_04 path: fr/2005_q2_04-* - split: 2005_q2_05 path: fr/2005_q2_05-* - split: 2005_q2_06 path: fr/2005_q2_06-* - split: 2005_q3_07 path: fr/2005_q3_07-* - split: 2005_q3_08 path: fr/2005_q3_08-* - split: 2005_q3_09 path: fr/2005_q3_09-* - split: 2005_q4_10 path: fr/2005_q4_10-* - split: 2005_q4_11 path: fr/2005_q4_11-* - split: 2005_q4_12 path: fr/2005_q4_12-* - split: 2006_q1_01 path: fr/2006_q1_01-* - split: 2006_q1_02 path: fr/2006_q1_02-* - split: 2006_q1_03 path: fr/2006_q1_03-* - split: 2006_q2_04 path: fr/2006_q2_04-* - split: 2006_q2_05 path: fr/2006_q2_05-* - split: 2006_q2_06 path: fr/2006_q2_06-* - split: 2006_q3_07 path: fr/2006_q3_07-* - split: 2006_q3_08 path: fr/2006_q3_08-* - split: 2006_q3_09 path: fr/2006_q3_09-* - split: 2006_q4_10 path: fr/2006_q4_10-* - split: 2006_q4_11 path: fr/2006_q4_11-* - split: 2006_q4_12 path: fr/2006_q4_12-* - split: 2007_q1_01 path: fr/2007_q1_01-* - split: 2007_q1_02 path: fr/2007_q1_02-* - split: 2007_q1_03 path: fr/2007_q1_03-* - split: 2007_q2_04 path: fr/2007_q2_04-* - split: 2007_q2_05 path: fr/2007_q2_05-* - split: no_date path: fr/no_date-* - split: 2007_q2_06 path: fr/2007_q2_06-* - split: 2007_q3_07 path: fr/2007_q3_07-* - split: 2007_q3_08 path: fr/2007_q3_08-* - split: 2007_q3_09 path: fr/2007_q3_09-* - split: 2007_q4_10 path: fr/2007_q4_10-* - split: 2007_q4_11 path: fr/2007_q4_11-* - split: 2007_q4_12 path: fr/2007_q4_12-* - split: 2008_q1_01 path: fr/2008_q1_01-* - split: 2008_q1_02 path: fr/2008_q1_02-* - split: 2008_q1_03 path: fr/2008_q1_03-* - split: 2008_q2_04 path: fr/2008_q2_04-* - split: 2008_q2_05 path: fr/2008_q2_05-* - split: 2008_q2_06 path: fr/2008_q2_06-* - split: 2008_q3_07 path: fr/2008_q3_07-* - split: 2008_q3_08 path: fr/2008_q3_08-* - split: 2008_q3_09 path: fr/2008_q3_09-* - split: 2008_q4_10 path: fr/2008_q4_10-* - split: 2008_q4_11 path: fr/2008_q4_11-* - split: 2008_q4_12 path: fr/2008_q4_12-* - split: 2009_q1_01 path: fr/2009_q1_01-* - split: 2009_q1_02 path: fr/2009_q1_02-* - split: 2009_q1_03 path: fr/2009_q1_03-* - split: 2009_q2_04 path: fr/2009_q2_04-* - split: 2009_q2_05 path: fr/2009_q2_05-* - split: 2009_q2_06 path: fr/2009_q2_06-* - split: 2009_q3_07 path: fr/2009_q3_07-* - split: 2009_q3_08 path: fr/2009_q3_08-* - split: 2009_q3_09 path: fr/2009_q3_09-* - split: 2011_q2_04 path: fr/2011_q2_04-* - split: 2009_q4_10 path: fr/2009_q4_10-* - split: 2009_q4_11 path: fr/2009_q4_11-* - split: 2009_q4_12 path: fr/2009_q4_12-* - split: 2010_q1_01 path: fr/2010_q1_01-* - split: 2010_q1_02 path: fr/2010_q1_02-* - split: 2010_q1_03 path: fr/2010_q1_03-* - split: 2010_q2_04 path: fr/2010_q2_04-* - split: 2010_q2_05 path: fr/2010_q2_05-* - split: 2010_q2_06 path: fr/2010_q2_06-* - split: 2010_q3_07 path: fr/2010_q3_07-* - split: 2010_q3_08 path: fr/2010_q3_08-* - split: 2010_q3_09 path: fr/2010_q3_09-* - split: 2010_q4_10 path: fr/2010_q4_10-* - split: 2010_q4_11 path: fr/2010_q4_11-* - split: 2010_q4_12 path: fr/2010_q4_12-* - split: 2011_q1_01 path: fr/2011_q1_01-* - split: 2011_q1_02 path: fr/2011_q1_02-* - split: 2011_q1_03 path: fr/2011_q1_03-* - split: 2011_q2_05 path: fr/2011_q2_05-* - split: 2011_q2_06 path: fr/2011_q2_06-* - split: 2011_q3_07 path: fr/2011_q3_07-* - split: 2011_q3_08 path: fr/2011_q3_08-* - split: 2011_q3_09 path: fr/2011_q3_09-* - split: 2011_q4_10 path: fr/2011_q4_10-* - split: 2011_q4_11 path: fr/2011_q4_11-* - split: 2011_q4_12 path: fr/2011_q4_12-* - split: 2012_q1_01 path: fr/2012_q1_01-* - split: 2012_q1_02 path: fr/2012_q1_02-* - split: 2012_q1_03 path: fr/2012_q1_03-* - split: 2012_q2_04 path: fr/2012_q2_04-* - split: 2012_q2_05 path: fr/2012_q2_05-* - split: 2012_q2_06 path: fr/2012_q2_06-* - split: 2012_q3_07 path: fr/2012_q3_07-* - split: 2012_q3_08 path: fr/2012_q3_08-* - split: 2024_q2_04 path: fr/2024_q2_04-* - split: 2012_q3_09 path: fr/2012_q3_09-* - split: 2012_q4_10 path: fr/2012_q4_10-* - split: 2012_q4_11 path: fr/2012_q4_11-* - split: 2012_q4_12 path: fr/2012_q4_12-* - split: 2013_q1_01 path: fr/2013_q1_01-* - split: 2013_q1_02 path: fr/2013_q1_02-* - split: 2013_q1_03 path: fr/2013_q1_03-* - split: 2013_q2_04 path: fr/2013_q2_04-* - split: 2013_q2_05 path: fr/2013_q2_05-* - split: 2013_q2_06 path: fr/2013_q2_06-* - split: 2013_q3_07 path: fr/2013_q3_07-* - split: 2013_q3_08 path: fr/2013_q3_08-* - split: 2013_q3_09 path: fr/2013_q3_09-* - split: 2013_q4_10 path: fr/2013_q4_10-* - split: 2013_q4_11 path: fr/2013_q4_11-* - split: 2013_q4_12 path: fr/2013_q4_12-* - split: 2014_q1_01 path: fr/2014_q1_01-* - split: 2014_q1_02 path: fr/2014_q1_02-* - split: 2014_q1_03 path: fr/2014_q1_03-* - split: 2024_q1_02 path: fr/2024_q1_02-* - split: 2014_q2_04 path: fr/2014_q2_04-* - split: 2014_q2_05 path: fr/2014_q2_05-* - split: 2014_q2_06 path: fr/2014_q2_06-* - split: 2014_q3_07 path: fr/2014_q3_07-* - split: 2014_q3_08 path: fr/2014_q3_08-* - split: 2014_q3_09 path: fr/2014_q3_09-* - split: 2014_q4_10 path: fr/2014_q4_10-* - split: 2014_q4_11 path: fr/2014_q4_11-* - split: 2014_q4_12 path: fr/2014_q4_12-* - split: 2015_q1_01 path: fr/2015_q1_01-* - split: 2015_q1_02 path: fr/2015_q1_02-* - split: 2015_q1_03 path: fr/2015_q1_03-* - split: 2015_q3_09 path: fr/2015_q3_09-* - split: 2015_q2_04 path: fr/2015_q2_04-* - split: 2015_q2_05 path: fr/2015_q2_05-* - split: 2015_q2_06 path: fr/2015_q2_06-* - split: 2016_q3_08 path: fr/2016_q3_08-* - split: 2015_q3_07 path: fr/2015_q3_07-* - split: 2015_q3_08 path: fr/2015_q3_08-* - split: 2015_q4_10 path: fr/2015_q4_10-* - split: 2015_q4_11 path: fr/2015_q4_11-* - split: 2015_q4_12 path: fr/2015_q4_12-* - split: 2016_q1_01 path: fr/2016_q1_01-* - split: 2016_q1_02 path: fr/2016_q1_02-* - split: 2016_q1_03 path: fr/2016_q1_03-* - split: 2016_q2_04 path: fr/2016_q2_04-* - split: 2016_q2_05 path: fr/2016_q2_05-* - split: 2016_q2_06 path: fr/2016_q2_06-* - split: 2016_q3_07 path: fr/2016_q3_07-* - split: 2016_q3_09 path: fr/2016_q3_09-* - split: 2020_q1_01 path: fr/2020_q1_01-* - split: 2016_q4_10 path: fr/2016_q4_10-* - split: 2016_q4_11 path: fr/2016_q4_11-* - split: 2016_q4_12 path: fr/2016_q4_12-* - split: 2017_q1_01 path: fr/2017_q1_01-* - split: 2017_q1_02 path: fr/2017_q1_02-* - split: 2017_q1_03 path: fr/2017_q1_03-* - split: 2017_q2_04 path: fr/2017_q2_04-* - split: 2017_q2_05 path: fr/2017_q2_05-* - split: 2017_q2_06 path: fr/2017_q2_06-* - split: 2017_q3_07 path: fr/2017_q3_07-* - split: 2017_q3_08 path: fr/2017_q3_08-* - split: 2017_q3_09 path: fr/2017_q3_09-* - split: 2017_q4_10 path: fr/2017_q4_10-* - split: 2017_q4_11 path: fr/2017_q4_11-* - split: 2017_q4_12 path: fr/2017_q4_12-* - split: 2018_q1_01 path: fr/2018_q1_01-* - split: 2018_q1_02 path: fr/2018_q1_02-* - split: 2018_q1_03 path: fr/2018_q1_03-* - split: 2018_q2_04 path: fr/2018_q2_04-* - split: 2018_q2_05 path: fr/2018_q2_05-* - split: 2018_q2_06 path: fr/2018_q2_06-* - split: 2018_q3_07 path: fr/2018_q3_07-* - split: 2018_q3_08 path: fr/2018_q3_08-* - split: 2018_q3_09 path: fr/2018_q3_09-* - split: 2018_q4_10 path: fr/2018_q4_10-* - split: 2018_q4_11 path: fr/2018_q4_11-* - split: 2018_q4_12 path: fr/2018_q4_12-* - split: 2019_q1_01 path: fr/2019_q1_01-* - split: 2019_q1_02 path: fr/2019_q1_02-* - split: 2019_q1_03 path: fr/2019_q1_03-* - split: 2019_q2_04 path: fr/2019_q2_04-* - split: 2019_q2_05 path: fr/2019_q2_05-* - split: 2019_q2_06 path: fr/2019_q2_06-* - split: 2019_q3_07 path: fr/2019_q3_07-* - split: 2019_q3_08 path: fr/2019_q3_08-* - split: 2019_q3_09 path: fr/2019_q3_09-* - split: 2019_q4_10 path: fr/2019_q4_10-* - split: 2019_q4_11 path: fr/2019_q4_11-* - split: 2019_q4_12 path: fr/2019_q4_12-* - split: 2020_q1_02 path: fr/2020_q1_02-* - split: 2020_q1_03 path: fr/2020_q1_03-* - split: 2020_q3_09 path: fr/2020_q3_09-* - split: 2020_q2_04 path: fr/2020_q2_04-* - split: 2020_q2_05 path: fr/2020_q2_05-* - split: 2020_q2_06 path: fr/2020_q2_06-* - split: 2020_q3_07 path: fr/2020_q3_07-* - split: 2020_q3_08 path: fr/2020_q3_08-* - split: 2020_q4_10 path: fr/2020_q4_10-* - split: 2020_q4_11 path: fr/2020_q4_11-* - split: 2020_q4_12 path: fr/2020_q4_12-* - split: 2021_q1_01 path: fr/2021_q1_01-* - split: 2021_q1_02 path: fr/2021_q1_02-* - split: 2021_q1_03 path: fr/2021_q1_03-* - split: 2021_q2_04 path: fr/2021_q2_04-* - split: 2021_q2_05 path: fr/2021_q2_05-* - split: 2021_q2_06 path: fr/2021_q2_06-* - split: 2021_q3_07 path: fr/2021_q3_07-* - split: 2021_q3_08 path: fr/2021_q3_08-* - split: 2021_q3_09 path: fr/2021_q3_09-* - split: 2021_q4_10 path: fr/2021_q4_10-* - split: 2021_q4_11 path: fr/2021_q4_11-* - split: 2021_q4_12 path: fr/2021_q4_12-* - split: 2022_q1_01 path: fr/2022_q1_01-* - split: 2022_q1_02 path: fr/2022_q1_02-* - split: 2022_q1_03 path: fr/2022_q1_03-* - split: 2022_q2_05 path: fr/2022_q2_05-* - split: 2022_q2_04 path: fr/2022_q2_04-* - split: 2022_q2_06 path: fr/2022_q2_06-* - split: 2022_q3_07 path: fr/2022_q3_07-* - split: 2022_q3_08 path: fr/2022_q3_08-* - split: 2022_q3_09 path: fr/2022_q3_09-* - split: 2022_q4_12 path: fr/2022_q4_12-* - split: 2022_q4_10 path: fr/2022_q4_10-* - split: 2022_q4_11 path: fr/2022_q4_11-* - split: 2023_q1_01 path: fr/2023_q1_01-* - split: 2023_q1_03 path: fr/2023_q1_03-* - split: 2023_q1_02 path: fr/2023_q1_02-* - split: 2023_q2_04 path: fr/2023_q2_04-* - split: 2023_q2_05 path: fr/2023_q2_05-* - split: 2023_q2_06 path: fr/2023_q2_06-* - split: 2023_q3_07 path: fr/2023_q3_07-* - split: 2023_q3_08 path: fr/2023_q3_08-* - split: 2023_q3_09 path: fr/2023_q3_09-* - split: 2023_q4_10 path: fr/2023_q4_10-* - split: 2023_q4_11 path: fr/2023_q4_11-* - split: 2023_q4_12 path: fr/2023_q4_12-* - split: 2024_q1_01 path: fr/2024_q1_01-* - split: '2005' path: fr/2005_* - split: '2006' path: fr/2006_* - split: '2007' path: fr/2007_* - split: '2008' path: fr/2008_* - split: '2009' path: fr/2009_* - split: '2010' path: fr/2010_* - split: '2011' path: fr/2011_* - split: '2012' path: fr/2012_* - split: '2013' path: fr/2013_* - split: '2014' path: fr/2014_* - split: '2015' path: fr/2015_* - split: '2016' path: fr/2016_* - split: '2017' path: fr/2017_* - split: '2018' path: fr/2018_* - split: '2019' path: fr/2019_* - split: '2020' path: fr/2020_* - split: '2021' path: fr/2021_* - split: '2022' path: fr/2022_* - split: '2023' path: fr/2023_* - split: '2024' path: fr/2024_* - split: 2005_q2 path: fr/2005_q2_* - split: 2016_q2 path: fr/2016_q2_* - split: 2017_q1 path: fr/2017_q1_* - split: 2010_q4 path: fr/2010_q4_* - split: 2021_q1 path: fr/2021_q1_* - split: 2014_q4 path: fr/2014_q4_* - split: 2015_q3 path: fr/2015_q3_* - split: 2019_q3 path: fr/2019_q3_* - split: 2005_q4 path: fr/2005_q4_* - split: 2016_q4 path: fr/2016_q4_* - split: 2017_q3 path: fr/2017_q3_* - split: 2021_q3 path: fr/2021_q3_* - split: 2006_q2 path: fr/2006_q2_* - split: 2024_q1 path: fr/2024_q1_* - split: 2011_q1 path: fr/2011_q1_* - split: 2022_q1 path: fr/2022_q1_* - split: 2008_q2 path: fr/2008_q2_* - split: 2012_q2 path: fr/2012_q2_* - split: 2023_q2 path: fr/2023_q2_* - split: 2013_q1 path: fr/2013_q1_* - split: 2006_q4 path: fr/2006_q4_* - split: 2011_q3 path: fr/2011_q3_* - split: 2022_q3 path: fr/2022_q3_* - split: 2008_q4 path: fr/2008_q4_* - split: 2012_q4 path: fr/2012_q4_* - split: 2014_q1 path: fr/2014_q1_* - split: 2013_q3 path: fr/2013_q3_* - split: 2023_q4 path: fr/2023_q4_* - split: 2007_q1 path: fr/2007_q1_* - split: 2018_q1 path: fr/2018_q1_* - split: 2015_q2 path: fr/2015_q2_* - split: 2019_q2 path: fr/2019_q2_* - split: 2009_q1 path: fr/2009_q1_* - split: 2020_q1 path: fr/2020_q1_* - split: 2017_q2 path: fr/2017_q2_* - split: 2007_q3 path: fr/2007_q3_* - split: 2018_q3 path: fr/2018_q3_* - split: 2021_q2 path: fr/2021_q2_* - split: 2015_q4 path: fr/2015_q4_* - split: 2019_q4 path: fr/2019_q4_* - split: 2009_q3 path: fr/2009_q3_* - split: 2020_q3 path: fr/2020_q3_* - split: 2021_q4 path: fr/2021_q4_* - split: 2010_q1 path: fr/2010_q1_* - split: 2011_q2 path: fr/2011_q2_* - split: 2022_q2 path: fr/2022_q2_* - split: 2005_q1 path: fr/2005_q1_* - split: 2016_q1 path: fr/2016_q1_* - split: 2010_q3 path: fr/2010_q3_* - split: 2013_q2 path: fr/2013_q2_* - split: 2014_q3 path: fr/2014_q3_* - split: 2011_q4 path: fr/2011_q4_* - split: 2022_q4 path: fr/2022_q4_* - split: 2005_q3 path: fr/2005_q3_* - split: 2016_q3 path: fr/2016_q3_* - split: 2013_q4 path: fr/2013_q4_* - split: 2019_q1 path: fr/2019_q1_* - split: 2006_q1 path: fr/2006_q1_* - split: 2007_q2 path: fr/2007_q2_* - split: 2017_q4 path: fr/2017_q4_* - split: 2008_q1 path: fr/2008_q1_* - split: 2018_q2 path: fr/2018_q2_* - split: 2012_q1 path: fr/2012_q1_* - split: 2023_q1 path: fr/2023_q1_* - split: 2006_q3 path: fr/2006_q3_* - split: 2009_q2 path: fr/2009_q2_* - split: 2020_q2 path: fr/2020_q2_* - split: 2024_q2 path: fr/2024_q2_* - split: 2007_q4 path: fr/2007_q4_* - split: 2018_q4 path: fr/2018_q4_* - split: 2008_q3 path: fr/2008_q3_* - split: 2012_q3 path: fr/2012_q3_* - split: 2023_q3 path: fr/2023_q3_* - split: 2009_q4 path: fr/2009_q4_* - split: 2020_q4 path: fr/2020_q4_* - split: 2010_q2 path: fr/2010_q2_* - split: 2014_q2 path: fr/2014_q2_* - split: 2015_q1 path: fr/2015_q1_* - config_name: it data_files: - split: 2005_q1_03 path: it/2005_q1_03-* - split: 2005_q2_04 path: it/2005_q2_04-* - split: no_date path: it/no_date-* - split: 2005_q2_05 path: it/2005_q2_05-* - split: 2006_q1_02 path: it/2006_q1_02-* - split: 2005_q2_06 path: it/2005_q2_06-* - split: 2005_q3_07 path: it/2005_q3_07-* - split: 2005_q3_09 path: it/2005_q3_09-* - split: 2005_q4_10 path: it/2005_q4_10-* - split: 2005_q3_08 path: it/2005_q3_08-* - split: 2005_q4_11 path: it/2005_q4_11-* - split: 2005_q4_12 path: it/2005_q4_12-* - split: 2006_q1_01 path: it/2006_q1_01-* - split: 2006_q2_04 path: it/2006_q2_04-* - split: 2006_q2_05 path: it/2006_q2_05-* - split: 2005_q1_01 path: it/2005_q1_01-* - split: 2006_q1_03 path: it/2006_q1_03-* - split: 2007_q3_08 path: it/2007_q3_08-* - split: 2007_q4_10 path: it/2007_q4_10-* - split: 2006_q2_06 path: it/2006_q2_06-* - split: 2006_q3_09 path: it/2006_q3_09-* - split: 2006_q4_10 path: it/2006_q4_10-* - split: 2006_q4_11 path: it/2006_q4_11-* - split: 2006_q4_12 path: it/2006_q4_12-* - split: 2007_q1_01 path: it/2007_q1_01-* - split: 2007_q1_02 path: it/2007_q1_02-* - split: 2007_q1_03 path: it/2007_q1_03-* - split: 2007_q2_04 path: it/2007_q2_04-* - split: 2006_q3_07 path: it/2006_q3_07-* - split: 2007_q2_05 path: it/2007_q2_05-* - split: 2007_q2_06 path: it/2007_q2_06-* - split: 2006_q3_08 path: it/2006_q3_08-* - split: 2007_q3_07 path: it/2007_q3_07-* - split: 2007_q3_09 path: it/2007_q3_09-* - split: 2008_q1_03 path: it/2008_q1_03-* - split: 2008_q1_02 path: it/2008_q1_02-* - split: 2007_q4_11 path: it/2007_q4_11-* - split: 2007_q4_12 path: it/2007_q4_12-* - split: 2008_q1_01 path: it/2008_q1_01-* - split: 2008_q2_06 path: it/2008_q2_06-* - split: 2008_q2_04 path: it/2008_q2_04-* - split: 2008_q2_05 path: it/2008_q2_05-* - split: 2008_q3_07 path: it/2008_q3_07-* - split: 2009_q3_08 path: it/2009_q3_08-* - split: 2008_q3_08 path: it/2008_q3_08-* - split: 2008_q3_09 path: it/2008_q3_09-* - split: 2008_q4_10 path: it/2008_q4_10-* - split: 2008_q4_11 path: it/2008_q4_11-* - split: 2008_q4_12 path: it/2008_q4_12-* - split: 2009_q1_01 path: it/2009_q1_01-* - split: 2009_q1_03 path: it/2009_q1_03-* - split: 2009_q1_02 path: it/2009_q1_02-* - split: 2012_q3_08 path: it/2012_q3_08-* - split: 2009_q2_04 path: it/2009_q2_04-* - split: 2009_q2_05 path: it/2009_q2_05-* - split: 2009_q2_06 path: it/2009_q2_06-* - split: 2009_q3_07 path: it/2009_q3_07-* - split: 2009_q3_09 path: it/2009_q3_09-* - split: 2009_q4_12 path: it/2009_q4_12-* - split: 2009_q4_10 path: it/2009_q4_10-* - split: 2009_q4_11 path: it/2009_q4_11-* - split: 2010_q1_01 path: it/2010_q1_01-* - split: 2010_q1_02 path: it/2010_q1_02-* - split: 2010_q1_03 path: it/2010_q1_03-* - split: 2010_q2_04 path: it/2010_q2_04-* - split: 2010_q2_05 path: it/2010_q2_05-* - split: 2010_q2_06 path: it/2010_q2_06-* - split: 2010_q3_07 path: it/2010_q3_07-* - split: 2010_q3_08 path: it/2010_q3_08-* - split: 2010_q3_09 path: it/2010_q3_09-* - split: 2010_q4_10 path: it/2010_q4_10-* - split: 2010_q4_11 path: it/2010_q4_11-* - split: 2010_q4_12 path: it/2010_q4_12-* - split: 2011_q1_01 path: it/2011_q1_01-* - split: 2011_q1_02 path: it/2011_q1_02-* - split: 2011_q1_03 path: it/2011_q1_03-* - split: 2011_q2_04 path: it/2011_q2_04-* - split: 2011_q2_05 path: it/2011_q2_05-* - split: 2011_q2_06 path: it/2011_q2_06-* - split: 2011_q3_07 path: it/2011_q3_07-* - split: 2011_q3_08 path: it/2011_q3_08-* - split: 2011_q3_09 path: it/2011_q3_09-* - split: 2011_q4_10 path: it/2011_q4_10-* - split: 2011_q4_11 path: it/2011_q4_11-* - split: 2011_q4_12 path: it/2011_q4_12-* - split: 2012_q1_01 path: it/2012_q1_01-* - split: 2012_q1_02 path: it/2012_q1_02-* - split: 2012_q1_03 path: it/2012_q1_03-* - 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split: 2021_q4 path: it/2021_q4_* - split: 2010_q1 path: it/2010_q1_* - split: 2011_q2 path: it/2011_q2_* - split: 2022_q2 path: it/2022_q2_* - split: 2005_q1 path: it/2005_q1_* - split: 2016_q1 path: it/2016_q1_* - split: 2010_q3 path: it/2010_q3_* - split: 2013_q2 path: it/2013_q2_* - split: 2014_q3 path: it/2014_q3_* - split: 2011_q4 path: it/2011_q4_* - split: 2022_q4 path: it/2022_q4_* - split: 2005_q3 path: it/2005_q3_* - split: 2016_q3 path: it/2016_q3_* - split: 2013_q4 path: it/2013_q4_* - split: 2019_q1 path: it/2019_q1_* - split: 2006_q1 path: it/2006_q1_* - split: 2007_q2 path: it/2007_q2_* - split: 2017_q4 path: it/2017_q4_* - split: 2008_q1 path: it/2008_q1_* - split: 2018_q2 path: it/2018_q2_* - split: 2012_q1 path: it/2012_q1_* - split: 2023_q1 path: it/2023_q1_* - split: 2006_q3 path: it/2006_q3_* - split: 2009_q2 path: it/2009_q2_* - split: 2020_q2 path: it/2020_q2_* - split: 2007_q4 path: it/2007_q4_* - split: 2018_q4 path: it/2018_q4_* - split: 2008_q3 path: it/2008_q3_* - split: 2012_q3 path: it/2012_q3_* - split: 2023_q3 path: it/2023_q3_* - split: 2009_q4 path: it/2009_q4_* - split: 2020_q4 path: it/2020_q4_* - split: 2010_q2 path: it/2010_q2_* - split: 2014_q2 path: it/2014_q2_* - split: 2015_q1 path: it/2015_q1_* --- # Wikinews The dataset contains news articles from Wikinews in different languages. Each article is associated with metadata like title, url, and date. The articles grouped into data splits by the article month, quarter, and year (the date is one mentioned in the article text, changes might have been after, see revision timestamp). The dataset config name defines the language. ## Usage ```python from datasets import load_dataset # all English news from 2008 ds = load_dataset("malteos/wikinews", config_name="en", split="2008") # all German news from January 2017 ds = load_dataset("malteos/wikinews", config_name="de", split="2017_q1_01") ``` ## Languages - en - es - fr - it - de ## License All text created after September 25, 2005 available under the terms of the [Creative Commons Attribution 2.5 License](https://creativecommons.org/licenses/by/2.5/).

mask-distilled-one-sec-cv12/chunk_240

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1453185512 num_examples: 285386 download_size: 1484150414 dataset_size: 1453185512 --- # Dataset Card for "chunk_240" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

linges0103/datasetfortrain

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 42812 num_examples: 59 download_size: 22507 dataset_size: 42812 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "datasetfortrain" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

abdoutony207/en_ar_dt

--- license: other ---

lazaroq11/billqa

--- dataset_info: features: - name: text dtype: string - name: additional_info dtype: string splits: - name: train num_bytes: 240602641 num_examples: 9846 download_size: 9341153 dataset_size: 240602641 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "billqa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Technoculture/MedpromptCoT

--- dataset_info: features: - name: question dtype: string - name: options dtype: string - name: reasoning dtype: string - name: answer dtype: string splits: - name: train num_bytes: 506301 num_examples: 676 download_size: 287262 dataset_size: 506301 configs: - config_name: default data_files: - split: train path: data/train-* license: mit language: - en --- # Dataset Card for "MedpromptCoT" ### Model used: gpt-3.5-turbo ## Dataset Mixture used for generating CoT | Dataset Name | Original Size(Rows) | Rows used | |----------------------------------------------------|---------------|-------| | openlifescienceai/medmcqa | 183k | 0.5k | | GBaker/MedQA-USMLE-4-options | 10.2k | 0.5k | Total Size: 1k Size after selecting only correct CoT: 0.676k ## How to Run the Script: To rerun the script, follow these steps: 1. Environment Setup: - Ensure Python environment with required dependencies (specified in requirements.txt) is set up. - Install necessary libraries using pip install -r requirements.txt. 2. Arguments - Choose the desired language model using the --model argument. - If using OpenAI's models (gpt-3.5-turbo or gpt-4-turbo-preview), set the OPENAI_API_KEY environment variable. For using Together models, set the TOGETHER_API_KEY and TOGETHER_API_BASE environment variables. - Specify the LLM client(openai, together or huggingface) using the --llm_client_type argument. - Specify the dataset name via the --dataset argument. - Define the output file location via the --output_file argument. - Specify the Hugging Face dataset repository for uploading the new dataset conisting of the COTs using the --huggingface_repo argument. - Specify the checkpointing number using the --checkpointing_steps. - Specify the count of examples to be taken from the dataset using --count. Run the script using python generate_medprompt_cot.py [arguments]. ### For full details please go through the generate_medprompt_cot.py file.

tyzhu/squad_qa_baseline_v5_full_recite_ans_sent_last_permute_rerun

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 2996506.0 num_examples: 2385 - name: validation num_bytes: 395889 num_examples: 300 download_size: 842977 dataset_size: 3392395.0 --- # Dataset Card for "squad_qa_baseline_v5_full_recite_ans_sent_last_permute_rerun" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

distilled-one-sec-cv12-each-chunk-uniq/chunk_172

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1072367324.0 num_examples: 208957 download_size: 1089947138 dataset_size: 1072367324.0 --- # Dataset Card for "chunk_172" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_ChaoticNeutrals__Prima-LelantaclesV7-experimental-7b

--- pretty_name: Evaluation run of ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b](https://huggingface.co/ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ChaoticNeutrals__Prima-LelantaclesV7-experimental-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-14T07:48:27.172404](https://huggingface.co/datasets/open-llm-leaderboard/details_ChaoticNeutrals__Prima-LelantaclesV7-experimental-7b/blob/main/results_2024-03-14T07-48-27.172404.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\": 0.6495664452929546,\n\ \ \"acc_stderr\": 0.03218441389606728,\n \"acc_norm\": 0.6487168204006772,\n\ \ \"acc_norm_stderr\": 0.03286122397355617,\n \"mc1\": 0.5826193390452876,\n\ \ \"mc1_stderr\": 0.017262891063272164,\n \"mc2\": 0.7461832399602438,\n\ \ \"mc2_stderr\": 0.01424474030426709\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7081911262798635,\n \"acc_stderr\": 0.013284525292403511,\n\ \ \"acc_norm\": 0.7286689419795221,\n \"acc_norm_stderr\": 0.012993807727545796\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7162915753833897,\n\ \ \"acc_stderr\": 0.0044987571944934,\n \"acc_norm\": 0.8871738697470624,\n\ \ \"acc_norm_stderr\": 0.0031573355082588415\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7302631578947368,\n \"acc_stderr\": 0.03611780560284898,\n\ \ \"acc_norm\": 0.7302631578947368,\n \"acc_norm_stderr\": 0.03611780560284898\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\ \ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933712,\n\ \ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933712\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\ \ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\ \ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\ : 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\ : {\n \"acc\": 0.56,\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\"\ : 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n\ \ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-college_medicine|5\"\ : {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.0368122963339432,\n\ \ \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.0368122963339432\n\ \ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n\ \ \"acc_stderr\": 0.04835503696107224,\n \"acc_norm\": 0.38235294117647056,\n\ \ \"acc_norm_stderr\": 0.04835503696107224\n },\n \"harness|hendrycksTest-computer_security|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5659574468085107,\n\ \ \"acc_stderr\": 0.032400380867927465,\n \"acc_norm\": 0.5659574468085107,\n\ \ \"acc_norm_stderr\": 0.032400380867927465\n },\n \"harness|hendrycksTest-econometrics|5\"\ : {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.0470070803355104,\n\ \ \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.0470070803355104\n\ \ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\ : 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"\ acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.41005291005291006,\n \"acc_stderr\": 0.02533120243894443,\n \"\ acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.02533120243894443\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.49206349206349204,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\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\": 0.7838709677419354,\n\ \ \"acc_stderr\": 0.023415293433568525,\n \"acc_norm\": 0.7838709677419354,\n\ \ \"acc_norm_stderr\": 0.023415293433568525\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\ \ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\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\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\ \ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644237,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644237\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n\ \ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473086,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473086\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886793,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886793\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\ acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461763,\n \"\ acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461763\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n\ \ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\ : {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926924,\n\ \ \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926924\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\": 0.6905829596412556,\n\ \ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\ \ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\ \ \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.7592592592592593,\n\ \ \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\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\": 0.7572815533980582,\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\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \ \ \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8186462324393359,\n\ \ \"acc_stderr\": 0.013778693778464085,\n \"acc_norm\": 0.8186462324393359,\n\ \ \"acc_norm_stderr\": 0.013778693778464085\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.02394851290546836,\n\ \ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.02394851290546836\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4424581005586592,\n\ \ \"acc_stderr\": 0.016611393687268584,\n \"acc_norm\": 0.4424581005586592,\n\ \ \"acc_norm_stderr\": 0.016611393687268584\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n\ \ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\ \ \"acc_stderr\": 0.02549425935069491,\n \"acc_norm\": 0.7202572347266881,\n\ \ \"acc_norm_stderr\": 0.02549425935069491\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7345679012345679,\n \"acc_stderr\": 0.024569223600460842,\n\ \ \"acc_norm\": 0.7345679012345679,\n \"acc_norm_stderr\": 0.024569223600460842\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\": 0.46479791395045633,\n \"acc_stderr\": 0.012738547371303956,\n\ \ \"acc_norm\": 0.46479791395045633,\n \"acc_norm_stderr\": 0.012738547371303956\n\ \ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\ : 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462923,\n \"\ acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462923\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \ \ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128448,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128448\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.026508590656233268,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.026508590656233268\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5826193390452876,\n\ \ \"mc1_stderr\": 0.017262891063272164,\n \"mc2\": 0.7461832399602438,\n\ \ \"mc2_stderr\": 0.01424474030426709\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8476716653512234,\n \"acc_stderr\": 0.0100992082460656\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6929492039423806,\n \ \ \"acc_stderr\": 0.012705685723131712\n }\n}\n```" repo_url: https://huggingface.co/ChaoticNeutrals/Prima-LelantaclesV7-experimental-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_14T07_48_27.172404 path: - '**/details_harness|arc:challenge|25_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-14T07-48-27.172404.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|gsm8k|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hellaswag|10_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-14T07-48-27.172404.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-management|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-14T07-48-27.172404.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|truthfulqa:mc|0_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-14T07-48-27.172404.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_14T07_48_27.172404 path: - '**/details_harness|winogrande|5_2024-03-14T07-48-27.172404.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-14T07-48-27.172404.parquet' - config_name: results data_files: - split: 2024_03_14T07_48_27.172404 path: - results_2024-03-14T07-48-27.172404.parquet - split: latest path: - results_2024-03-14T07-48-27.172404.parquet --- # Dataset Card for Evaluation run of ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b  Dataset automatically created during the evaluation run of model [ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b](https://huggingface.co/ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_ChaoticNeutrals__Prima-LelantaclesV7-experimental-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-14T07:48:27.172404](https://huggingface.co/datasets/open-llm-leaderboard/details_ChaoticNeutrals__Prima-LelantaclesV7-experimental-7b/blob/main/results_2024-03-14T07-48-27.172404.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": 0.6495664452929546, "acc_stderr": 0.03218441389606728, "acc_norm": 0.6487168204006772, "acc_norm_stderr": 0.03286122397355617, "mc1": 0.5826193390452876, "mc1_stderr": 0.017262891063272164, "mc2": 0.7461832399602438, "mc2_stderr": 0.01424474030426709 }, "harness|arc:challenge|25": { "acc": 0.7081911262798635, "acc_stderr": 0.013284525292403511, "acc_norm": 0.7286689419795221, "acc_norm_stderr": 0.012993807727545796 }, "harness|hellaswag|10": { "acc": 0.7162915753833897, "acc_stderr": 0.0044987571944934, "acc_norm": 0.8871738697470624, "acc_norm_stderr": 0.0031573355082588415 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7302631578947368, "acc_stderr": 0.03611780560284898, "acc_norm": 0.7302631578947368, "acc_norm_stderr": 0.03611780560284898 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7094339622641509, "acc_stderr": 0.02794321998933712, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.02794321998933712 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7708333333333334, "acc_stderr": 0.03514697467862388, "acc_norm": 0.7708333333333334, "acc_norm_stderr": 0.03514697467862388 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.56, "acc_stderr": 0.049888765156985884, "acc_norm": 0.56, "acc_norm_stderr": 0.049888765156985884 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107224, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107224 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5659574468085107, "acc_stderr": 0.032400380867927465, "acc_norm": 0.5659574468085107, "acc_norm_stderr": 0.032400380867927465 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.0470070803355104, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.0470070803355104 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41005291005291006, "acc_stderr": 0.02533120243894443, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.02533120243894443 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.49206349206349204, "acc_stderr": 0.044715725362943486, "acc_norm": 0.49206349206349204, "acc_norm_stderr": 0.044715725362943486 }, "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": 0.7838709677419354, "acc_stderr": 0.023415293433568525, "acc_norm": 0.7838709677419354, "acc_norm_stderr": 0.023415293433568525 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "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": 0.7636363636363637, "acc_stderr": 0.03317505930009182, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009182 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8963730569948186, "acc_stderr": 0.021995311963644237, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.021995311963644237 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6641025641025641, "acc_stderr": 0.023946724741563973, "acc_norm": 0.6641025641025641, "acc_norm_stderr": 0.023946724741563973 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32592592592592595, "acc_stderr": 0.028578348365473086, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.028578348365473086 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886793, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886793 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242742, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242742 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8403669724770643, "acc_stderr": 0.015703498348461763, "acc_norm": 0.8403669724770643, "acc_norm_stderr": 0.015703498348461763 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5, "acc_stderr": 0.034099716973523674, "acc_norm": 0.5, "acc_norm_stderr": 0.034099716973523674 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8382352941176471, "acc_stderr": 0.025845017986926924, "acc_norm": 0.8382352941176471, "acc_norm_stderr": 0.025845017986926924 }, "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": 0.6905829596412556, "acc_stderr": 0.03102441174057221, "acc_norm": 0.6905829596412556, "acc_norm_stderr": 0.03102441174057221 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8015267175572519, "acc_stderr": 0.034981493854624714, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.034981493854624714 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228732, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228732 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7592592592592593, "acc_stderr": 0.04133119440243839, "acc_norm": 0.7592592592592593, "acc_norm_stderr": 0.04133119440243839 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "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": 0.7572815533980582, "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": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8186462324393359, "acc_stderr": 0.013778693778464085, "acc_norm": 0.8186462324393359, "acc_norm_stderr": 0.013778693778464085 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7283236994219653, "acc_stderr": 0.02394851290546836, "acc_norm": 0.7283236994219653, "acc_norm_stderr": 0.02394851290546836 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4424581005586592, "acc_stderr": 0.016611393687268584, "acc_norm": 0.4424581005586592, "acc_norm_stderr": 0.016611393687268584 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7202572347266881, "acc_stderr": 0.02549425935069491, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.02549425935069491 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7345679012345679, "acc_stderr": 0.024569223600460842, "acc_norm": 0.7345679012345679, "acc_norm_stderr": 0.024569223600460842 }, "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": 0.46479791395045633, "acc_stderr": 0.012738547371303956, "acc_norm": 0.46479791395045633, "acc_norm_stderr": 0.012738547371303956 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462923, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462923 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6748366013071896, "acc_stderr": 0.018950886770806315, "acc_norm": 0.6748366013071896, "acc_norm_stderr": 0.018950886770806315 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.726530612244898, "acc_stderr": 0.028535560337128448, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.028535560337128448 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8308457711442786, "acc_stderr": 0.026508590656233268, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.026508590656233268 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.5826193390452876, "mc1_stderr": 0.017262891063272164, "mc2": 0.7461832399602438, "mc2_stderr": 0.01424474030426709 }, "harness|winogrande|5": { "acc": 0.8476716653512234, "acc_stderr": 0.0100992082460656 }, "harness|gsm8k|5": { "acc": 0.6929492039423806, "acc_stderr": 0.012705685723131712 } } ``` ## 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]

Sunbird/salt

--- dataset_info: - config_name: multispeaker-ach features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 1789773755 num_examples: 4811 - name: dev num_bytes: 37429640 num_examples: 101 - name: test num_bytes: 36224395 num_examples: 96 download_size: 861112801 dataset_size: 1863427790 - config_name: multispeaker-eng features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 1490684144 num_examples: 4797 - name: dev num_bytes: 30879913 num_examples: 100 - name: test num_bytes: 32136197 num_examples: 96 download_size: 746376946 dataset_size: 1553700254 - config_name: multispeaker-lgg features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 2346309650 num_examples: 4768 - name: dev num_bytes: 49044863 num_examples: 101 - name: test num_bytes: 49347397 num_examples: 96 download_size: 1191834787 dataset_size: 2444701910 - config_name: multispeaker-lug features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 2000647332 num_examples: 5016 - name: dev num_bytes: 38741382 num_examples: 103 - name: test num_bytes: 39746716 num_examples: 97 download_size: 1010619540 dataset_size: 2079135430 - config_name: multispeaker-nyn features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 2097997736 num_examples: 4812 - name: dev num_bytes: 42040138 num_examples: 101 - name: test num_bytes: 45063129 num_examples: 96 download_size: 1426293640 dataset_size: 2185101003 - config_name: multispeaker-teo features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 1980187546 num_examples: 4839 - name: dev num_bytes: 38906909 num_examples: 99 - name: test num_bytes: 40474249 num_examples: 96 download_size: 992185148 dataset_size: 2059568704 - config_name: studio-acholi features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 1347658634 num_examples: 4801 - name: dev num_bytes: 27757030 num_examples: 101 - name: test num_bytes: 26447325 num_examples: 96 download_size: 698234854 dataset_size: 1401862989 - config_name: studio-ateso features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 2308097503 num_examples: 4564 - name: dev num_bytes: 49170958 num_examples: 96 - name: test num_bytes: 47400438 num_examples: 92 download_size: 977293946 dataset_size: 2404668899 - config_name: studio-english features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 851109381 num_examples: 2411 - name: dev num_bytes: 17784430 num_examples: 50 - name: test num_bytes: 15322757 num_examples: 42 download_size: 435775221 dataset_size: 884216568 - config_name: studio-luganda features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 880656730 num_examples: 2395 - name: dev num_bytes: 18853020 num_examples: 50 - name: test num_bytes: 16076901 num_examples: 43 download_size: 455441369 dataset_size: 915586651 - config_name: studio-runyankole features: - name: id dtype: int64 - name: text dtype: string - name: audio sequence: float32 - name: audio_language dtype: string - name: is_studio dtype: bool - name: speaker_id dtype: string - name: sample_rate dtype: int64 splits: - name: train num_bytes: 39234984 num_examples: 94 - name: dev num_bytes: 1666059 num_examples: 4 - name: test num_bytes: 947547 num_examples: 2 download_size: 20592402 dataset_size: 41848590 - config_name: text-all features: - name: id dtype: int64 - name: teo_text dtype: string - name: swa_text dtype: string - name: eng_text dtype: string - name: nyn_text dtype: string - name: ibo_text dtype: string - name: ach_text dtype: string - name: lgg_text dtype: string - name: lug_text dtype: string splits: - name: train num_bytes: 11763775 num_examples: 23947 - name: dev num_bytes: 242587 num_examples: 496 - name: test num_bytes: 253968 num_examples: 500 download_size: 7228279 dataset_size: 12260330 configs: - config_name: multispeaker-ach data_files: - split: train path: multispeaker-ach/train-* - split: dev path: multispeaker-ach/dev-* - split: test path: multispeaker-ach/test-* - config_name: multispeaker-eng data_files: - split: train path: multispeaker-eng/train-* - split: dev path: multispeaker-eng/dev-* - split: test path: multispeaker-eng/test-* - config_name: multispeaker-lgg data_files: - split: train path: multispeaker-lgg/train-* - split: dev path: multispeaker-lgg/dev-* - split: test path: multispeaker-lgg/test-* - config_name: multispeaker-lug data_files: - split: train path: multispeaker-lug/train-* - split: dev path: multispeaker-lug/dev-* - split: test path: multispeaker-lug/test-* - config_name: multispeaker-nyn data_files: - split: train path: multispeaker-nyn/train-* - split: dev path: multispeaker-nyn/dev-* - split: test path: multispeaker-nyn/test-* - config_name: multispeaker-teo data_files: - split: train path: multispeaker-teo/train-* - split: dev path: multispeaker-teo/dev-* - split: test path: multispeaker-teo/test-* - config_name: studio-acholi data_files: - split: train path: studio-acholi/train-* - split: dev path: studio-acholi/dev-* - split: test path: studio-acholi/test-* - config_name: studio-ateso data_files: - split: train path: studio-ateso/train-* - split: dev path: studio-ateso/dev-* - split: test path: studio-ateso/test-* - config_name: studio-english data_files: - split: train path: studio-english/train-* - split: dev path: studio-english/dev-* - split: test path: studio-english/test-* - config_name: studio-luganda data_files: - split: train path: studio-luganda/train-* - split: dev path: studio-luganda/dev-* - split: test path: studio-luganda/test-* - config_name: studio-runyankole data_files: - split: train path: studio-runyankole/train-* - split: dev path: studio-runyankole/dev-* - split: test path: studio-runyankole/test-* - config_name: text-all data_files: - split: train path: text-all/train-* - split: dev path: text-all/dev-* - split: test path: text-all/test-* ---

felipesampaio/darwin2dataset

--- license: openrail ---

witchling22/tokenized_T5_base

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: validation path: data/validation-* dataset_info: features: - name: source dtype: string - name: source_labels dtype: string - name: rouge_scores dtype: string - name: paper_id dtype: string - name: target dtype: string - name: full_source_text dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 17340567 num_examples: 1992 - name: test num_bytes: 5620222 num_examples: 618 - name: validation num_bytes: 5534448 num_examples: 619 download_size: 6371599 dataset_size: 28495237 --- # Dataset Card for "tokenized_T5_base" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

NebulaSense/Legal_Clause_Instructions

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

wangxingjun778/test_123

--- license: apache-2.0 --- ## 1. ONLY FOR TEST ## 2. ONLY FOR TEST

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/629bee15

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 176 num_examples: 10 download_size: 1341 dataset_size: 176 --- # Dataset Card for "629bee15" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

AdapterOcean/code_instructions_standardized_cluster_8_std

--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 32500014 num_examples: 26748 download_size: 15520517 dataset_size: 32500014 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "code_instructions_standardized_cluster_8_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

EleutherAI/naturenet

--- dataset_info: features: - name: img dtype: image - name: label dtype: class_label: names: '0': amphibian '1': bird '2': dog '3': feline '4': fish '5': flower '6': horse '7': primate '8': rodent '9': snake splits: - name: train num_bytes: 2195586500.24 num_examples: 490000 - name: test num_bytes: 45820817.76 num_examples: 10000 download_size: 2188877286 dataset_size: 2241407318.0 --- # Dataset Card for "naturenet" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

wyx-ucl/SUM-DATASET-BASED-EDGAR-CORPUS

--- license: other ---

PJMixers/oasst2_dpo_metharme_english

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: lang dtype: string - name: parent_id dtype: string - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 32775306 num_examples: 12353 - name: validation num_bytes: 1717916 num_examples: 668 download_size: 18679121 dataset_size: 34493222 source_datasets: - OpenAssistant/oasst2 tags: - dpo - rlhf - human-feedback - reward - preference - pairwise - pair pretty_name: Open Assistant 2 DPO (Metharme) size_categories: - 10K<n<100K --- Similar to [monology/oasst2_dpo](https://huggingface.co/datasets/monology/oasst2_dpo), except this uses Metharme tags instead. Only samples marked english were kept.

datasets-examples/doc-image-6

--- size_categories: - n<1K --- # [doc] image dataset 6 This dataset contains 4 jpeg files in the `train/images/` subdirectory, along with a `train/metadata.csv` file that provides the data for other columns. The metadata file contains relative paths to the images.

open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla

--- pretty_name: Evaluation run of l3utterfly/open-llama-3b-v2-layla dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [l3utterfly/open-llama-3b-v2-layla](https://huggingface.co/l3utterfly/open-llama-3b-v2-layla)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T08:49:03.131155](https://huggingface.co/datasets/open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla/blob/main/results_2023-09-17T08-49-03.131155.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.011954697986577181,\n\ \ \"em_stderr\": 0.0011130056898859086,\n \"f1\": 0.07875629194630916,\n\ \ \"f1_stderr\": 0.0018920865515620476,\n \"acc\": 0.3194349118852447,\n\ \ \"acc_stderr\": 0.008202509803690292\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.011954697986577181,\n \"em_stderr\": 0.0011130056898859086,\n\ \ \"f1\": 0.07875629194630916,\n \"f1_stderr\": 0.0018920865515620476\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01061410159211524,\n \ \ \"acc_stderr\": 0.0028227133223877035\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6282557221783741,\n \"acc_stderr\": 0.013582306284992879\n\ \ }\n}\n```" repo_url: https://huggingface.co/l3utterfly/open-llama-3b-v2-layla 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_08_18T14_37_31.844402 path: - '**/details_harness|arc:challenge|25_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-18T14:37:31.844402.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T08_49_03.131155 path: - '**/details_harness|drop|3_2023-09-17T08-49-03.131155.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-17T08-49-03.131155.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T08_49_03.131155 path: - '**/details_harness|gsm8k|5_2023-09-17T08-49-03.131155.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-17T08-49-03.131155.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hellaswag|10_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-18T14:37:31.844402.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-management|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T14:37:31.844402.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_18T14_37_31.844402 path: - '**/details_harness|truthfulqa:mc|0_2023-08-18T14:37:31.844402.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-18T14:37:31.844402.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T08_49_03.131155 path: - '**/details_harness|winogrande|5_2023-09-17T08-49-03.131155.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-17T08-49-03.131155.parquet' - config_name: results data_files: - split: 2023_08_18T14_37_31.844402 path: - results_2023-08-18T14:37:31.844402.parquet - split: 2023_09_17T08_49_03.131155 path: - results_2023-09-17T08-49-03.131155.parquet - split: latest path: - results_2023-09-17T08-49-03.131155.parquet --- # Dataset Card for Evaluation run of l3utterfly/open-llama-3b-v2-layla ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/l3utterfly/open-llama-3b-v2-layla - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [l3utterfly/open-llama-3b-v2-layla](https://huggingface.co/l3utterfly/open-llama-3b-v2-layla) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T08:49:03.131155](https://huggingface.co/datasets/open-llm-leaderboard/details_l3utterfly__open-llama-3b-v2-layla/blob/main/results_2023-09-17T08-49-03.131155.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.011954697986577181, "em_stderr": 0.0011130056898859086, "f1": 0.07875629194630916, "f1_stderr": 0.0018920865515620476, "acc": 0.3194349118852447, "acc_stderr": 0.008202509803690292 }, "harness|drop|3": { "em": 0.011954697986577181, "em_stderr": 0.0011130056898859086, "f1": 0.07875629194630916, "f1_stderr": 0.0018920865515620476 }, "harness|gsm8k|5": { "acc": 0.01061410159211524, "acc_stderr": 0.0028227133223877035 }, "harness|winogrande|5": { "acc": 0.6282557221783741, "acc_stderr": 0.013582306284992879 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

sanchit-gandhi/librispeech_asr_dummy_pseudo_labelled

--- dataset_info: config_name: clean features: - name: file dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string - name: speaker_id dtype: int64 - name: chapter_id dtype: int64 - name: id dtype: string - name: whisper_transcript sequence: int64 splits: - name: validation num_bytes: 9700520.0 num_examples: 73 download_size: 9198584 dataset_size: 9700520.0 configs: - config_name: clean data_files: - split: validation path: clean/validation-* ---

adamjweintraut/kwsylgen

--- dataset_info: features: - name: midi_id dtype: string - name: song_title dtype: string - name: lyrics dtype: string - name: genre dtype: string - name: lyric_summary_bartv2 dtype: string - name: topic dtype: string - name: clean_lyrics dtype: string - name: lyric_chunk_n dtype: int64 - name: prev_clean_lyrics dtype: string - name: sylls dtype: int64 - name: keywords sequence: string - name: orig dtype: string - name: target dtype: string splits: - name: train num_bytes: 358889793.8653989 num_examples: 178354 - name: test num_bytes: 47312159.64738172 num_examples: 23126 - name: valid num_bytes: 47185445.65799565 num_examples: 23683 download_size: 21206048 dataset_size: 453387399.17077625 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---

EleutherAI/headqa

--- license: other ---

CyberHarem/shiina_mahiru_otonarinotenshisamaniitsunomanikadameningennisareteitaken

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Shiina Mahiru/椎名真昼 (Otonari no Tenshi-sama ni Itsunomanika Dame Ningen ni Sareteita Ken) This is the dataset of Shiina Mahiru/椎名真昼 (Otonari no Tenshi-sama ni Itsunomanika Dame Ningen ni Sareteita Ken), containing 463 images and their tags. The core tags of this character are `long_hair, blonde_hair, yellow_eyes, brown_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 463 | 399.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiina_mahiru_otonarinotenshisamaniitsunomanikadameningennisareteitaken/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 463 | 399.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiina_mahiru_otonarinotenshisamaniitsunomanikadameningennisareteitaken/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 830 | 656.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shiina_mahiru_otonarinotenshisamaniitsunomanikadameningennisareteitaken/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/shiina_mahiru_otonarinotenshisamaniitsunomanikadameningennisareteitaken', 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 | 11 | ![](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, open_mouth, portrait, solo, close-up, looking_at_viewer, :d, :o | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, closed_mouth, portrait, solo, blush, looking_at_viewer, smile, close-up | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, portrait, shirt, solo, blush, indoors, closed_mouth, collarbone, looking_at_viewer | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, collared_shirt, open_mouth, solo, white_shirt, blush, indoors, upper_body, curtains, portrait | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, closed_mouth, solo, blush, portrait, profile, white_shirt, collared_shirt, from_side | | 5 | 6 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, closed_mouth, collared_shirt, indoors, long_sleeves, looking_at_viewer, smile, solo, upper_body, white_shirt, dutch_angle, holding, apron, whisk | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, ponytail, portrait, sidelocks, solo, closed_mouth, shirt, smile, collarbone, anime_coloring, apron, grey_background, simple_background | | 7 | 7 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, indoors, ponytail, sidelocks, smile, solo, blush, collarbone, open_mouth, purple_shirt, very_long_hair, yellow_apron, long_sleeves, standing, white_skirt | | 8 | 9 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, sidelocks, solo, braid, collarbone, white_shirt, open_mouth, upper_body, blue_ribbon, blunt_bangs, single_hair_bun | | 9 | 24 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, school_uniform, white_shirt, collared_shirt, solo, blazer, red_bowtie, closed_mouth, upper_body, blue_jacket, blurry_background, looking_at_viewer, outdoors, blush | | 10 | 6 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | 1girl, hair_scrunchie, ponytail, sidelocks, blue_scrunchie, closed_mouth, solo, blush, indoors, pink_shirt, profile, upper_body | | 11 | 7 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | 1girl, solo_focus, upper_body, 2girls, closed_mouth, collared_shirt, looking_at_viewer, white_shirt, brown_hair, collarbone, blush, pajamas, simple_background, smile | | 12 | 5 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | 1girl, blazer, brown_skirt, long_sleeves, plaid_skirt, school_uniform, very_long_hair, 2boys, blue_jacket, shoes, smile, solo_focus, white_shirt, 1boy, head_out_of_frame, pants, pleated_skirt, red_bowtie | | 13 | 7 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-sample4.png) | 1girl, braid, cherry_blossoms, hair_bow, long_sleeves, looking_at_viewer, outdoors, smile, solo, upper_body, white_shirt, closed_mouth, petals, tree, from_side, looking_back, flower, very_long_hair, floating_hair | | 14 | 5 | ![](samples/14/clu14-sample0.png) | ![](samples/14/clu14-sample1.png) | ![](samples/14/clu14-sample2.png) | ![](samples/14/clu14-sample3.png) | ![](samples/14/clu14-sample4.png) | 1girl, blanket, pillow, under_covers, blush, closed_mouth, indoors, looking_at_viewer, on_bed, on_back, solo_focus | | 15 | 8 | ![](samples/15/clu15-sample0.png) | ![](samples/15/clu15-sample1.png) | ![](samples/15/clu15-sample2.png) | ![](samples/15/clu15-sample3.png) | ![](samples/15/clu15-sample4.png) | 1girl, pink_kimono, sidelocks, closed_mouth, hair_flower, blush, floral_print, looking_at_viewer, obi, upper_body, long_sleeves, solo_focus | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | open_mouth | portrait | solo | close-up | looking_at_viewer | :d | :o | closed_mouth | smile | shirt | indoors | collarbone | collared_shirt | white_shirt | upper_body | curtains | profile | from_side | long_sleeves | dutch_angle | holding | apron | whisk | ponytail | sidelocks | anime_coloring | grey_background | simple_background | purple_shirt | very_long_hair | yellow_apron | standing | white_skirt | braid | blue_ribbon | blunt_bangs | single_hair_bun | school_uniform | blazer | red_bowtie | blue_jacket | blurry_background | outdoors | hair_scrunchie | blue_scrunchie | pink_shirt | solo_focus | 2girls | brown_hair | pajamas | brown_skirt | plaid_skirt | 2boys | shoes | 1boy | head_out_of_frame | pants | pleated_skirt | cherry_blossoms | hair_bow | petals | tree | looking_back | flower | floating_hair | blanket | pillow | under_covers | on_bed | on_back | pink_kimono | hair_flower | floral_print | obi | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:-------------|:-----------|:-------|:-----------|:--------------------|:-----|:-----|:---------------|:--------|:--------|:----------|:-------------|:-----------------|:--------------|:-------------|:-----------|:----------|:------------|:---------------|:--------------|:----------|:--------|:--------|:-----------|:------------|:-----------------|:------------------|:--------------------|:---------------|:-----------------|:---------------|:-----------|:--------------|:--------|:--------------|:--------------|:------------------|:-----------------|:---------|:-------------|:--------------|:--------------------|:-----------|:-----------------|:-----------------|:-------------|:-------------|:---------|:-------------|:----------|:--------------|:--------------|:--------|:--------|:-------|:--------------------|:--------|:----------------|:------------------|:-----------|:---------|:-------|:---------------|:---------|:----------------|:----------|:---------|:---------------|:---------|:----------|:--------------|:--------------|:---------------|:------| | 0 | 11 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | X | X | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | X | X | | X | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | X | | | | | | | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | | X | X | | | | | X | | | | | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 6 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | | X | | X | | | X | X | | X | | X | X | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | X | X | | | | | X | X | X | | X | | | | | | | | | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 7 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | X | | X | | | | | | X | | X | X | | | | | | | X | | | | | X | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 9 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | X | | X | | | | | | | | | X | | X | X | | | | | | | | | | X | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 24 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | X | | | X | | X | | | X | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 6 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | X | X | | | X | | | | | X | | | X | | | | X | | X | | | | | | | X | X | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 11 | 7 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | X | X | | | | | X | | | X | X | | | X | X | X | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 12 | 5 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | X | | | | | | | | | | X | | | | | X | | | | | X | | | | | | | | | | | X | | | | | | | | X | X | X | X | | | | | | X | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | 13 | 7 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-sample4.png) | X | | | | X | | X | | | X | X | | | | | X | X | | | X | X | | | | | | | | | | | X | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | 14 | 5 | ![](samples/14/clu14-sample0.png) | ![](samples/14/clu14-sample1.png) | ![](samples/14/clu14-sample2.png) | ![](samples/14/clu14-sample3.png) | ![](samples/14/clu14-sample4.png) | X | X | | | | | X | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | 15 | 8 | ![](samples/15/clu15-sample0.png) | ![](samples/15/clu15-sample1.png) | ![](samples/15/clu15-sample2.png) | ![](samples/15/clu15-sample3.png) | ![](samples/15/clu15-sample4.png) | X | X | | | | | X | | | X | | | | | | | X | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X |

projecte-aina/vilaquad

--- annotations_creators: - expert-generated language_creators: - found language: - ca license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: VilaQuAD size_categories: - 1K<n<10K source_datasets: - original task_categories: - question-answering task_ids: - extractive-qa --- # Dataset Card for VilaQuAD ## 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://doi.org/10.5281/zenodo.4562337 - **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903) - **Point of Contact:** langtech@bsc.es ### Dataset Summary VilaQuAD, An extractive QA dataset for Catalan, from [VilaWeb](https://www.vilaweb.cat/) newswire text. This dataset contains 2095 of Catalan language news articles along with 1 to 5 questions referring to each fragment (or context). VilaQuad articles are extracted from the daily [VilaWeb](https://www.vilaweb.cat/) and used under [CC-BY-NC-SA-ND](https://creativecommons.org/licenses/by-nc-nd/3.0/deed.ca) licence. This dataset can be used to build extractive-QA and Language Models. ### Supported Tasks and Leaderboards Extractive-QA, Language Model. ### Languages The dataset is in Catalan (`ca-ES`). ## Dataset Structure ### Data Instances ``` { 'id': 'P_556_C_556_Q1', 'title': "El Macba posa en qüestió l'eufòria amnèsica dels anys vuitanta a l'estat espanyol", 'context': "El Macba ha obert una nova exposició, 'Gelatina dura. Històries escamotejades dels 80', dedicada a revisar el discurs hegemònic que es va instaurar en aquella dècada a l'estat espanyol, concretament des del començament de la transició, el 1977, fins a la fita de Barcelona 92. És una mirada en clau espanyola, però també centralista, perquè més enllà dels esdeveniments ocorreguts a Catalunya i els artistes que els van combatre, pràcticament només s'hi mostren fets polítics i culturals generats des de Madrid. No es parla del País Basc, per exemple. Però, dit això, l'exposició revisa aquesta dècada de la història recent tot qüestionant un triomfalisme homogeneïtzador, que ja se sap que va arrasar una gran quantitat de sectors crítics i radicals de l'àmbit social, polític i cultural. Com diu la comissària, Teresa Grandas, de l'equip del Macba: 'El relat oficial dels anys vuitanta a l'estat espanyol va prioritzar la necessitat per damunt de la raó i va consolidar una mirada que privilegiava el futur abans que l'anàlisi del passat recent, obviant qualsevol consideració crítica respecte de la filiació amb el poder franquista.", 'question': 'Com es diu la nova exposició que ha obert el Macba?', 'answers': [ { 'text': "'Gelatina dura. Històries escamotejades dels 80'", 'answer_start': 38 } ] } ``` ### Data Fields Follows [Rajpurkar, Pranav et al., (2016)](http://arxiv.org/abs/1606.05250) for SQuAD v1 datasets. - `id` (str): Unique ID assigned to the question. - `title` (str): Title of the VilaWeb article. - `context` (str): VilaWeb section text. - `question` (str): Question. - `answers` (list): List of answers to the question, each containing: - `text` (str): Span text answering to the question. - `answer_start` Starting offset of the span text answering to the question. ### Data Splits - train.json: 1295 contexts, 3882 questions - dev.json: 400 contexts, 1200 questions - test.json: 400 contexts, 1200 questions ## Dataset Creation ### Curation Rationale We created this dataset to contribute to the development of language models in Catalan, a low-resource language. ### Source Data - [VilaWeb site](https://www.vilaweb.cat/) #### Initial Data Collection and Normalization The source data are scraped articles from archives of Catalan newspaper website [Vilaweb](https://www.vilaweb.cat). From a the online edition of the newspaper [VilaWeb](https://www.vilaweb.cat), 2095 articles were randomnly selected. These headlines were also used to create a Textual Entailment dataset. For the extractive QA dataset, creation of between 1 and 5 questions for each news context was commissioned, following an adaptation of the guidelines from SQuAD 1.0 ([Rajpurkar, Pranav et al. (2016)](http://arxiv.org/abs/1606.05250)). In total, 6282 pairs of a question and an extracted fragment that contains the answer were created. For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines. We also created [another QA dataset with wikipedia](https://huggingface.co/datasets/projecte-aina/viquiquad) to ensure thematic and stylistic variety. #### Who are the source language producers? CA Professional journalists from the Catalan newspaper [VilaWeb](https://www.vilaweb.cat/). ### Annotations #### Annotation process We comissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQuAD 1.0 ([Rajpurkar, Pranav et al. (2016)](http://arxiv.org/abs/1606.05250)). #### Who are the annotators? Annotation was commissioned to an specialized company that hired a team of native language speakers. ### Personal and Sensitive Information No personal or sensitive information included. ## Considerations for Using the Data ### Social Impact of Dataset We hope this dataset contributes to the development of language models in Catalan, a low-resource language. ### Discussion of Biases [N/A] ### Other Known Limitations [N/A] ## Additional Information ### Dataset Curators Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es) This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/en/inici/index.html) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/). ### Licensing Information This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International License</a>. ### Citation Information ``` @inproceedings{armengol-estape-etal-2021-multilingual, title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan", author = "Armengol-Estap{\'e}, Jordi and Carrino, Casimiro Pio and Rodriguez-Penagos, Carlos and de Gibert Bonet, Ona and Armentano-Oller, Carme and Gonzalez-Agirre, Aitor and Melero, Maite and Villegas, Marta", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.437", doi = "10.18653/v1/2021.findings-acl.437", pages = "4933--4946", } ``` [DOI](https://doi.org/10.5281/zenodo.4562337) ### Contributions [N/A]

AdapterOcean/Open_Platypus_standardized_cluster_8_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 3469650 num_examples: 3068 download_size: 1775180 dataset_size: 3469650 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Open_Platypus_standardized_cluster_8_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pravsels/manim_3b1b_code

--- dataset_info: features: - name: file_path dtype: string - name: content dtype: string splits: - name: train num_bytes: 887937 num_examples: 123 download_size: 336530 dataset_size: 887937 configs: - config_name: default data_files: - split: train path: data/train-* ---

AsphyXIA/Baarat-Kan-Summarization

--- dataset_info: features: - name: input dtype: string - name: target dtype: string splits: - name: train num_bytes: 97489736 num_examples: 140890 download_size: 40315874 dataset_size: 97489736 configs: - config_name: default data_files: - split: train path: data/train-* ---