librarian-bots/dataset_cards_with_metadata_with_embeddings

datasetId stringlengths 2 117	author stringlengths 2 42 ⌀	last_modified timestamp[us, tz=UTC]	downloads int64 0 9.36M	likes int64 0 3.89k	tags sequence	task_categories sequence	createdAt timestamp[us, tz=UTC]	card stringlengths 19 1.01M	embedding sequence
open-llm-leaderboard/details_upstage__SOLAR-10.7B-Instruct-v1.0	open-llm-leaderboard	2023-12-13T21:06:09Z	9	0	[ "region:us" ]	null	2023-12-13T21:05:25Z	--- pretty_name: Evaluation run of upstage/SOLAR-10.7B-Instruct-v1.0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)\ \ 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_upstage__SOLAR-10.7B-Instruct-v1.0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-13T21:02:33.929144](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__SOLAR-10.7B-Instruct-v1.0/blob/main/results_2023-12-13T21-02-33.929144.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\": 0.6657586984797939,\n\ \ \"acc_stderr\": 0.03165995758526614,\n \"acc_norm\": 0.6666511531376961,\n\ \ \"acc_norm_stderr\": 0.0323050384069596,\n \"mc1\": 0.5667074663402693,\n\ \ \"mc1_stderr\": 0.017347024450107485,\n \"mc2\": 0.7142943510205136,\n\ \ \"mc2_stderr\": 0.015024530295000761\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6808873720136519,\n \"acc_stderr\": 0.013621696119173307,\n\ \ \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.01325001257939344\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7070304720175263,\n\ \ \"acc_stderr\": 0.004541944342035901,\n \"acc_norm\": 0.8815972913762199,\n\ \ \"acc_norm_stderr\": 0.003224240722351317\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness\|hendrycksTest-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\": 0.7368421052631579,\n \"acc_stderr\": 0.03583496176361072,\n\ \ \"acc_norm\": 0.7368421052631579,\n \"acc_norm_stderr\": 0.03583496176361072\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.74,\n\ \ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \ \ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.02872750295788027,\n\ \ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.02872750295788027\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\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.52,\n \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\": 0.52,\n\ \ \"acc_norm_stderr\": 0.05021167315686779\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6647398843930635,\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\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.6297872340425532,\n \"acc_stderr\": 0.03156564682236785,\n\ \ \"acc_norm\": 0.6297872340425532,\n \"acc_norm_stderr\": 0.03156564682236785\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6413793103448275,\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\"\ : 0.47883597883597884,\n \"acc_stderr\": 0.025728230952130726,\n \"\ acc_norm\": 0.47883597883597884,\n \"acc_norm_stderr\": 0.025728230952130726\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.8032258064516129,\n \"acc_stderr\": 0.022616409420742025,\n \"\ acc_norm\": 0.8032258064516129,\n \"acc_norm_stderr\": 0.022616409420742025\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5172413793103449,\n \"acc_stderr\": 0.03515895551165698,\n \"\ acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.03515895551165698\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\": 0.8,\n \"acc_stderr\": 0.031234752377721175,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721175\n \ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8737373737373737,\n \"acc_stderr\": 0.02366435940288023,\n \"\ acc_norm\": 0.8737373737373737,\n \"acc_norm_stderr\": 0.02366435940288023\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3814814814814815,\n \"acc_stderr\": 0.029616718927497593,\n \ \ \"acc_norm\": 0.3814814814814815,\n \"acc_norm_stderr\": 0.029616718927497593\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7184873949579832,\n \"acc_stderr\": 0.02921354941437217,\n \ \ \"acc_norm\": 0.7184873949579832,\n \"acc_norm_stderr\": 0.02921354941437217\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\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5555555555555556,\n \"acc_stderr\": 0.03388857118502325,\n \"\ acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.03388857118502325\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.0251956584289318,\n \"acc_norm\"\ : 0.8480392156862745,\n \"acc_norm_stderr\": 0.0251956584289318\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\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n\ \ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\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\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8589743589743589,\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\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8033205619412516,\n\ \ \"acc_stderr\": 0.014214138556913917,\n \"acc_norm\": 0.8033205619412516,\n\ \ \"acc_norm_stderr\": 0.014214138556913917\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7601156069364162,\n \"acc_stderr\": 0.022989592543123567,\n\ \ \"acc_norm\": 0.7601156069364162,\n \"acc_norm_stderr\": 0.022989592543123567\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.39329608938547483,\n\ \ \"acc_stderr\": 0.016337268694270112,\n \"acc_norm\": 0.39329608938547483,\n\ \ \"acc_norm_stderr\": 0.016337268694270112\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\": 0.729903536977492,\n\ \ \"acc_stderr\": 0.02521804037341062,\n \"acc_norm\": 0.729903536977492,\n\ \ \"acc_norm_stderr\": 0.02521804037341062\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7901234567901234,\n \"acc_stderr\": 0.02265834408598137,\n\ \ \"acc_norm\": 0.7901234567901234,\n \"acc_norm_stderr\": 0.02265834408598137\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4934810951760104,\n\ \ \"acc_stderr\": 0.012769150688867503,\n \"acc_norm\": 0.4934810951760104,\n\ \ \"acc_norm_stderr\": 0.012769150688867503\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.026679252270103135,\n\ \ \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.026679252270103135\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6911764705882353,\n \"acc_stderr\": 0.018690850273595294,\n \ \ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.018690850273595294\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.0282638899437846,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.0282638899437846\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598052,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598052\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03126781714663179,\n\ \ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03126781714663179\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5667074663402693,\n\ \ \"mc1_stderr\": 0.017347024450107485,\n \"mc2\": 0.7142943510205136,\n\ \ \"mc2_stderr\": 0.015024530295000761\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8358326756116812,\n \"acc_stderr\": 0.01041084977522279\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6474601971190296,\n \ \ \"acc_stderr\": 0.013159909755930337\n }\n}\n```" repo_url: https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|arc:challenge\|25_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-13T21-02-33.929144.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|gsm8k\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hellaswag\|10_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-13T21-02-33.929144.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|winogrande\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-13T21-02-33.929144.parquet' - config_name: results data_files: - split: 2023_12_13T21_02_33.929144 path: - results_2023-12-13T21-02-33.929144.parquet - split: latest path: - results_2023-12-13T21-02-33.929144.parquet --- # Dataset Card for Evaluation run of upstage/SOLAR-10.7B-Instruct-v1.0 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) 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_upstage__SOLAR-10.7B-Instruct-v1.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-13T21:02:33.929144](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__SOLAR-10.7B-Instruct-v1.0/blob/main/results_2023-12-13T21-02-33.929144.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": 0.6657586984797939, "acc_stderr": 0.03165995758526614, "acc_norm": 0.6666511531376961, "acc_norm_stderr": 0.0323050384069596, "mc1": 0.5667074663402693, "mc1_stderr": 0.017347024450107485, "mc2": 0.7142943510205136, "mc2_stderr": 0.015024530295000761 }, "harness\|arc:challenge\|25": { "acc": 0.6808873720136519, "acc_stderr": 0.013621696119173307, "acc_norm": 0.7107508532423208, "acc_norm_stderr": 0.01325001257939344 }, "harness\|hellaswag\|10": { "acc": 0.7070304720175263, "acc_stderr": 0.004541944342035901, "acc_norm": 0.8815972913762199, "acc_norm_stderr": 0.003224240722351317 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "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": 0.7368421052631579, "acc_stderr": 0.03583496176361072, "acc_norm": 0.7368421052631579, "acc_norm_stderr": 0.03583496176361072 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6792452830188679, "acc_stderr": 0.02872750295788027, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.02872750295788027 }, "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": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.52, "acc_stderr": 0.05021167315686779, "acc_norm": 0.52, "acc_norm_stderr": 0.05021167315686779 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6647398843930635, "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": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6297872340425532, "acc_stderr": 0.03156564682236785, "acc_norm": 0.6297872340425532, "acc_norm_stderr": 0.03156564682236785 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6413793103448275, "acc_stderr": 0.039966295748767186, "acc_norm": 0.6413793103448275, "acc_norm_stderr": 0.039966295748767186 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.47883597883597884, "acc_stderr": 0.025728230952130726, "acc_norm": 0.47883597883597884, "acc_norm_stderr": 0.025728230952130726 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.044444444444444495, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.044444444444444495 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8032258064516129, "acc_stderr": 0.022616409420742025, "acc_norm": 0.8032258064516129, "acc_norm_stderr": 0.022616409420742025 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.03515895551165698, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.03515895551165698 }, "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": 0.8, "acc_stderr": 0.031234752377721175, "acc_norm": 0.8, "acc_norm_stderr": 0.031234752377721175 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8737373737373737, "acc_stderr": 0.02366435940288023, "acc_norm": 0.8737373737373737, "acc_norm_stderr": 0.02366435940288023 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328973, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328973 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3814814814814815, "acc_stderr": 0.029616718927497593, "acc_norm": 0.3814814814814815, "acc_norm_stderr": 0.029616718927497593 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7184873949579832, "acc_stderr": 0.02921354941437217, "acc_norm": 0.7184873949579832, "acc_norm_stderr": 0.02921354941437217 }, "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": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.03388857118502325, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.03388857118502325 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8480392156862745, "acc_stderr": 0.0251956584289318, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.0251956584289318 }, "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": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7480916030534351, "acc_stderr": 0.03807387116306086, "acc_norm": 0.7480916030534351, "acc_norm_stderr": 0.03807387116306086 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "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": 0.44642857142857145, "acc_stderr": 0.047184714852195886, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.047184714852195886 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8589743589743589, "acc_stderr": 0.02280138253459753, "acc_norm": 0.8589743589743589, "acc_norm_stderr": 0.02280138253459753 }, "harness\|hendrycksTest-medical_genetics\|5": { 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0.7894736842105263, "acc_norm_stderr": 0.03126781714663179 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5667074663402693, "mc1_stderr": 0.017347024450107485, "mc2": 0.7142943510205136, "mc2_stderr": 0.015024530295000761 }, "harness\|winogrande\|5": { "acc": 0.8358326756116812, "acc_stderr": 0.01041084977522279 }, "harness\|gsm8k\|5": { "acc": 0.6474601971190296, "acc_stderr": 0.013159909755930337 } } ``` ## 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 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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.). 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]	[ -0.740734875202179, -0.8005796074867249, 0.30450865626335144, 0.22493064403533936, -0.15440736711025238, -0.05218392238020897, 0.019054826349020004, -0.17790308594703674, 0.585347056388855, -0.041386622935533524, -0.5279486179351807, -0.6874756217002869, -0.417203426361084, 0.23108451068401337, -0.06992459297180176, 0.8144623041152954, -0.137826606631279, 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T1m3y/CodeparrotV1	T1m3y	2023-12-14T09:38:30Z	9	0	[ "region:us" ]	null	2023-12-14T09:37:07Z	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: assembly dtype: string - name: c_source_code dtype: string splits: - name: train num_bytes: 427678057 num_examples: 231628 - name: val num_bytes: 108906658 num_examples: 57908 - name: test num_bytes: 134041825 num_examples: 72384 download_size: 181124979 dataset_size: 670626540 --- # Dataset Card for "CodeparrotV1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5961499810218811, -0.11058643460273743, -0.07827305048704147, 0.3086622953414917, -0.278887003660202, 0.18375608325004578, 0.2607592046260834, 0.17922872304916382, 0.8481168150901794, 0.44202664494514465, -0.8796290159225464, -0.8292089700698853, -0.5602554678916931, 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abhishek-mungoli/srk_wittiness	abhishek-mungoli	2023-12-14T15:03:02Z	9	1	[ "license:cc", "region:us" ]	null	2023-12-14T14:51:50Z	--- license: cc --- # Shah Rukh Khan Wittiness Reply DataSet Shah Rukh Khan is known for his wit, humor, and charm. I have carefully crafted some questions and imagined how SRK would have replied to them using Chat-GPT. You can use this data to train a smaller open-source language model or for any other use case you have in mind.	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kevind13/vuejs-codellama	kevind13	2023-12-14T15:28:57Z	9	0	[ "region:us" ]	null	2023-12-14T15:28:56Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 620238 num_examples: 52 download_size: 322188 dataset_size: 620238 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.12853379547595978, -0.1861676573753357, 0.6529129147529602, 0.49436256289482117, -0.1931929588317871, 0.2360745668411255, 0.3607199192047119, 0.05056329071521759, 0.5793652534484863, 0.7400139570236206, -0.6508101224899292, -0.2378396987915039, -0.7102251052856445, -0.047825805842876434, -0.3894752264022827, 0.8470761775970459, -0.09598271548748016, 0.024005070328712463, 0.04712017625570297, -0.14317847788333893, -0.6121039390563965, -0.04771736264228821, -1.052453637123108, -0.06787490844726562, 0.3002278804779053, 0.5120974779129028, 0.8275895118713379, 0.39602914452552795, 0.5030565857887268, 1.7515559196472168, -0.0883692055940628, 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sproos/twitter-pairclass-es	sproos	2023-12-14T18:47:30Z	9	0	[ "region:us" ]	null	2023-12-14T17:14:54Z	--- dataset_info: features: - name: sent1 sequence: string - name: sent2 sequence: string - name: labels sequence: int64 splits: - name: train num_bytes: 11427395 num_examples: 1 download_size: 4228525 dataset_size: 11427395 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter-pairclass-es" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.43976274132728577, -0.29801198840141296, 0.08236901462078094, 0.16962209343910217, -0.20233456790447235, 0.4536568522453308, 0.05558275803923607, -0.16543364524841309, 1.0585484504699707, 0.33646145462989807, -0.754996120929718, -0.8089259266853333, -0.6040475368499756, -0.3754293620586395, -0.6301393508911133, 1.051116704940796, 0.03862155228853226, -0.00357228796929121, -0.2641865909099579, 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MarkusStoll/cifar10-enrichments	MarkusStoll	2023-12-15T06:57:57Z	9	0	[ "region:us" ]	null	2023-12-15T06:57:45Z	--- dataset_info: features: - name: embedding sequence: float32 splits: - name: test num_bytes: 30760000 num_examples: 10000 download_size: 36966542 dataset_size: 30760000 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "cifar10-enrichments" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.8060940504074097, -0.10726102441549301, 0.15325213968753815, 0.26594093441963196, -0.06619898229837418, 0.21658499538898468, 0.1171865239739418, -0.3978562355041504, 0.8870227336883545, 0.2784007489681244, -0.7565516829490662, -0.5921339392662048, -0.5965147614479065, -0.1613059639930725, -0.46750253438949585, 1.291054606437683, 0.19634288549423218, 0.20395945012569427, -0.5987289547920227, -0.07739061862230301, -0.18915727734565735, -0.31190264225006104, 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nlplabtdtu/edu_parent_v2	nlplabtdtu	2023-12-15T08:00:36Z	9	0	[ "region:us" ]	null	2023-12-15T08:00:32Z	--- dataset_info: features: - name: content dtype: string - name: metadata struct: - name: id dtype: string - name: seq_num dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 165190 num_examples: 78 download_size: 82925 dataset_size: 165190 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.12853379547595978, -0.1861676573753357, 0.6529129147529602, 0.49436256289482117, -0.1931929588317871, 0.2360745668411255, 0.3607199192047119, 0.05056329071521759, 0.5793652534484863, 0.7400139570236206, -0.6508101224899292, -0.2378396987915039, -0.7102251052856445, -0.047825805842876434, -0.3894752264022827, 0.8470761775970459, -0.09598271548748016, 0.024005070328712463, 0.04712017625570297, -0.14317847788333893, -0.6121039390563965, -0.04771736264228821, -1.052453637123108, -0.06787490844726562, 0.3002278804779053, 0.5120974779129028, 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thorirhrafn/rmh_ds2	thorirhrafn	2023-12-15T10:59:32Z	9	0	[ "region:us" ]	null	2023-12-15T10:59:22Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 127179121 num_examples: 56000 - name: test num_bytes: 50199223 num_examples: 16800 - name: valid num_bytes: 3436783.6 num_examples: 1440 download_size: 109255791 dataset_size: 180815127.6 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, 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Isotonic/pii-masking-200k	Isotonic	2023-12-15T16:43:25Z	9	0	[ "task_categories:conversational", "task_categories:text-classification", "task_categories:token-classification", "task_categories:table-question-answering", "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:summarization", "task_categories:feature-extraction", "task_categories:text-generation", "task_categories:text2text-generation", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "language:fr", "language:de", "language:it", "license:apache-2.0", "legal", "business", "psychology", "privacy", "region:us" ]	[ "conversational", "text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-generation" ]	2023-12-15T12:27:53Z	--- language: - en - fr - de - it license: apache-2.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - conversational - text-classification - token-classification - table-question-answering - question-answering - zero-shot-classification - summarization - feature-extraction - text-generation - text2text-generation pretty_name: Ai4Privacy PII200k Dataset configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: masked_text dtype: string - name: unmasked_text dtype: string - name: privacy_mask dtype: string - name: span_labels dtype: string - name: bio_labels sequence: string - name: tokenised_text sequence: string - name: language dtype: string splits: - name: train num_bytes: 315574161 num_examples: 209261 download_size: 0 dataset_size: 315574161 tags: - legal - business - psychology - privacy --- # Purpose and Features World's largest open source privacy dataset. The purpose of the dataset is to train models to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs. The example texts have 54 PII classes (types of sensitive data), targeting 229 discussion subjects / use cases split across business, education, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...). Key facts: - Size: 13.6m text tokens in ~209k examples with 649k PII tokens (see [summary.json](summary.json)) - 4 languages, more to come! - English - French - German - Italian - Synthetic data generated using proprietary algorithms - No privacy violations! - Human-in-the-loop validated high quality dataset # Getting started Option 1: Python ```terminal pip install datasets ``` ```python from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k", data_files=[".jsonl"]) ``` or ```python from datasets import load_dataset dataset = load_dataset("Isotonic/pii-masking-200k") # use "language" column ``` # Token distribution across PII classes We have taken steps to balance the token distribution across PII classes covered by the dataset. This graph shows the distribution of observations across the different PII classes in this release: ![Token distribution across PII classes](pii_class_count_histogram.png) There is 1 class that is still overrepresented in the dataset: firstname. We will further improve the balance with future dataset releases. This is the token distribution excluding the FIRSTNAME class: ![Token distribution across PII classes excluding `FIRSTNAME`](pii_class_count_histogram_without_FIRSTNAME.png) # Compatible Machine Learning Tasks: - Tokenclassification. Check out a HuggingFace's [guide on token classification](https://huggingface.co/docs/transformers/tasks/token_classification). - [ALBERT](https://huggingface.co/docs/transformers/model_doc/albert), [BERT](https://huggingface.co/docs/transformers/model_doc/bert), [BigBird](https://huggingface.co/docs/transformers/model_doc/big_bird), [BioGpt](https://huggingface.co/docs/transformers/model_doc/biogpt), [BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom), [BROS](https://huggingface.co/docs/transformers/model_doc/bros), [CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert), [CANINE](https://huggingface.co/docs/transformers/model_doc/canine), [ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert), [Data2VecText](https://huggingface.co/docs/transformers/model_doc/data2vec-text), [DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta), [DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2), [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert), [ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra), [ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie), [ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m), [ESM](https://huggingface.co/docs/transformers/model_doc/esm), [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon), [FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert), [FNet](https://huggingface.co/docs/transformers/model_doc/fnet), [Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel), [GPT-Sw3](https://huggingface.co/docs/transformers/model_doc/gpt-sw3), [OpenAI GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2), [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode), [GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo), [GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox), [I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert), [LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm), [LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2), [LayoutLMv3](https://huggingface.co/docs/transformers/model_doc/layoutlmv3), [LiLT](https://huggingface.co/docs/transformers/model_doc/lilt), [Longformer](https://huggingface.co/docs/transformers/model_doc/longformer), [LUKE](https://huggingface.co/docs/transformers/model_doc/luke), [MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm), [MEGA](https://huggingface.co/docs/transformers/model_doc/mega), [Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert), [MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert), [MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet), [MPT](https://huggingface.co/docs/transformers/model_doc/mpt), [MRA](https://huggingface.co/docs/transformers/model_doc/mra), [Nezha](https://huggingface.co/docs/transformers/model_doc/nezha), [Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer), [QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert), [RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert), [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta), [RoBERTa-PreLayerNorm](https://huggingface.co/docs/transformers/model_doc/roberta-prelayernorm), [RoCBert](https://huggingface.co/docs/transformers/model_doc/roc_bert), [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer), [SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm), [XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta), [XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl), [XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet), [X-MOD](https://huggingface.co/docs/transformers/model_doc/xmod), [YOSO](https://huggingface.co/docs/transformers/model_doc/yoso) - Text Generation: Mapping the unmasked_text to to the masked_text or privacy_mask attributes. Check out HuggingFace's [guide to fine-tunning](https://huggingface.co/docs/transformers/v4.15.0/training) - [T5 Family](https://huggingface.co/docs/transformers/model_doc/t5), [Llama2](https://huggingface.co/docs/transformers/main/model_doc/llama2) # Information regarding the rows: - Each row represents a json object with a natural language text that includes placeholders for PII (and could plausibly be written by a human to an AI assistant). - Sample row: - "masked_text" contains a PII free natural text - "Product officially launching in [COUNTY_1]. Estimate profit of [CURRENCYSYMBOL_1][AMOUNT_1]. Expenses by [ACCOUNTNAME_1].", - "unmasked_text" shows a natural sentence containing PII - "Product officially launching in Washington County. Estimate profit of $488293.16. Expenses by Checking Account." - "privacy_mask" indicates the mapping between the privacy token instances and the string within the natural text. - "{'[COUNTY_1]': 'Washington County', '[CURRENCYSYMBOL_1]': '$', '[AMOUNT_1]': '488293.16', '[ACCOUNTNAME_1]': 'Checking Account'}" - "span_labels" is an array of arrays formatted in the following way [start, end, pii token instance].* - "[[0, 32, 'O'], [32, 49, 'COUNTY_1'], [49, 70, 'O'], [70, 71, 'CURRENCYSYMBOL_1'], [71, 80, 'AMOUNT_1'], [80, 94, 'O'], [94, 110, 'ACCOUNTNAME_1'], [110, 111, 'O']]", - "bio_labels" follows the common place notation for "beginning", "inside" and "outside" of where each private tokens starts.[original paper](https://arxiv.org/abs/cmp-lg/9505040) -["O", "O", "O", "O", "B-COUNTY", "I-COUNTY", "O", "O", "O", "O", "B-CURRENCYSYMBOL", "O", "O", "I-AMOUNT", "I-AMOUNT", "I-AMOUNT", "I-AMOUNT", "O", "O", "O", "B-ACCOUNTNAME", "I-ACCOUNTNAME", "O"], - "tokenised_text" breaks down the unmasked sentence into tokens using Bert Family tokeniser to help fine-tune large language models. - ["product", "officially", "launching", "in", "washington", "county", ".", "estimate", "profit", "of", "$", "48", "##8", "##29", "##3", ".", "16", ".", "expenses", "by", "checking", "account", "."] note for the nested objects, we store them as string to maximise compability between various software. Note: the bio_labels and tokenised_text have been created using [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) # About Us: At Ai4Privacy, we are commited to building the global seatbelt of the 21st century for Artificial Intelligence to help fight against potential risks of personal information being integrated into data pipelines. Newsletter & updates: [www.Ai4Privacy.com](www.Ai4Privacy.com) - Looking for ML engineers, developers, beta-testers, human in the loop validators (all languages) - Integrations with already existing open source solutions - Ask us a question on discord: [https://discord.gg/kxSbJrUQZF](https://discord.gg/kxSbJrUQZF) # Roadmap and Future Development - Carbon Neutral - Benchmarking - Better multilingual and especially localisation - Extended integrations - Continuously increase the training set - Further optimisation to the model to reduce size and increase generalisability - Next released major update is planned for the 14th of December 2023 (subscribe to newsletter for updates) # Use Cases and Applications Chatbots: Incorporating a PII masking model into chatbot systems can ensure the privacy and security of user conversations by automatically redacting sensitive information such as names, addresses, phone numbers, and email addresses. Customer Support Systems: When interacting with customers through support tickets or live chats, masking PII can help protect sensitive customer data, enabling support agents to handle inquiries without the risk of exposing personal information. Email Filtering: Email providers can utilize a PII masking model to automatically detect and redact PII from incoming and outgoing emails, reducing the chances of accidental disclosure of sensitive information. Data Anonymization: Organizations dealing with large datasets containing PII, such as medical or financial records, can leverage a PII masking model to anonymize the data before sharing it for research, analysis, or collaboration purposes. Social Media Platforms: Integrating PII masking capabilities into social media platforms can help users protect their personal information from unauthorized access, ensuring a safer online environment. Content Moderation: PII masking can assist content moderation systems in automatically detecting and blurring or redacting sensitive information in user-generated content, preventing the accidental sharing of personal details. Online Forms: Web applications that collect user data through online forms, such as registration forms or surveys, can employ a PII masking model to anonymize or mask the collected information in real-time, enhancing privacy and data protection. Collaborative Document Editing: Collaboration platforms and document editing tools can use a PII masking model to automatically mask or redact sensitive information when multiple users are working on shared documents. Research and Data Sharing: Researchers and institutions can leverage a PII masking model to ensure privacy and confidentiality when sharing datasets for collaboration, analysis, or publication purposes, reducing the risk of data breaches or identity theft. Content Generation: Content generation systems, such as article generators or language models, can benefit from PII masking to automatically mask or generate fictional PII when creating sample texts or examples, safeguarding the privacy of individuals. (...and whatever else your creative mind can think of) # Support and Maintenance AI4Privacy is a project affiliated with [AISuisse SA](https://www.aisuisse.com/).	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NAYEIRN23/CASOSPJ	NAYEIRN23	2023-12-15T20:01:05Z	9	0	[ "license:llama2", "region:us" ]	null	2023-12-15T19:06:36Z	--- license: llama2 dataset_info: features: - name: EXPEDIENTE dtype: string - name: ' JUZGADO' dtype: string - name: ' F.INICIO' dtype: string - name: ' MOTIVO ING' dtype: string - name: ' JUEZ' dtype: string - name: ' ESP.LEGAL' dtype: string - name: ' ESTADO' dtype: string - name: ' DEMANDADO' dtype: string - name: ' DEMANDANTE' dtype: string - name: 'SUMILLA ' dtype: string - name: TITULO dtype: string splits: - name: train num_bytes: 6816 num_examples: 14 - name: validation num_bytes: 1015 num_examples: 2 - name: test num_bytes: 747 num_examples: 2 download_size: 26817 dataset_size: 8578 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---	[ -0.12853386998176575, -0.18616794049739838, 0.6529127359390259, 0.4943622946739197, -0.19319306313991547, 0.2360745519399643, 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deokhk/te_wiki_sentences_1000000	deokhk	2023-12-17T12:06:54Z	9	0	[ "region:us" ]	null	2023-12-17T12:06:46Z	--- dataset_info: features: - name: sentence dtype: string splits: - name: train num_bytes: 189344044 num_examples: 1000000 - name: dev num_bytes: 166164 num_examples: 1000 download_size: 43341997 dataset_size: 189510208 --- # Dataset Card for "te_wiki_sentences_1000000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5657354593276978, -0.4481031596660614, 0.25489363074302673, 0.34874093532562256, -0.08204740285873413, -0.338430255651474, -0.05839109793305397, -0.059443216770887375, 0.7549464702606201, 0.4568209648132324, -0.7568772435188293, -0.6309769153594971, -0.49599432945251465, 0.13849906623363495, -0.4561978280544281, 1.2055529356002808, 0.21063867211341858, 0.0014643383910879493, -0.48845216631889343, -0.07172559201717377, -0.4989776611328125, -0.15744544565677643, 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zjguoHF/tokenized_TinyShakespear_dataset	zjguoHF	2023-12-17T16:16:25Z	9	0	[ "region:us" ]	null	2023-12-17T16:16:22Z	--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 2638965 num_examples: 32592 - name: validation num_bytes: 223829 num_examples: 2795 - name: test num_bytes: 358179 num_examples: 4611 download_size: 988180 dataset_size: 3220973 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---	[ -0.12853389978408813, -0.18616773188114166, 0.6529126167297363, 0.49436259269714355, -0.19319310784339905, 0.2360745072364807, 0.36071985960006714, 0.050563156604766846, 0.579365611076355, 0.7400138974189758, -0.6508100628852844, -0.23783960938453674, -0.7102248072624207, -0.04782594367861748, -0.38947513699531555, 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justinian336/salvadoran-news-edh	justinian336	2023-12-18T01:03:22Z	8	0	[ "region:us" ]	null	2023-02-15T14:34:40Z	--- dataset_info: features: - name: image_src dtype: string - name: title dtype: string - name: content dtype: string - name: category dtype: class_label: names: '0': deportes '1': videos '2': opinion '3': deportes/zona-mundialista '4': opinion/caricaturas '5': vida '6': entretenimiento '7': null '8': noticias '9': fotogalerias - name: link dtype: string splits: - name: train num_bytes: 196407515 num_examples: 55345 download_size: 111585539 dataset_size: 196407515 --- # Dataset Card for "salvadoran-news-edh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5064284205436707, -0.3554539680480957, 0.31866440176963806, 0.3239046037197113, -0.4753098487854004, 0.04776420816779137, -0.02869872748851776, -0.20182998478412628, 1.1237468719482422, 0.46967557072639465, -0.8066130876541138, 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TheLastBen/RNPD	TheLastBen	2023-12-14T10:35:07Z	8	5	[ "license:cc-by-nc-4.0", "region:us" ]	null	2023-02-15T23:44:26Z	--- license: cc-by-nc-4.0 ---	[ -0.12853379547595978, -0.1861676573753357, 0.6529129147529602, 0.49436256289482117, -0.1931929588317871, 0.2360745668411255, 0.3607199192047119, 0.05056329071521759, 0.5793652534484863, 0.7400139570236206, -0.6508101224899292, -0.2378396987915039, -0.7102251052856445, -0.047825805842876434, -0.3894752264022827, 0.8470761775970459, -0.09598271548748016, 0.024005070328712463, 0.04712017625570297, -0.14317847788333893, -0.6121039390563965, -0.04771736264228821, -1.052453637123108, -0.06787490844726562, 0.3002278804779053, 0.5120974779129028, 0.8275895118713379, 0.39602914452552795, 0.5030565857887268, 1.7515559196472168, -0.0883692055940628, -0.22754433751106262, -0.4589202404022217, 0.4223068654537201, -0.33277350664138794, -0.4213371276855469, -0.2624165117740631, -0.07449345290660858, 0.3238039016723633, 0.7903713583946228, 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mikehemberger/plantnet300K	mikehemberger	2023-12-14T15:25:38Z	8	1	[ "region:us" ]	null	2023-08-06T17:04:42Z	--- 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: label dtype: class_label: names: '0': '1355868' '1': '1355920' '2': '1355932' '3': '1355936' '4': '1355937' '5': '1355955' '6': '1355959' '7': '1355961' '8': '1355978' '9': '1355990' '10': '1356003' '11': '1356022' '12': '1356037' '13': '1356055' '14': '1356075' '15': '1356076' '16': '1356111' '17': '1356126' '18': '1356138' '19': '1356257' '20': '1356278' '21': '1356279' '22': '1356309' '23': '1356379' '24': '1356380' '25': '1356382' '26': '1356420' '27': '1356421' '28': '1356428' '29': '1356469' '30': '1356692' '31': '1356781' '32': '1356816' '33': '1356847' '34': '1356901' '35': '1357330' '36': '1357331' '37': '1357367' '38': '1357379' '39': '1357506' '40': '1357635' '41': '1357652' '42': '1357677' '43': '1357681' '44': '1357682' '45': '1357705' '46': '1358094' '47': '1358095' '48': '1358096' '49': '1358097' '50': '1358099' '51': '1358101' '52': '1358102' '53': '1358103' '54': '1358105' '55': '1358108' '56': '1358112' '57': '1358119' '58': '1358127' '59': '1358132' '60': '1358133' '61': '1358150' '62': '1358193' '63': '1358302' '64': '1358365' '65': '1358519' '66': '1358605' '67': '1358608' '68': '1358689' '69': '1358690' '70': '1358691' '71': '1358695' '72': '1358699' '73': '1358700' '74': '1358701' '75': '1358703' '76': '1358704' '77': '1358706' '78': '1358710' '79': '1358711' '80': '1358748' '81': '1358749' '82': '1358750' '83': '1358751' '84': '1358752' '85': '1358755' '86': '1358759' '87': '1358760' '88': '1358766' '89': '1359060' '90': '1359064' '91': '1359065' '92': '1359197' '93': '1359331' '94': '1359332' '95': '1359359' '96': '1359364' '97': '1359483' '98': '1359485' '99': '1359486' '100': '1359488' '101': '1359489' '102': '1359495' '103': '1359497' '104': '1359498' '105': '1359502' '106': '1359505' '107': '1359506' '108': '1359508' '109': '1359510' '110': '1359513' '111': '1359514' '112': '1359517' '113': '1359518' '114': '1359519' '115': '1359521' '116': '1359523' '117': '1359524' '118': '1359525' '119': '1359526' '120': '1359528' '121': '1359530' '122': '1359616' '123': '1359620' '124': '1359622' '125': '1359625' '126': '1359669' '127': '1359806' '128': '1359815' '129': '1359821' '130': '1360004' '131': '1360147' '132': '1360148' '133': '1360150' '134': '1360152' '135': '1360153' '136': '1360154' '137': '1360427' '138': '1360550' '139': '1360555' '140': '1360588' '141': '1360590' '142': '1360591' '143': '1360618' '144': '1360671' '145': '1360687' '146': '1360759' '147': '1360808' '148': '1360811' '149': '1360835' '150': '1360838' '151': '1360976' '152': '1360978' '153': '1360998' '154': '1361024' '155': '1361233' '156': '1361316' '157': '1361357' '158': '1361507' '159': '1361508' '160': '1361524' '161': '1361655' '162': '1361656' '163': '1361658' '164': '1361660' '165': '1361663' '166': '1361666' '167': '1361668' '168': '1361672' '169': '1361704' '170': '1361745' '171': '1361759' '172': '1361823' '173': '1361824' '174': '1361847' '175': '1361850' '176': '1361891' '177': '1362024' '178': '1362064' '179': '1362080' '180': '1362192' '181': '1362294' '182': '1362385' '183': '1362398' '184': '1362434' '185': '1362435' '186': '1362489' '187': '1362490' '188': '1362516' '189': '1362582' '190': '1362834' '191': '1362927' '192': '1362928' '193': '1362954' '194': '1363016' '195': '1363019' '196': '1363021' '197': '1363110' '198': '1363117' '199': '1363126' '200': '1363127' '201': '1363128' '202': '1363129' '203': '1363130' '204': '1363227' '205': '1363336' '206': '1363343' '207': '1363359' '208': '1363451' '209': '1363463' '210': '1363464' '211': '1363489' '212': '1363490' '213': '1363491' '214': '1363492' '215': '1363493' '216': '1363613' '217': '1363688' '218': '1363699' '219': '1363700' '220': '1363701' '221': '1363703' '222': '1363735' '223': '1363737' '224': '1363738' '225': '1363739' '226': '1363740' '227': '1363743' '228': '1363749' '229': '1363750' '230': '1363764' '231': '1363769' '232': '1363770' '233': '1363772' '234': '1363778' '235': '1363871' '236': '1363872' '237': '1363970' '238': '1363991' '239': '1364000' '240': '1364099' '241': '1364112' '242': '1364158' '243': '1364159' '244': '1364161' '245': '1364164' '246': '1364165' '247': '1364172' '248': '1364173' '249': '1364440' '250': '1364512' '251': '1365712' '252': '1365961' '253': '1366875' '254': '1366898' '255': '1366899' '256': '1366997' '257': '1367060' '258': '1367087' '259': '1367267' '260': '1367343' '261': '1367369' '262': '1367431' '263': '1367432' '264': '1367781' '265': '1367784' '266': '1367921' '267': '1367938' '268': '1367949' '269': '1367998' '270': '1369472' '271': '1369887' '272': '1369960' '273': '1370279' '274': '1370991' '275': '1370993' '276': '1371680' '277': '1371691' '278': '1372016' '279': '1372602' '280': '1373151' '281': '1373231' '282': '1373259' '283': '1373530' '284': '1373535' '285': '1374048' '286': '1374448' '287': '1374450' '288': '1374466' '289': '1374933' '290': '1375776' '291': '1375797' '292': '1375895' '293': '1375954' '294': '1375965' '295': '1375966' '296': '1376366' '297': '1376703' '298': '1376726' '299': '1379303' '300': '1379513' '301': '1379556' '302': '1379585' '303': '1380874' '304': '1380904' '305': '1381173' '306': '1381468' '307': '1382099' '308': '1383149' '309': '1383772' '310': '1383774' '311': '1384199' '312': '1384482' '313': '1384485' '314': '1384497' '315': '1384499' '316': '1384500' '317': '1384760' '318': '1385700' '319': '1385701' '320': '1385749' '321': '1385853' '322': '1385937' '323': '1387107' '324': '1387284' '325': '1387687' '326': '1387727' '327': '1388905' '328': '1388911' '329': '1389016' '330': '1389031' '331': '1389044' '332': '1389078' '333': '1389230' '334': '1389231' '335': '1389294' '336': '1389297' '337': '1389307' '338': '1389308' '339': '1389426' '340': '1389442' '341': '1389492' '342': '1389510' '343': '1389553' '344': '1389567' '345': '1389628' '346': '1389630' '347': '1389633' '348': '1389921' '349': '1389966' '350': '1390617' '351': '1390637' '352': '1390956' '353': '1390957' '354': '1391110' '355': '1391112' '356': '1391113' '357': '1391167' '358': '1391184' '359': '1391190' '360': '1391192' '361': '1391193' '362': '1391197' '363': '1391198' '364': '1391206' '365': '1391209' '366': '1391221' '367': '1391226' '368': '1391235' '369': '1391250' '370': '1391326' '371': '1391483' '372': '1391498' '373': '1391499' '374': '1391529' '375': '1391530' '376': '1391648' '377': '1391649' '378': '1391652' '379': '1391654' '380': '1391762' '381': '1391797' '382': '1391805' '383': '1391807' '384': '1391809' '385': '1391810' '386': '1391821' '387': '1391868' '388': '1391953' '389': '1391960' '390': '1391961' '391': '1391962' '392': '1391963' '393': '1391964' '394': '1391967' '395': '1392093' '396': '1392094' '397': '1392096' '398': '1392254' '399': '1392361' '400': '1392365' '401': '1392418' '402': '1392475' '403': '1392601' '404': '1392653' '405': '1392654' '406': '1392658' '407': '1392689' '408': '1392690' '409': '1392691' '410': '1392695' '411': '1392730' '412': '1392777' '413': '1393241' '414': '1393242' '415': '1393245' '416': '1393248' '417': '1393250' '418': '1393253' '419': '1393254' '420': '1393257' '421': '1393294' '422': '1393322' '423': '1393393' '424': '1393414' '425': '1393416' '426': '1393418' '427': '1393423' '428': '1393425' '429': '1393449' '430': '1393450' '431': '1393465' '432': '1393466' '433': '1393537' '434': '1393614' '435': '1393654' '436': '1393656' '437': '1393725' '438': '1393789' '439': '1393792' '440': '1393796' '441': '1393818' '442': '1393823' '443': '1393846' '444': '1393850' '445': '1393851' '446': '1393933' '447': '1393945' '448': '1393946' '449': '1393965' '450': '1393967' '451': '1393968' '452': '1393980' '453': '1393981' '454': '1394072' '455': '1394073' '456': '1394077' '457': '1394078' '458': '1394082' '459': '1394088' '460': '1394114' '461': '1394120' '462': '1394128' '463': '1394143' '464': '1394153' '465': '1394164' '466': '1394169' '467': '1394183' '468': '1394189' '469': '1394313' '470': '1394378' '471': '1394382' '472': '1394383' '473': '1394394' '474': '1394396' '475': '1394399' '476': '1394401' '477': '1394404' '478': '1394405' '479': '1394406' '480': '1394418' '481': '1394420' '482': '1394453' '483': '1394454' '484': '1394455' '485': '1394457' '486': '1394460' '487': '1394489' '488': '1394498' '489': '1394502' '490': '1394504' '491': '1394508' '492': '1394513' '493': '1394549' '494': '1394580' '495': '1394591' '496': '1394648' '497': '1394714' '498': '1394759' '499': '1394994' '500': '1394995' '501': '1395391' '502': '1395720' '503': '1395721' '504': '1395722' '505': '1396133' '506': '1396134' '507': '1396143' '508': '1396144' '509': '1396145' '510': '1396156' '511': '1396159' '512': '1396161' '513': '1396165' '514': '1396708' '515': '1396713' '516': '1396754' '517': '1396758' '518': '1396823' '519': '1396824' '520': '1396842' '521': '1396843' '522': '1396858' '523': '1396867' '524': '1396869' '525': '1397208' '526': '1397209' '527': '1397268' '528': '1397269' '529': '1397303' '530': '1397305' '531': '1397311' '532': '1397312' '533': '1397313' '534': '1397349' '535': '1397351' '536': '1397352' '537': '1397364' '538': '1397379' '539': '1397387' '540': '1397403' '541': '1397407' '542': '1397408' '543': '1397420' '544': '1397491' '545': '1397510' '546': '1397514' '547': '1397515' '548': '1397550' '549': '1397551' '550': '1397556' '551': '1397585' '552': '1397598' '553': '1397613' '554': '1397841' '555': '1397959' '556': '1398041' '557': '1398111' '558': '1398125' '559': '1398128' '560': '1398130' '561': '1398178' '562': '1398196' '563': '1398274' '564': '1398326' '565': '1398333' '566': '1398355' '567': '1398444' '568': '1398447' '569': '1398469' '570': '1398480' '571': '1398494' '572': '1398513' '573': '1398515' '574': '1398526' '575': '1398567' '576': '1398592' '577': '1398608' '578': '1398615' '579': '1398700' '580': '1398805' '581': '1398830' '582': '1399063' '583': '1399141' '584': '1399145' '585': '1399157' '586': '1399231' '587': '1399338' '588': '1399384' '589': '1399667' '590': '1399783' '591': '1399803' '592': '1399804' '593': '1399920' '594': '1400060' '595': '1400080' '596': '1400081' '597': '1400096' '598': '1400100' '599': '1400104' '600': '1400110' '601': '1400471' '602': '1400478' '603': '1400798' '604': '1400844' '605': '1401141' '606': '1401148' '607': '1401211' '608': '1401216' '609': '1401222' '610': '1401242' '611': '1401701' '612': '1401711' '613': '1401718' '614': '1401735' '615': '1401736' '616': '1401737' '617': '1401883' '618': '1402303' '619': '1402356' '620': '1402423' '621': '1402433' '622': '1402464' '623': '1402576' '624': '1402811' '625': '1402920' '626': '1402921' '627': '1402924' '628': '1402925' '629': '1402926' '630': '1403272' '631': '1403479' '632': '1403654' '633': '1403826' '634': '1403967' '635': '1404002' '636': '1404481' '637': '1404745' '638': '1404776' '639': '1404804' '640': '1405061' '641': '1405088' '642': '1405252' '643': '1405641' '644': '1405653' '645': '1405685' '646': '1405686' '647': '1405703' '648': '1406426' '649': '1406486' '650': '1406756' '651': '1406863' '652': '1407077' '653': '1407379' '654': '1407873' '655': '1407874' '656': '1408034' '657': '1408037' '658': '1408041' '659': '1408045' '660': '1408066' '661': '1408071' '662': '1408092' '663': '1408107' '664': '1408160' '665': '1408214' '666': '1408227' '667': '1408284' '668': '1408386' '669': '1408401' '670': '1408421' '671': '1408451' '672': '1408468' '673': '1408490' '674': '1408502' '675': '1408507' '676': '1408508' '677': '1408509' '678': '1408557' '679': '1408594' '680': '1408657' '681': '1408688' '682': '1408718' '683': '1408727' '684': '1408734' '685': '1408738' '686': '1408774' '687': '1408788' '688': '1408810' '689': '1408812' '690': '1408845' '691': '1408869' '692': '1408874' '693': '1408953' '694': '1408961' '695': '1408979' '696': '1409185' '697': '1409191' '698': '1409195' '699': '1409214' '700': '1409215' '701': '1409216' '702': '1409238' '703': '1409239' '704': '1409283' '705': '1409292' '706': '1409295' '707': '1409296' '708': '1409299' '709': '1409328' '710': '1409407' '711': '1409521' '712': '1409525' '713': '1409550' '714': '1409551' '715': '1409552' '716': '1409586' '717': '1409642' '718': '1409705' '719': '1409746' '720': '1409824' '721': '1409834' '722': '1409839' '723': '1409918' '724': '1409919' '725': '1409932' '726': '1410024' '727': '1410025' '728': '1410147' '729': '1410168' '730': '1411423' '731': '1411462' '732': '1411466' '733': '1411469' '734': '1411472' '735': '1411520' '736': '1411524' '737': '1411687' '738': '1411700' '739': '1411760' '740': '1411827' '741': '1411829' '742': '1411832' '743': '1411833' '744': '1411834' '745': '1411925' '746': '1412311' '747': '1412313' '748': '1412314' '749': '1412337' '750': '1412344' '751': '1412368' '752': '1412410' '753': '1412412' '754': '1412413' '755': '1412418' '756': '1412444' '757': '1412445' '758': '1412595' '759': '1412596' '760': '1412659' '761': '1412661' '762': '1412662' '763': '1412663' '764': '1412697' '765': '1412698' '766': '1412699' '767': '1412740' '768': '1412745' '769': '1412831' '770': '1412833' '771': '1412834' '772': '1412888' '773': '1412889' '774': '1412946' '775': '1412992' '776': '1413002' '777': '1413013' '778': '1413391' '779': '1413751' '780': '1413752' '781': '1413753' '782': '1413755' '783': '1413757' '784': '1413832' '785': '1413835' '786': '1414057' '787': '1414058' '788': '1414272' '789': '1414275' '790': '1414276' '791': '1414397' '792': '1414746' '793': '1414808' '794': '1414903' '795': '1414924' '796': '1415584' '797': '1415593' '798': '1415911' '799': '1416420' '800': '1416509' '801': '1417117' '802': '1417506' '803': '1417546' '804': '1417900' '805': '1417916' '806': '1418033' '807': '1418061' '808': '1418084' '809': '1418102' '810': '1418140' '811': '1418146' '812': '1418191' '813': '1418192' '814': '1418295' '815': '1418345' '816': '1418475' '817': '1418545' '818': '1418546' '819': '1418547' '820': '1418563' '821': '1418576' '822': '1418653' '823': '1418655' '824': '1418659' '825': '1418665' '826': '1418732' '827': '1418984' '828': '1419077' '829': '1419086' '830': '1419091' '831': '1419112' '832': '1419115' '833': '1419173' '834': '1419197' '835': '1419334' '836': '1419352' '837': '1419595' '838': '1419598' '839': '1419612' '840': '1419713' '841': '1419720' '842': '1419807' '843': '1419864' '844': '1419874' '845': '1419923' '846': '1419924' '847': '1419942' '848': '1419949' '849': '1420092' '850': '1420288' '851': '1420364' '852': '1420365' '853': '1420496' '854': '1420544' '855': '1420653' '856': '1420700' '857': '1420767' '858': '1420781' '859': '1420787' '860': '1420792' '861': '1420795' '862': '1420796' '863': '1420863' '864': '1420896' '865': '1420929' '866': '1420931' '867': '1421001' '868': '1421011' '869': '1421021' '870': '1421026' '871': '1421107' '872': '1421386' '873': '1421523' '874': '1421628' '875': '1422105' '876': '1422636' '877': '1422712' '878': '1422721' '879': '1423286' '880': '1423469' '881': '1423633' '882': '1423932' '883': '1424003' '884': '1424005' '885': '1424377' '886': '1425433' '887': '1425899' '888': '1427043' '889': '1427659' '890': '1427821' '891': '1428059' '892': '1430280' '893': '1430287' '894': '1430515' '895': '1431222' '896': '1431925' '897': '1432545' '898': '1432783' '899': '1432786' '900': '1432824' '901': '1433158' '902': '1433176' '903': '1433496' '904': '1434011' '905': '1434232' '906': '1434249' '907': '1434258' '908': '1434267' '909': '1434320' '910': '1434326' '911': '1434383' '912': '1434584' '913': '1434594' '914': '1434712' '915': '1435225' '916': '1435228' '917': '1435260' '918': '1435709' '919': '1435714' '920': '1436922' '921': '1436923' '922': '1437724' '923': '1438033' '924': '1438041' '925': '1438043' '926': '1438045' '927': '1438049' '928': '1438052' '929': '1438056' '930': '1438352' '931': '1438354' '932': '1438358' '933': '1438616' '934': '1438868' '935': '1439145' '936': '1439703' '937': '1440273' '938': '1440412' '939': '1440416' '940': '1440423' '941': '1440425' '942': '1440440' '943': '1440446' '944': '1440476' '945': '1441099' '946': '1441349' '947': '1441375' '948': '1441410' '949': '1442541' '950': '1442833' '951': '1442865' '952': '1442930' '953': '1444049' '954': '1444156' '955': '1444195' '956': '1444216' '957': '1447962' '958': '1449139' '959': '1449149' '960': '1453004' '961': '1455408' '962': '1460343' '963': '1463177' '964': '1464023' '965': '1464054' '966': '1464075' '967': '1464086' '968': '1464088' '969': '1464459' '970': '1464567' '971': '1465016' '972': '1465026' '973': '1465149' '974': '1465170' '975': '1470435' '976': '1486475' '977': '1486501' '978': '1486655' '979': '1488001' '980': '1488026' '981': '1488171' '982': '1488206' '983': '1488285' '984': '1491513' '985': '1494482' '986': '1497434' '987': '1497630' '988': '1497667' '989': '1503981' '990': '1506976' '991': '1513142' '992': '1513780' '993': '1514035' '994': '1514627' '995': '1516297' '996': '1518683' '997': '1522375' '998': '1522377' '999': '1529081' '1000': '1529084' '1001': '1529107' '1002': '1529124' '1003': '1529128' '1004': '1529144' '1005': '1529148' '1006': '1529179' '1007': '1529205' '1008': '1529212' '1009': '1529242' '1010': '1529265' '1011': '1529305' '1012': '1529328' '1013': '1535203' '1014': '1538569' '1015': '1542499' '1016': '1550658' '1017': '1550692' '1018': '1550785' '1019': '1550799' '1020': '1560405' '1021': '1567995' '1022': '1568056' '1023': '1568102' '1024': '1568134' '1025': '1568144' '1026': '1568799' '1027': '1568914' '1028': '1576296' '1029': '1580985' '1030': '1584568' '1031': '1600531' '1032': '1608001' '1033': '1608003' '1034': '1608023' '1035': '1608027' '1036': '1608034' '1037': '1608440' '1038': '1617292' '1039': '1617904' '1040': '1618100' '1041': '1618570' '1042': '1618661' '1043': '1621823' '1044': '1638998' '1045': '1643184' '1046': '1643341' '1047': '1643349' '1048': '1643491' '1049': '1643553' '1050': '1643690' '1051': '1643819' '1052': '1643866' '1053': '1643887' '1054': '1644145' '1055': '1644223' '1056': '1646149' '1057': '1662426' '1058': '1663341' '1059': '1663381' '1060': '1664348' '1061': '1667445' '1062': '1675513' '1063': '1682159' '1064': '1689963' '1065': '1694631' '1066': '1697960' '1067': '1697991' '1068': '1698065' '1069': '1711751' '1070': '1711753' '1071': '1711775' '1072': '1711787' '1073': '1711873' '1074': '1712003' '1075': '1712019' '1076': '1712365' '1077': '1713198' '1078': '1713199' '1079': '1717570' '1080': '1718287' splits: - name: train num_bytes: 25342011377.02 num_examples: 243916 - name: validation num_bytes: 3577279070.02 num_examples: 31118 - name: test num_bytes: 3537246514.632 num_examples: 31112 download_size: 3223619352 dataset_size: 32456536961.672 --- # Dataset Card for "plantnet300K" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) Original Work is here: https://github.com/plantnet/PlantNet-300K @inproceedings{plantnet-300k, author = {C. Garcin and A. Joly and P. Bonnet and A. Affouard and \JC Lombardo and M. Chouet and M. Servajean and T. Lorieul and J. Salmon}, booktitle = {NeurIPS Datasets and Benchmarks 2021}, title = {{Pl@ntNet-300K}: a plant image dataset with high label ambiguity and a long-tailed distribution}, year = {2021}, }	[ -0.5563533902168274, -0.002701585413888097, 0.1099977046251297, 0.3291757106781006, -0.07409205287694931, -0.2881348133087158, -0.04723058640956879, -0.6111621260643005, 0.6798145771026611, 0.38216397166252136, -0.802592933177948, -0.5205819606781006, -0.5892308354377747, 0.25992950797080994, -0.12358108162879944, 0.9168094396591187, 0.10601333528757095, 0.009094656445086002, -0.3352397084236145, -0.39643433690071106, -0.18344242870807648, -0.2642613351345062, -0.7155769467353821, -0.49413228034973145, 0.7949549555778503, 0.6188297867774963, 0.6040217876434326, 0.64909827709198, 0.4783289432525635, 0.26457709074020386, -0.26908567547798157, -0.1592143177986145, -0.24431714415550232, 0.03391396626830101, -0.06804592162370682, -0.008304248563945293, -1.0049748420715332, 0.20583641529083252, 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macarious/en_corpora_parliament_processed	macarious	2023-12-15T01:13:56Z	8	0	[ "region:us" ]	null	2023-09-16T00:52:40Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 309185247 num_examples: 2051014 download_size: 171553321 dataset_size: 309185247 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "en_corpora_parliament_processed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.547006368637085, -0.22935697436332703, 0.31427842378616333, 0.352134108543396, -0.37217628955841064, 0.03884729743003845, -0.2700398862361908, -0.17091019451618195, 0.7887668013572693, 0.8268519639968872, -0.7137999534606934, -0.9043006896972656, -0.7860357761383057, 0.11811497062444687, -0.3140920102596283, 0.892570972442627, 0.20003008842468262, 0.25092580914497375, -0.3898553252220154, -0.08585911244153976, -0.32251405715942383, 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rohit901/nlp_proj_llm_hallucination	rohit901	2023-12-16T06:41:42Z	8	0	[ "region:us" ]	null	2023-10-27T10:49:03Z	--- configs: - config_name: default data_files: - split: gpt3_5 path: "gpt3_5_hallucination.json" - split: gpt4 path: "gpt4_hallucination.json" - split: vicuna path: "vicuna_hallucination.json" - split: open_orca_mistral path: "open_orca_mistral_hallucination.json" ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, -0.06787490844726562, 0.3002279996871948, 0.5120974779129028, 0.8275896310806274, 0.3960292339324951, 0.5030564665794373, 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deepghs/csip	deepghs	2023-12-11T11:54:47Z	8	1	[ "task_categories:zero-shot-classification", "size_categories:1M<n<10M", "license:openrail", "art", "region:us" ]	[ "zero-shot-classification" ]	2023-11-16T14:17:28Z	--- license: openrail task_categories: - zero-shot-classification tags: - art size_categories: - 1M<n<10M ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, -0.06787490844726562, 0.3002279996871948, 0.5120974779129028, 0.8275896310806274, 0.3960292339324951, 0.5030564665794373, 1.7515558004379272, -0.08836928009986877, -0.22754442691802979, 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CarlosBuiol/testimage	CarlosBuiol	2023-12-17T14:17:01Z	8	0	[ "license:mit", "region:us" ]	null	2023-11-25T16:26:54Z	--- license: mit ---	[ -0.12853407859802246, -0.1861678659915924, 0.6529132127761841, 0.494362473487854, -0.19319304823875427, 0.23607467114925385, 0.3607199788093567, 0.05056385695934296, 0.5793653726577759, 0.7400137782096863, -0.6508100032806396, -0.23783984780311584, -0.710224986076355, -0.04782610014081001, -0.38947540521621704, 0.8470761775970459, -0.09598255157470703, 0.024004563689231873, 0.04711991548538208, -0.14317817986011505, -0.6121035814285278, -0.04771742224693298, -1.0524539947509766, -0.06787490844726562, 0.30022814869880676, 0.5120974779129028, 0.8275901079177856, 0.39602965116500854, 0.5030569434165955, 1.751556158065796, -0.0883694589138031, -0.22754427790641785, -0.45892083644866943, 0.42230719327926636, -0.3327736556529999, -0.4213365912437439, -0.2624165117740631, -0.07449331879615784, 0.3238042891025543, 0.7903711795806885, -0.38104119896888733, 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arm/illumicore-llama2-1k	arm	2023-12-14T03:51:21Z	8	1	[ "task_categories:text-generation", "task_categories:summarization", "size_categories:1K<n<10K", "language:en", "license:llama2", "region:us" ]	[ "text-generation", "summarization" ]	2023-12-07T04:38:29Z	--- language: - en license: llama2 size_categories: - 1K<n<10K task_categories: - text-generation - summarization pretty_name: IllumiCore-Llama2-1k dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 12515626 num_examples: 1000 download_size: 2510028 dataset_size: 12515626 configs: - config_name: default data_files: - split: train path: data/train-* --- # IllumiCore-1k: Llama2 Formatting This is a VNF resource allocation dataset (1000 samples) generated by IllumiCore [1], processed to match Llama 2's prompt format [2]: ``` <s>[INST] <<SYS>> {{ system_prompt }} <</SYS>> {{ user_msg_1 }} [/INST] {{ model_answer_1 }} </s><s>[INST] {{ user_msg_2 }} [/INST] {{ model_answer_1 }} </s> ``` Here is an example of a dataset record: ``` <s>[INST] <<SYS>> As a telecommunication realm expert with professional knowledge of network function virtualization and software-defined networks, your task is to assist the user in answering questions related to network functions virtualization infrastructure. Please provide clear and detailed explanations, drawing upon your expertise in the field to address the user's inquiries effectively. Your responses should be informative and accurate, catering to the user's specific questions and providing valuable insights into network function virtualization infrastructure. Ensure that your explanations are accessible to users with varying levels of familiarity with the subject matter, and feel free to incorporate relevant examples or analogies to enhance understanding. Here is the current NFVI topology tree: NFVI └── Core Switch ├── Data Center 1 │ ├── Aggregation Switch 1 │ │ ├── +---------------+ │ │ │ \| Top of Rack 1 \| │ │ │ +---------------+ │ │ ├── +---------------+ │ │ │ \| Top of Rack 2 \| │ │ │ +---------------+ │ │ └── +---------------+ │ │ \| Top of Rack 3 \| │ │ +---------------+ │ ├── Aggregation Switch 2 │ │ ├── +---------------+ │ │ │ \| Top of Rack 1 \| │ │ │ +---------------+ │ │ ├── +---------------+ │ │ │ \| Top of Rack 2 \| │ │ │ +---------------+ │ │ └── +---------------+ │ │ \| Top of Rack 3 \| │ │ +---------------+ │ ├── Rack 1 │ │ ├── Top of Rack 1 │ │ ├── ╔═══════════════╗ │ │ │ ║ Server 1 ║ │ │ │ ║ \| ║ │ │ │ ║ CH1 ║ │ │ │ ║ CPU: 48 Cores ║ │ │ │ ║ Mem: 408 GB ║ │ │ │ ║ Net: 1 GB ║ │ │ │ ╚═══════════════╝ │ │ ├── ╔═══════════════╗ │ │ │ ║ Server 2 ║ │ │ │ ║ \| ║ │ │ │ ║ CH2 ║ │ │ │ ║ CPU: 40 Cores ║ │ │ │ ║ Mem: 348 GB ║ │ │ │ ║ Net: 9 GB ║ │ │ │ ╚═══════════════╝ │ │ └── ╔═══════════════╗ │ │ ║ Server 3 ║ │ │ ║ \| ║ │ │ ║ CH3 ║ │ │ ║ CPU: 50 Cores ║ │ │ ║ Mem: 392 GB ║ │ │ ║ Net: 3 GB ║ │ │ ╚═══════════════╝ │ ├── Rack 2 │ │ ├── Top of Rack 2 │ │ ├── ╔═══════════════╗ │ │ │ ║ Server 4 ║ │ │ │ ║ \| ║ │ │ │ ║ CH4 ║ │ │ │ ║ CPU: 64 Cores ║ │ │ │ ║ Mem: 354 GB ║ │ │ │ ║ Net: 3 GB ║ │ │ │ ╚═══════════════╝ │ │ ├── ╔═══════════════╗ │ │ │ ║ Server 5 ║ │ │ │ ║ \| ║ │ │ │ ║ CH5 ║ │ │ │ ║ CPU: 48 Cores ║ │ │ │ ║ Mem: 408 GB ║ │ │ │ ║ Net: 5 GB ║ │ │ │ ╚═══════════════╝ │ │ └── ╔═══════════════╗ │ │ ║ Server 6 ║ │ │ ║ \| ║ │ │ ║ CH6 ║ │ │ ║ CPU: 54 Cores ║ │ │ ║ Mem: 206 GB ║ │ │ ║ Net: 1 GB ║ │ │ ╚═══════════════╝ │ └── Rack 3 │ ├── Top of Rack 3 │ ├── ╔═══════════════╗ │ │ ║ Server 7 ║ │ │ ║ \| ║ │ │ ║ CH7 ║ │ │ ║ CPU: 64 Cores ║ │ │ ║ Mem: 412 GB ║ │ │ ║ Net: 9 GB ║ │ │ ╚═══════════════╝ │ ├── ╔═══════════════╗ │ │ ║ Server 8 ║ │ │ ║ \| ║ │ │ ║ CH8 ║ │ │ ║ CPU: 40 Cores ║ │ │ ║ Mem: 314 GB ║ │ │ ║ Net: 5 GB ║ │ │ ╚═══════════════╝ │ └── ╔═══════════════╗ │ ║ Server 9 ║ │ ║ \| ║ │ ║ CH9 ║ │ ║ CPU: 60 Cores ║ │ ║ Mem: 402 GB ║ │ ║ Net: 3 GB ║ │ ╚═══════════════╝ └── Data Center 2 ├── Aggregation Switch 3 │ ├── +---------------+ │ │ \| Top of Rack 4 \| │ │ +---------------+ │ ├── +---------------+ │ │ \| Top of Rack 5 \| │ │ +---------------+ │ └── +---------------+ │ \| Top of Rack 6 \| │ +---------------+ ├── Aggregation Switch 4 │ ├── +---------------+ │ │ \| Top of Rack 4 \| │ │ +---------------+ │ ├── +---------------+ │ │ \| Top of Rack 5 \| │ │ +---------------+ │ └── +---------------+ │ \| Top of Rack 6 \| │ +---------------+ ├── Rack 4 │ ├── Top of Rack 4 │ ├── ╔═══════════════╗ │ │ ║ Server 10 ║ │ │ ║ \| ║ │ │ ║ CH10 ║ │ │ ║ CPU: 58 Cores ║ │ │ ║ Mem: 350 GB ║ │ │ ║ Net: 7 GB ║ │ │ ╚═══════════════╝ │ ├── ╔═══════════════╗ │ │ ║ Server 11 ║ │ │ ║ \| ║ │ │ ║ CH11 ║ │ │ ║ CPU: 46 Cores ║ │ │ ║ Mem: 198 GB ║ │ │ ║ Net: 5 GB ║ │ │ ╚═══════════════╝ │ └── ╔═══════════════╗ │ ║ Server 12 ║ │ ║ \| ║ │ ║ CH12 ║ │ ║ CPU: 58 Cores ║ │ ║ Mem: 234 GB ║ │ ║ Net: 1 GB ║ │ ╚═══════════════╝ ├── Rack 5 │ ├── Top of Rack 5 │ ├── ╔═══════════════╗ │ │ ║ Server 13 ║ │ │ ║ \| ║ │ │ ║ CH13 ║ │ │ ║ CPU: 42 Cores ║ │ │ ║ Mem: 348 GB ║ │ │ ║ Net: 7 GB ║ │ │ ╚═══════════════╝ │ ├── ╔═══════════════╗ │ │ ║ Server 14 ║ │ │ ║ \| ║ │ │ ║ CH14 ║ │ │ ║ CPU: 56 Cores ║ │ │ ║ Mem: 180 GB ║ │ │ ║ Net: 3 GB ║ │ │ ╚═══════════════╝ │ └── ╔═══════════════╗ │ ║ Server 15 ║ │ ║ \| ║ │ ║ CH15 ║ │ ║ CPU: 44 Cores ║ │ ║ Mem: 310 GB ║ │ ║ Net: 1 GB ║ │ ╚═══════════════╝ └── Rack 6 ├── Top of Rack 6 ├── ╔═══════════════╗ │ ║ Server 16 ║ │ ║ \| ║ │ ║ CH16 ║ │ ║ CPU: 50 Cores ║ │ ║ Mem: 254 GB ║ │ ║ Net: 1 GB ║ │ ╚═══════════════╝ ├── ╔═══════════════╗ │ ║ Server 17 ║ │ ║ \| ║ │ ║ CH17 ║ │ ║ CPU: 34 Cores ║ │ ║ Mem: 478 GB ║ │ ║ Net: 7 GB ║ │ ╚═══════════════╝ └── ╔═══════════════╗ ║ Server 18 ║ ║ \| ║ ║ CH18 ║ ║ CPU: 46 Cores ║ ║ Mem: 454 GB ║ ║ Net: 3 GB ║ ╚═══════════════╝ <</SYS>> Can you show me the current NFVI network topology diagram? [/INST] The Network Functions Virtualization Infrastructure (NFVI) consists of a core switch that connects two data centers. Data Center 1 comprises three racks, each containing servers with varying configurations. Each rack is connected to the network via two aggregation switches. The first rack houses three servers with different core counts, memory capacities, and network speeds. The same configuration exists for the second and third racks. Similarly, Data Center 2 is designed with the same architecture, hosting three more racks, each connected via two additional aggregation switches and containing three servers each. The servers across all racks display a diverse range of core counts, memory capacities, and network speeds, providing a robust and versatile NFVI. </s><s>[INST] Can I see a sample of the current VNF resource allocation? [/INST] The resource allocation results indicate that the Network Functions Virtualization Infrastructure Management (VIM) has allocated servers to top of rack (TOR) switches optimally. The servers 1 to 6, 7 to 12, and 13 to 18 are allocated to VIM 1, 2, and 3 respectively. Most servers remain unoccupied, except for servers 13 and 14. Server 13, with a total capacity of 42 CPU cores, 348 GB memory, and 7 GB network, has four virtual machines (VMs) assigned to it, consuming 36 cores, 99 GB memory, and 4 GB network. The remaining capacity is 6 cores, 249 GB memory, and 3 GB network. Server 14, with a total capacity of 56 cores, 180 GB memory, and 3 GB network, has two VMs assigned, consuming 25 cores, 71 GB memory, and 2 GB network. The remaining capacity is 31 cores, 109 GB memory, and 1 GB network. The allocation has achieved an optimal status, demonstrating efficient resource distribution. </s> ``` --- [1] L. Popokh, J. Su, S. Nair and E. Olinick, "IllumiCore: Optimization Modeling and Implementation for Efficient VNF Placement," 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Hvar, Croatia, 2021, pp. 1-7, doi: 10.23919/SoftCOM52868.2021.9559076. [2] [How to Prompt Llama 2](https://huggingface.co/blog/llama2#how-to-prompt-llama-2).	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ZeeshanChaudharee/example	ZeeshanChaudharee	2023-12-12T11:55:27Z	8	0	[ "license:apache-2.0", "region:us" ]	null	2023-12-07T13:01:26Z	--- license: apache-2.0 ---	[ -0.12853381037712097, -0.1861676573753357, 0.6529128551483154, 0.49436283111572266, -0.19319342076778412, 0.236074298620224, 0.3607199192047119, 0.05056328698992729, 0.579365611076355, 0.7400140166282654, -0.6508103609085083, -0.23783956468105316, -0.7102248668670654, -0.04782595485448837, -0.38947513699531555, 0.8470762968063354, -0.09598278254270554, 0.024005025625228882, 0.04711998254060745, -0.14317822456359863, -0.6121044754981995, -0.04771703481674194, -1.0524537563323975, -0.067874476313591, 0.3002280592918396, 0.5120974779129028, 0.8275896310806274, 0.39602935314178467, 0.5030564665794373, 1.7515555620193481, -0.08836941421031952, -0.2275443822145462, -0.45892053842544556, 0.42230653762817383, -0.33277368545532227, -0.42133697867393494, -0.26241710782051086, -0.07449347525835037, 0.3238036632537842, 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kpriyanshu256/semeval-task-8-b-v2-test-paraphrase-2-mistral-7b	kpriyanshu256	2023-12-11T15:29:04Z	8	0	[ "region:us" ]	null	2023-12-11T03:56:09Z	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: model dtype: string - name: source dtype: string - name: label dtype: int64 - name: id dtype: int64 - name: paraphrase dtype: string - name: paraphrase2 dtype: string - name: mistral-7b_estimated_loss dtype: float64 - name: mistral-7b_mean_lowest25 dtype: float64 - name: mistral-7b_mean_highest25 dtype: float64 - name: mistral-7b_max dtype: float64 - name: mistral-7b_min dtype: float64 - name: mistral-7b_range dtype: float64 - name: mistral-7b_mean dtype: float64 - name: mistral-7b_std dtype: float64 - name: mistral-7b_entropy dtype: float64 - name: mistral-7b_kurtosis dtype: float64 - name: mistral-7b_skewness dtype: float64 - name: mistral-7b_perplexity dtype: float64 splits: - name: test num_bytes: 11397023 num_examples: 3000 download_size: 5515131 dataset_size: 11397023 --- # Dataset Card for "semeval-task-8-b-v2-test-paraphrase-2-mistral-7b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.17120976746082306, -0.5018330812454224, 0.3529684543609619, 0.4793659746646881, -0.4657899737358093, -0.45434054732322693, 0.3704398572444916, 0.084353506565094, 0.5026667714118958, 0.8292046189308167, -0.6568887233734131, -0.5023559331893921, -0.7059800624847412, -0.06558289378881454, -0.4301186203956604, 1.1811065673828125, -0.13497917354106903, 0.13228584825992584, -0.4991161525249481, -0.2528664171695709, -0.4275023639202118, -0.502902626991272, -0.6663190722465515, -0.3773554265499115, 0.7649968862533569, 0.8466008305549622, 0.3850204348564148, 0.42881038784980774, 0.8633028268814087, 0.17374035716056824, 0.043276820331811905, -0.19627869129180908, -0.42361339926719666, 0.31415796279907227, 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DanKamNdi/law-test	DanKamNdi	2023-12-11T09:02:42Z	8	0	[ "task_categories:question-answering", "size_categories:n<1K", "language:en", "legal", "doi:10.57967/hf/1449", "region:us" ]	[ "question-answering" ]	2023-12-11T08:36:39Z	--- task_categories: - question-answering language: - en tags: - legal size_categories: - n<1K ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, -0.06787490844726562, 0.3002279996871948, 0.5120974779129028, 0.8275896310806274, 0.3960292339324951, 0.5030564665794373, 1.7515558004379272, -0.08836928009986877, -0.22754442691802979, 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kardosdrur/dawiki_title_content	kardosdrur	2023-12-11T10:00:44Z	8	0	[ "region:us" ]	null	2023-12-11T09:58:04Z	--- dataset_info: features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 375216220.38822436 num_examples: 559975 - name: test num_bytes: 93804222.61177567 num_examples: 139994 download_size: 316070037 dataset_size: 469020443.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, -0.06787490844726562, 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yuasosnin/imdb-dpo	yuasosnin	2023-12-11T11:32:25Z	8	0	[ "region:us" ]	null	2023-12-11T11:32:19Z	--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: chosen_reward dtype: float32 - name: rejected_reward dtype: float32 splits: - name: train num_bytes: 5978519.1 num_examples: 9000 - name: test num_bytes: 664279.9 num_examples: 1000 download_size: 3876556 dataset_size: 6642799.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---	[ -0.12853379547595978, -0.1861676573753357, 0.6529129147529602, 0.49436256289482117, -0.1931929588317871, 0.2360745668411255, 0.3607199192047119, 0.05056329071521759, 0.5793652534484863, 0.7400139570236206, -0.6508101224899292, -0.2378396987915039, -0.7102251052856445, -0.047825805842876434, -0.3894752264022827, 0.8470761775970459, -0.09598271548748016, 0.024005070328712463, 0.04712017625570297, -0.14317847788333893, 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dputilov/generatemidas24ai	dputilov	2023-12-11T21:12:15Z	8	0	[ "region:us" ]	null	2023-12-11T20:39:20Z	--- dataset_info: features: - name: image dtype: image - name: conditioning_image dtype: image - name: mask dtype: image - name: text dtype: string splits: - name: train num_bytes: 2390531232.212 num_examples: 8682 download_size: 3277241869 dataset_size: 2390531232.212 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "generatemidas24ai" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.714856743812561, -0.018926497548818588, 0.055540114641189575, 0.3858075439929962, -0.1824895292520523, 0.07016066461801529, 0.3842984139919281, -0.05108443647623062, 0.9170048236846924, 0.4012851119041443, -1.1852575540542603, -0.4855918884277344, -0.5349417328834534, -0.2140825092792511, -0.09330251067876816, 1.2882449626922607, 0.06290911883115768, 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faizalnf1800/SciFi_Stories_Text_Corpus	faizalnf1800	2023-12-12T13:37:31Z	8	0	[ "region:us" ]	null	2023-12-12T12:38:09Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 149918239 num_examples: 207993 download_size: 95836490 dataset_size: 149918239 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, -0.06787490844726562, 0.3002279996871948, 0.5120974779129028, 0.8275896310806274, 0.3960292339324951, 0.5030564665794373, 1.7515558004379272, 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zwr-shu/docreICL	zwr-shu	2023-12-12T13:51:50Z	8	0	[ "license:apache-2.0", "region:us" ]	null	2023-12-12T13:45:09Z	--- license: apache-2.0 ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, -0.06787490844726562, 0.3002279996871948, 0.5120974779129028, 0.8275896310806274, 0.3960292339324951, 0.5030564665794373, 1.7515558004379272, -0.08836928009986877, -0.22754442691802979, -0.4589202404022217, 0.42230692505836487, -0.33277368545532227, -0.4213372468948364, -0.26241639256477356, -0.07449338585138321, 0.32380396127700806, 0.7903713583946228, 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TeamSODA/ae-awgn_commonvoice	TeamSODA	2023-12-13T05:33:44Z	8	0	[ "region:us" ]	null	2023-12-12T14:11:31Z	--- dataset_info: features: - name: audio_1 dtype: audio - name: audio_0 dtype: audio - name: snr dtype: int64 splits: - name: SNR_0 num_bytes: 41266161.0 num_examples: 50 - name: SNR_5 num_bytes: 41266161.0 num_examples: 50 - name: SNR_10 num_bytes: 41266111.0 num_examples: 50 - name: SNR_20 num_bytes: 41266111.0 num_examples: 50 - name: SNR_30 num_bytes: 41266111.0 num_examples: 50 download_size: 206321474 dataset_size: 206330655.0 configs: - config_name: default data_files: - split: SNR_0 path: data/SNR_0-* - split: SNR_5 path: data/SNR_5-* - split: SNR_10 path: data/SNR_10-* - split: SNR_20 path: data/SNR_20-* - split: SNR_30 path: data/SNR_30-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, 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Hunzla/xls-r-crema-d-300-each-train	Hunzla	2023-12-13T09:48:12Z	8	0	[ "region:us" ]	null	2023-12-13T09:47:53Z	--- dataset_info: features: - name: emotionG_label dtype: string - name: gender dtype: string - name: emotion dtype: string - name: path dtype: string - name: label sequence: int64 - name: input_values sequence: sequence: float32 - name: input_length dtype: int64 splits: - name: train num_bytes: 415072955 num_examples: 1296 download_size: 367342063 dataset_size: 415072955 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, 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prhegde/preference-data-math-stack-exchange	prhegde	2023-12-13T10:16:15Z	8	1	[ "license:apache-2.0", "region:us" ]	null	2023-12-13T10:07:23Z	--- license: apache-2.0 --- The preference dataset is derived from the [stack exchange dataset](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences) which contains questions and answers from the Stack Overflow Data Dump. This contains questions and answers for various topics. For this work, we used only question and answers from [math.stackexchange.com](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences/tree/main/data/math.meta.stackexchange.com) sub-folder. The questions are grouped with answers that are assigned a score corresponding to the Anthropic paper: ``` score = log2 (1 + upvotes) rounded to the nearest integer, plus 1 if the answer was accepted by the questioner (we assign a score of −1 if the number of upvotes is negative). ``` We performed following processing to derive the final dataset. 1) Basic pre-processing ([code](https://github.com/PraveenSH/dpo-arithmo-mistral-7B/blob/main/src/data_processing/stack_exchange_data.py)) to clean the text 2) Filter Mathematical question using regex based detector ([code](https://github.com/PraveenSH/dpo-arithmo-mistral-7B/blob/main/src/data_processing/stack_exchange_data.py)) 3) For each question, extract 2 answers - one with highest score and one with the lowest score. Former is used as Preferred response and latter is used as the rejected response ## References ``` @online{h4stackexchange, author = {Lambert, Nathan and Tunstall, Lewis and Rajani, Nazneen and Thrush, Tristan}, title = {HuggingFace H4 Stack Exchange Preference Dataset}, year = 2023, url = {https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences}, } ```	[ -0.8096835613250732, -0.6178295016288757, 0.42802223563194275, 0.10220669955015182, -0.0157863087952137, -0.2096327245235443, -0.1977749913930893, -0.3788903057575226, 0.644934356212616, 0.5563100576400757, -0.6205384135246277, -0.39144182205200195, -0.37758901715278625, 0.36909711360931396, 0.08167190104722977, 1.5505428314208984, -0.11757634580135345, -0.32464465498924255, -0.399117648601532, -0.23037877678871155, 0.03256755322217941, -0.5049300193786621, -0.33443644642829895, -0.2401619255542755, 0.3628541827201843, 0.5935389995574951, 0.6104875802993774, 0.6285938024520874, 0.36321908235549927, 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Seongill/Trivia_missing_5_full_substitution	Seongill	2023-12-13T10:24:10Z	8	0	[ "region:us" ]	null	2023-12-13T10:24:04Z	--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: id dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: has_answer dtype: bool - name: random_sub dtype: string - name: similar_sub dtype: string - name: ent_type dtype: string splits: - name: train num_bytes: 41367743 num_examples: 11313 download_size: 25016105 dataset_size: 41367743 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.12853401899337769, -0.1861673891544342, 0.6529126763343811, 0.4943625330924988, -0.19319301843643188, 0.23607464134693146, 0.3607196807861328, 0.05056333541870117, 0.5793654322624207, 0.740013837814331, -0.6508100628852844, -0.23783957958221436, -0.7102248668670654, -0.04782595857977867, 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Seongill/Trivia_5_only_adversary_1159	Seongill	2023-12-13T10:39:48Z	8	0	[ "region:us" ]	null	2023-12-13T10:37:15Z	--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: has_answer dtype: bool - name: similar_sub dtype: string - name: ctxs list: - name: answer_sent sequence: string - name: hasanswer dtype: bool - name: id dtype: string - name: is_adv dtype: bool - name: new_answer_sent dtype: string - name: original_text dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: num_advs dtype: int64 splits: - name: train num_bytes: 7404322 num_examples: 1159 download_size: 3197268 dataset_size: 7404322 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, 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krishan-CSE/Unified_Dataset_BERT	krishan-CSE	2023-12-13T13:19:45Z	8	0	[ "license:apache-2.0", "region:us" ]	null	2023-12-13T12:20:15Z	--- license: apache-2.0 ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, -0.06787490844726562, 0.3002279996871948, 0.5120974779129028, 0.8275896310806274, 0.3960292339324951, 0.5030564665794373, 1.7515558004379272, -0.08836928009986877, -0.22754442691802979, -0.4589202404022217, 0.42230692505836487, -0.33277368545532227, -0.4213372468948364, -0.26241639256477356, -0.07449338585138321, 0.32380396127700806, 0.7903713583946228, 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Erynan/eval_util_100	Erynan	2023-12-13T22:05:10Z	8	0	[ "region:us" ]	null	2023-12-13T22:05:09Z	--- dataset_info: features: - name: prompt dtype: string - name: response_a dtype: string - name: response_b dtype: string - name: more_reasonable dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 23557 num_examples: 100 download_size: 15532 dataset_size: 23557 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, -0.06787490844726562, 0.3002279996871948, 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Yunij/testing_kaggle_ai_detection_dataset	Yunij	2023-12-14T06:21:30Z	8	0	[ "license:apache-2.0", "region:us" ]	null	2023-12-14T05:43:14Z	--- license: apache-2.0 dataset_info: features: - name: prompt dtype: string - name: text dtype: string - name: source dtype: string - name: label dtype: int64 - name: perplexity dtype: float64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 1244922869 num_examples: 330345 - name: test num_bytes: 65575515 num_examples: 17387 download_size: 435690981 dataset_size: 1310498384 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 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Tippawan/test2-data-semi-trainulb-r3	Tippawan	2023-12-14T09:54:31Z	8	0	[ "region:us" ]	null	2023-12-14T09:54:25Z	--- dataset_info: features: - name: tokens sequence: string - name: ner_tags sequence: int64 - name: prob sequence: float64 - name: ifpass sequence: int64 - name: pred dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 202202230 num_examples: 41738 download_size: 31409433 dataset_size: 202202230 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, 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ScandEval/cnn-dailymail-mini	ScandEval	2023-12-14T10:09:01Z	8	0	[ "region:us" ]	null	2023-12-14T10:08:56Z	--- dataset_info: features: - name: text dtype: string - name: target_text dtype: string - name: id dtype: string splits: - name: train num_bytes: 4443630 num_examples: 1024 - name: val num_bytes: 1104312 num_examples: 256 - name: test num_bytes: 8831952 num_examples: 2048 download_size: 8836565 dataset_size: 14379894 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, 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huggingface/autotrain-data-8xid-g6rv-rkuc	huggingface	2023-12-14T23:04:02Z	8	0	[ "region:us" ]	null	2023-12-14T23:04:00Z	Invalid username or password.	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huggingface/autotrain-data-8xid-g6rv-rkuc2	huggingface	2023-12-14T23:08:28Z	8	0	[ "region:us" ]	null	2023-12-14T23:08:26Z	Invalid username or password.	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huggingface/autotrain-data-8xid-g6rv-rkuc5	huggingface	2023-12-14T23:19:04Z	8	0	[ "region:us" ]	null	2023-12-14T23:19:02Z	Invalid username or password.	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huggingface/autotrain-data-lw1q-vmpq-ylvw2	huggingface	2023-12-14T23:48:40Z	8	0	[ "region:us" ]	null	2023-12-14T23:48:37Z	Invalid username or password.	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huggingface/autotrain-data-4v9x-3cwh-jsa8	huggingface	2023-12-15T01:54:00Z	8	0	[ "region:us" ]	null	2023-12-15T01:53:31Z	Invalid username or password.	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huggingface/autotrain-data-4v9x-3cwh-jsa81	huggingface	2023-12-15T02:04:38Z	8	0	[ "region:us" ]	null	2023-12-15T02:04:09Z	Invalid username or password.	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huggingface/autotrain-data-yrsq-dnj7-jghjk3	huggingface	2023-12-15T02:33:37Z	8	0	[ "region:us" ]	null	2023-12-15T02:33:35Z	Invalid username or password.	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Raziullah/asr_finetune_cv_13_dv	Raziullah	2023-12-15T06:38:03Z	8	0	[ "license:unknown", "region:us" ]	null	2023-12-15T06:31:48Z	--- license: unknown dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string splits: - name: train num_bytes: 167429327.552 num_examples: 4904 - name: test num_bytes: 88593702.704 num_examples: 2212 download_size: 262021485 dataset_size: 256023030.25599998 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---	[ -0.12853378057479858, -0.18616795539855957, 0.6529127359390259, 0.49436259269714355, -0.1931932121515274, 0.23607474565505981, 0.36071982979774475, 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Mursel/mlsum-tr-gpt2-tokenized	Mursel	2023-12-15T18:41:15Z	8	0	[ "region:us" ]	null	2023-12-15T18:39:31Z	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 5314170808 num_examples: 398842 - name: test num_bytes: 1328562688 num_examples: 99712 download_size: 456145997 dataset_size: 6642733496 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, 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Jiahuan/teach_action	Jiahuan	2023-12-15T21:26:13Z	8	0	[ "region:us" ]	null	2023-12-15T21:26:03Z	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 605833565 num_examples: 231272 - name: validation num_bytes: 76965684 num_examples: 28960 - name: test num_bytes: 208815535 num_examples: 91903 download_size: 65220573 dataset_size: 891614784 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, 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adamzinebi/mmm_track_lmd_8bars_nots	adamzinebi	2023-12-16T13:30:35Z	8	0	[ "region:us" ]	null	2023-12-15T21:35:59Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 74262767 num_examples: 3764 download_size: 12045427 dataset_size: 74262767 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.12853394448757172, -0.1861674040555954, 0.6529132127761841, 0.49436259269714355, -0.19319306313991547, 0.2360745519399643, 0.36071985960006714, 0.050563570111989975, 0.5793654918670654, 0.7400139570236206, -0.6508103013038635, -0.23783987760543823, -0.7102250456809998, -0.04782596975564957, -0.3894755244255066, 0.8470767140388489, -0.09598255157470703, 0.024004653096199036, 0.0471201092004776, -0.14317835867404938, -0.6121037006378174, -0.04771704971790314, -1.0524539947509766, -0.06787478178739548, 0.3002283573150635, 0.5120974779129028, 0.8275898694992065, 0.3960292637348175, 0.5030567646026611, 1.7515560388565063, 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MohamedRashad/arabic-billion-words	MohamedRashad	2023-12-16T02:47:39Z	8	0	[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:ar", "region:us" ]	[ "text-generation" ]	2023-12-16T01:01:47Z	--- language: - ar size_categories: - 1M<n<10M task_categories: - text-generation pretty_name: Arabic Billion Words dataset_info: features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 17017097763 num_examples: 5222964 download_size: 8050780387 dataset_size: 17017097763 configs: - config_name: default data_files: - split: train path: data/train-* --- # Arabic Billion Words Dataset 🌕 The Abu El-Khair Arabic News Corpus (arabic-billion-words) is a comprehensive collection of Arabic text, encompassing over five million newspaper articles. The corpus is rich in linguistic diversity, containing more than a billion and a half words, with approximately three million unique words. The text is encoded in two formats: UTF-8 and Windows CP-1256, and marked up using two markup languages: SGML and XML. ## Data Example An example from the "Almasryalyoum" configuration subset: ```python { "url": "http://today.almasryalyoum.com/printerfriendly.aspx?ArticleID=61300", "head_line": "رئيس وزراء المجر: عنصرية جماهير أوجبيست جلبت العار للبلاد", "date": "19/5/2007", "text": """قال متحدث باسم الحكومة المجرية: إن رئيس الوزراء فيرنك جيوركساني رحب بقرار اتحاد كرة القدم المجري بخصم ثلاث نقاط من نادي أوجبيست بسبب السلوك العنصري الذي صدر من جماهيره. ... يذكر أن المجر بها مجموعة من مشجعي كرة القدم المشاغبين «الهوليجانز»، وشارك الكثير منهم في أعمال شغب معادية للحكومة في العام الماضي.""", } ``` ## Data Fields The dataset comprises the following fields: - "url": string, original URL of the article. - "head_line": string, headline of the article. - "date": string, date of the article. - "text": string, textual content of the article. ## Data Splits The dataset is divided into a single "training" split, with the following distribution of examples across different configuration subsets: \| Configuration Subset \| Number of Examples \| \|:---------------------\|-------------------:\| \| Alittihad \| 349,342 \| \| Almasryalyoum \| 291,723 \| \| Almustaqbal \| 446,873 \| \| Alqabas \| 817,274 \| \| Echoroukonline \| 139,732 \| \| Ryiadh \| 858,188 \| \| Sabanews \| 92,149 \| \| SaudiYoum \| 888,068 \| \| Techreen \| 314,597 \| \| Youm7 \| 1,172,136 \| ## Dataset Usage This dataset is valuable for natural language processing (NLP) tasks, including but not limited to sentiment analysis, topic modeling, and language modeling in the context of Arabic news articles. Researchers and practitioners in the field of NLP can benefit from the diverse linguistic content and the substantial size of the corpus.	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silk-road/Embedding-Adapter	silk-road	2023-12-17T07:33:14Z	8	0	[ "license:apache-2.0", "region:us" ]	null	2023-12-17T07:21:10Z	--- license: apache-2.0 ---	[ -0.12853389978408813, -0.18616773188114166, 0.6529126167297363, 0.49436259269714355, -0.19319310784339905, 0.2360745072364807, 0.36071985960006714, 0.050563156604766846, 0.579365611076355, 0.7400138974189758, -0.6508100628852844, -0.23783960938453674, -0.7102248072624207, -0.04782594367861748, -0.38947513699531555, 0.8470762372016907, -0.09598279744386673, 0.02400519698858261, 0.04712005704641342, -0.14317844808101654, -0.6121039390563965, -0.04771699011325836, -1.0524537563323975, -0.06787476688623428, 0.3002280294895172, 0.5120972990989685, 0.8275895118713379, 0.3960295021533966, 0.5030564069747925, 1.7515560388565063, -0.08836917579174042, -0.22754418849945068, -0.45892053842544556, 0.42230671644210815, -0.33277350664138794, -0.42133697867393494, -0.26241645216941833, -0.07449360191822052, 0.32380378246307373, 0.790371298789978, 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justinian336/salvadoran-news-elmundo	justinian336	2023-12-18T01:04:55Z	7	0	[ "region:us" ]	null	2023-02-15T14:38:15Z	--- dataset_info: features: - name: image_src dtype: string - name: title dtype: string - name: content dtype: string - name: category dtype: class_label: names: '0': El Mundo '1': Tecnomundo '2': Nacionales '3': Guia Mundialista '4': Economia '5': Editorial '6': Confidencial '7': Politica - name: date dtype: string - name: link dtype: string splits: - name: train num_bytes: 89486722 num_examples: 45858 download_size: 49745848 dataset_size: 89486722 --- # Dataset Card for "salvadoran-news-elmundo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5392752289772034, -0.15636655688285828, 0.19137153029441833, 0.48532333970069885, -0.5098780989646912, -0.013641538098454475, -0.1886317878961563, -0.5024244785308838, 1.0069634914398193, 0.440297394990921, -0.73790043592453, 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projecte-aina/CoQCat	projecte-aina	2023-12-12T13:27:41Z	7	0	[ "task_categories:conversational", "task_categories:question-answering", "task_ids:dialogue-generation", "task_ids:extractive-qa", "task_ids:closed-domain-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:ca", "license:cc-by-nc-nd-4.0", "region:us" ]	[ "conversational", "question-answering" ]	2023-09-12T15:42:48Z	--- annotations_creators: - expert-generated language: - ca language_creators: - found license: - cc-by-nc-nd-4.0 multilinguality: - monolingual pretty_name: CoQCat - Conversational Question Answering in Catalan size_categories: - 10K<n<100K source_datasets: [] tags: [] task_categories: - conversational - question-answering task_ids: - dialogue-generation - extractive-qa - closed-domain-qa --- # Dataset Card for CoQCat ## 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: - Repository: - Paper: - Leaderboard: - Point of Contact: [email protected] ### Dataset Summary CoQCat is a dataset for Conversational Question Answering in Catalan. It is based on [CoQA dataset](https://stanfordnlp.github.io/coqa/). CoQCat comprises 89,364 question-answer pairs, sourced from conversations related to 6,000 text passages from six different domains. The questions and responses are designed to maintain a conversational tone. The answers are presented in a free-form text format, with evidence highlighted from the passage. For the development and test sets, an additional 2 responses to each question have been collected. This work is licensed under a [CC BY-NC-ND 4.0 International License](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en_GB). ### Supported Tasks and Leaderboards Conversational models, question answering. ### Languages The dataset is in Catalan (ca-ES). ## Dataset Structure ### Data Instances Three JSON files, one for each split. An example of 'test' looks as follows: ``` { "source": "petites_histories", "id": "petites_histories_100", "filename": "coqcat_batch1", "title": "Superant la crisi dels 40", "story": "“Què collons estic fent aquí” es preguntà, “…fotent bots i suant a raig als meus quaranta tacos en una classe de body-step. Si no he estat mai un amant del gimnàs! Tot això és culpa del club de ‘singles', els desaparellats desesperats per trobar algú que ens escolti una mica, mentre calculem les possibilitats de fotre un clau”. Pensant tot això es va descomptar i anava a contratemps, un altre cop.\nHo acaba de decidir: sortint de la classe es donarà de baixa. Sí, serà millor oblidar els ‘singles' i dedicar els caps de setmana que no tingui les nenes a fer sortides amb el seu germà, els nebots són més grans que les seves filles i ja fan bones excursions.\nUn cop dutxat s'apropà al mostrador de l'entrada. El noi el va fer passar a l'oficina per omplir el full de baixa. Dins, una veu el va cridar: “Txume?”. Es girà estranyat ja que li deien així només a l'institut, després va ser el Jaume. Va mirar la dona que li somreia.:\n-“Estàs igual que fa vint-i-cinc anys! Has fet un pacte amb el diable?”\n-“… Alba?? Ets l'Alba?”- la seva cara li recordava a la de la noia grassoneta de la classe, però ara no ho estava pas de grassa! -“Tu sí que hi has pactat! Estàs molt millor… bé, vull dir que…”\n-“Sí, em vaig posar les piles.”\n-“Segueixes escrivint relats? Recordo que a tots ens agradaven les històries de l'Alba.”\n-“Doncs encara ho faig, just ara en plegar vaig a una sessió literària musical. Si vols venir, podem recordar batalletes.”\nS'hi apuntà. Van xerrar, van riure, van escoltar relats i poemes, van reviure les aventures de l'institut amb música de jazz de fons, … Hi estava a gust, i s'adonà que, per primer cop en molt temps, no avaluava les possibilitats d'acabar prenent la darrera copa a casa seva.", "questions": [ {"input_text": "Està satisfet del que està fent?","turn_id": 1}, {"input_text": "Sua?","turn_id": 2}, {"input_text": "Quants anys té?","turn_id": 3}, {"input_text": "Li agrada anar al gimnàs?","turn_id": 4}, {"input_text": "Està solter?","turn_id": 5}, {"input_text": "Segueix bé el ritme de la música?","turn_id": 6}, {"input_text": "Continuarà apuntat al gimnàs?","turn_id": 7}, {"input_text": "Què farà els caps de setmana?","turn_id": 8}, {"input_text": "On li deien Txume?","turn_id": 9}, {"input_text": "Com l'anomenaven després?","turn_id": 10}, {"input_text": "En Jaume ha envellit bé?","turn_id": 11}, {"input_text": "L'Alba s'havia aprimat?","turn_id": 12}, {"input_text": "A què es dedica l'Alba?","turn_id": 13}, {"input_text": "On van anar plegats?","turn_id": 14}, {"input_text": "Volia que acabessin anant a la seva llar?","turn_id": 15} ], "answers": [ {"input_text": "No.", "span_text": "Què collons estic fent aquí", "span_start": 1, "span_end": 28,"turn_id": 1}, {"input_text": "Sí, a raig.", "span_text": "fotent bots i suant a raig", "span_start": 45, "span_end": 71,"turn_id": 2}, {"input_text": "Quaranta.", "span_text": "als meus quaranta tacos", "span_start": 72, "span_end": 95,"turn_id": 3}, {"input_text": "No.", "span_text": "Si no he estat mai un amant del gimnàs", "span_start": 124, "span_end": 162,"turn_id": 4}, {"input_text": "Sí.", "span_text": "Tot això és culpa del club de ‘singles'", "span_start": 164, "span_end": 203,"turn_id": 5}, {"input_text": "No.", "span_text": "es va descomptar i anava a contratemps, un altre cop", "span_start": 347, "span_end": 399,"turn_id": 6}, {"input_text": "No, quan surti de la classe es donarà de baixa.", "span_text": "sortint de la classe es donarà de baixa", "span_start": 422, "span_end": 461,"turn_id": 7}, {"input_text": "Farà sortides amb el seu germà i els seus nebots.", "span_text": "dedicar els caps de setmana que no tingui les nenes a fer sortides amb el seu germà, els nebots són més grans que les seves filles i ja fan bones excursions", "span_start": 503, "span_end": 659,"turn_id": 8}, {"input_text": "A l'institut.", "span_text": "Dins, una veu el va cridar: “Txume?”. Es girà estranyat ja que li deien així només a l'institut", "span_start": 776, "span_end": 871,"turn_id": 9}, {"input_text": "Jaume.", "span_text": "que li deien així només a l'institut, després va ser el Jaume", "span_start": 835, "span_end": 896,"turn_id": 10}, {"input_text": "Sí.", "span_text": "Estàs igual que fa vint-i-cinc anys", "span_start": 934, "span_end": 969,"turn_id": 11}, {"input_text": "Sí.", "span_text": "però ara no ho estava pas de grassa", "span_start": 1096, "span_end": 1131,"turn_id": 12}, {"input_text": "Escriu relats.", "span_text": "Segueixes escrivint relats? Recordo que a tots ens agradaven les històries de l'Alba.”\n-“Doncs encara ho faig", "span_start": 1232, "span_end": 1341,"turn_id": 13}, {"input_text": "A una sessió literària musical.", "span_text": "just ara en plegar vaig a una sessió literària musical. Si vols venir, podem recordar batalletes.”\nS'hi apuntà", "span_start": 1343, "span_end": 1453,"turn_id": 14}, {"input_text": "No.", "span_text": "no avaluava les possibilitats d'acabar prenent la darrera copa a casa seva", "span_start": 1641, "span_end": 1715,"turn_id": 15} ], "additional_answers": { "0": [ {"input_text": "No.", "span_text": "Què collons estic fent aquí", "span_start": 1, "span_end": 28, "turn_id": 1}, {"input_text": "Sí, molt.", "span_text": "fotent bots i suant a raig", "span_start": 45, "span_end": 71, "turn_id": 2}, {"input_text": "Quaranta.", "span_text": "als meus quaranta tacos", "span_start": 72, "span_end": 95, "turn_id": 3}, {"input_text": "No.", "span_text": "Si no he estat mai un amant del gimnàs", "span_start": 124, "span_end": 162, "turn_id": 4}, {"input_text": "Sí.", "span_text": "Tot això és culpa del club de ‘singles'", "span_start": 164, "span_end": 203, "turn_id": 5}, {"input_text": "No.", "span_text": "es va descomptar i anava a contratemps, un altre cop", "span_start": 347, "span_end": 399, "turn_id": 6}, {"input_text": "No.", "span_text": "sortint de la classe es donarà de baixa", "span_start": 422, "span_end": 461, "turn_id": 7}, {"input_text": "Farà sortides amb el seu germà i els seus nebots.", "span_text": "serà millor oblidar els ‘singles' i dedicar els caps de setmana que no tingui les nenes a fer sortides amb el seu germà, els nebots són més grans que les seves filles i ja fan bones excursions", "span_start": 467, "span_end": 659, "turn_id": 8}, {"input_text": "A l'institut.", "span_text": "“Txume?”. Es girà estranyat ja que li deien així només a l'institut", "span_start": 804, "span_end": 871, "turn_id": 9}, {"input_text": "Jaume.", "span_text": "després va ser el Jaume", "span_start": 873, "span_end": 896, "turn_id": 10}, {"input_text": "Sí.", "span_text": "Estàs igual que fa vint-i-cinc anys", "span_start": 934, "span_end": 969, "turn_id": 11}, {"input_text": "Sí.", "span_text": "però ara no ho estava pas de grassa", "span_start": 1096, "span_end": 1131, "turn_id": 12}, {"input_text": "Escriu relats.", "span_text": "Segueixes escrivint relats? Recordo que a tots ens agradaven les històries de l'Alba.”\n-“Doncs encara ho faig", "span_start": 1232, "span_end": 1341, "turn_id": 13}, {"input_text": "A una sessió literària musical.", "span_text": "just ara en plegar vaig a una sessió literària musical. Si vols venir, podem recordar batalletes.”\nS'hi apuntà", "span_start": 1343, "span_end": 1453, "turn_id": 14}, {"input_text": "No.", "span_text": "no avaluava les possibilitats d'acabar prenent la darrera copa a casa seva", "span_start": 1641, "span_end": 1715, "turn_id": 15} ], "1": [ {"input_text": "No.", "span_text": "Què collons estic fent aquí", "span_start": 1, "span_end": 28, "turn_id": 1}, {"input_text": "Sí.", "span_text": "fotent bots i suant", "span_start": 45, "span_end": 64, "turn_id": 2}, {"input_text": "Quaranta.", "span_text": "als meus quaranta tacos", "span_start": 72, "span_end": 95, "turn_id": 3}, {"input_text": "No.", "span_text": "Si no he estat mai un amant del gimnàs", "span_start": 124, "span_end": 162, "turn_id": 4}, {"input_text": "Sí.", "span_text": "Tot això és culpa del club de ‘singles', els desaparellats desesperats per trobar algú que ens escolti una mica", "span_start": 164, "span_end": 275, "turn_id": 5}, {"input_text": "No.", "span_text": "es va descomptar i anava a contratemps, un altre cop", "span_start": 347, "span_end": 399, "turn_id": 6}, {"input_text": "No.", "span_text": "sortint de la classe es donarà de baixa", "span_start": 422, "span_end": 461, "turn_id": 7}, {"input_text": "A fer sortides amb el seu germà.", "span_text": "serà millor oblidar els ‘singles' i dedicar els caps de setmana que no tingui les nenes a fer sortides amb el seu germà", "span_start": 467, "span_end": 586, "turn_id": 8}, {"input_text": "A l'institut.", "span_text": "una veu el va cridar: “Txume?”. Es girà estranyat ja que li deien així només a l'institut", "span_start": 782, "span_end": 871, "turn_id": 9}, {"input_text": "Jaume.", "span_text": "després va ser el Jaume", "span_start": 873, "span_end": 896, "turn_id": 10}, {"input_text": "Sí.", "span_text": "Estàs igual que fa vint-i-cinc anys", "span_start": 934, "span_end": 969, "turn_id": 11}, {"input_text": "Sí.", "span_text": "però ara no ho estava pas de grassa", "span_start": 1096, "span_end": 1131, "turn_id": 12}, {"input_text": "Escriu relats.", "span_text": "Segueixes escrivint relats? Recordo que a tots ens agradaven les històries de l'Alba.”\n-“Doncs encara ho faig", "span_start": 1232, "span_end": 1341, "turn_id": 13}, {"input_text": "A una sessió literària musical.", "span_text": "una sessió literària musical. Si vols venir, podem recordar batalletes.”\nS'hi apuntà", "span_start": 1369, "span_end": 1453, "turn_id": 14}, {"input_text": "No.", "span_text": "no avaluava les possibilitats d'acabar prenent la darrera copa a casa seva", "span_start": 1641, "span_end": 1715, "turn_id": 15} ] } } ``` ### Data Fields The data fields are the same among all splits. - `source`: a `string` feature. - `id`: a `string` feature. - `filename`: a `string` feature. - `story`: a `string` feature. - `questions`: a `list` of dictionaries containing: - `input_text`: a `string` feature. - `turn_id`: a `int32` feature. - `answers`: a `list` of dictionaries containing: - `input_text`: a `string` feature. - `span_text`: a `string` feature. - `answer_start`: a `int32` feature. - `answer_end`: a `int32` feature. - `turn_id`: a `int32` feature. - `additional_answers` (only in `dev`and `test`): a dictionary feature containing: - `0`: a `list` of dictionaries equal to `answers`. - `1`: a `list` of dictionaries equal to `answers`. ### Data Splits * dev.json: 8,909 question-answering examples. 600 text passages from 4 domains * test.json: 8,986 question-answering examples. 600 text passages from 6 domains * train.json: 71,489 question-answering examples. 4800 text passages from 4 domains ## Dataset Creation ### Curation Rationale We created this dataset to contribute to the development of language models in Catalan, a low-resource language. ### Source Data #### Initial Data Collection and Normalization We obtained the initial text passages from different sources, depending on the domain: \| domain \| source \| train \| dev \| test \| total \| \|:-\|:-\|-:\|-:\|-:\|-:\| \| biographies \| [Catalan wikipedia](ca.wikipedia.org) \| 1200 \| 150 \| 100 \| 1450\| \| literature \| [Gutenberg Project](gutenberg.org/) \| 1200 \| 150 \| 100 \| 1450\| \| news \| [VilaWeb](vilaweb.cat) \| 1200 \| 150 \| 100 \| 1450\| \| mitology \| Catalan wikipedia \| 1200 \| 150 \| 100 \| 1450\| \| short histories \| [Petites històries](https://petiteshistories.wordpress.com/) \| 0 \| 0 \| 100 \| 100\| \| movie plots \| Catalan wikipedia \| 0 \| 0 \| 100 \| 100 \| \| TOTAL \| \| 4800 \| 600 \| 600 \| 6000 \| #### Who are the source language producers? The contents of [Catalan Wikipedia](ca.wikipedia.org) are developed by a team of volunteers, and are subject to review processes also carried out by volunteers. The texts from [VilaWeb](vilaweb.cat) are prepared by journalists and communication professionals, and edited by expert proofreaders. The paragraphes extracted from the [Gutenberg Project](gutenberg.org/) are written by diferent Catalan authors. The texts of the [Petites històries website](https://petiteshistories.wordpress.com/) are written by the writer M. Carme Marí and published on her website. The author has provided them for the preparation of this dataset. ### Annotations #### Annotation process The annotation process was entrusted to the company [M47 labs](https://www.m47labs.com/) through a public tender process. We asked to organize the annotators team in pairs: one of the members asking questions from the text passage and the other one answering them. For the elaboration of addicional answers for the dev and test splits, we asked the participation of a third person, not involved in the original conversation. Annotation guidelines can be found in [Zenodo](https://zenodo.org/records/10362295). #### Who are the annotators? Annotation was entrusted to the company [M47 labs](https://www.m47labs.com/) through a public tender process. We asked for a team of at least 4 annotators, students or graduates of universities, ideally in the field of Humanities or Social Sciences, with optimal demonstrable knowledge of the Catalan language (minimum level C1, or equivalent), and a senior coordinator with proven experience in management and coordination of text annotation. ### Personal and Sensitive Information No personal or sensitive information included. ## Considerations for Using the Data ### Social Impact of Dataset We hope that this dataset will help the development of virtual assistants in Catalan, a language that is often not taken into account. ### Discussion of Biases [N/A] ### Other Known Limitations [N/A] ## Additional Information ### Dataset Curators Language Technologies Unit at the Barcelona Supercomputing Center ([email protected]) 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/ca/inici/index.html#googtrans(ca\|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ### Licensing Information This work is licensed under a [CC BY-NC-ND 4.0 International License](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en_GB). ### Citation Information [DOI](https://zenodo.org/records/10362295) ### Contributions The annotation was entrusted to the company [M47 labs](https://www.m47labs.com/) through a public tender process. Thanks to [M. Carme Marí](https://petiteshistories.wordpress.com/quant-a/) and the [VilaWeb](vilaweb.cat) team for allowing us to use their texts. And also to all the [Catalan Wikipedia](ca.wikipedia.org) and [Gutenberg Project](gutenberg.org/) volunteers all their work.	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optimum/llm-perf-dataset	optimum	2023-12-18T01:13:55Z	7	0	[ "license:apache-2.0", "region:us" ]	null	2023-11-14T12:16:55Z	--- license: apache-2.0 ---	[ -0.1285340040922165, -0.1861676573753357, 0.6529127955436707, 0.49436259269714355, -0.19319328665733337, 0.23607435822486877, 0.36072009801864624, 0.05056355893611908, 0.579365611076355, 0.7400140166282654, -0.6508103609085083, -0.23783960938453674, -0.7102246284484863, -0.04782567173242569, -0.3894752860069275, 0.8470766544342041, -0.09598266333341599, 0.024004675447940826, 0.04712015390396118, -0.14317838847637177, -0.6121037006378174, -0.04771718382835388, -1.052453875541687, -0.06787467747926712, 0.3002282381057739, 0.5120975375175476, 0.8275901079177856, 0.39602965116500854, 0.5030567049980164, 1.7515555620193481, -0.08836933970451355, -0.2275441437959671, -0.45892030000686646, 0.42230701446533203, -0.3327735662460327, -0.4213368594646454, -0.26241639256477356, -0.07449351251125336, 0.32380399107933044, 0.7903711199760437, 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LLM360/CrystalCoderDatasets	LLM360	2023-12-12T16:29:38Z	7	12	[ "language:en", "pretrained", "arxiv:2207.14255", "region:us" ]	null	2023-11-30T23:57:32Z	--- language: - en tags: - pretrained --- # Description of the Dataset This release integrates the entire data sequence utilized in the CrystalCoder training. It encompasses data sequences from the three pre-training stages, combining information from two prior works: the [SlimPajama dataset](https://huggingface.co/datasets/cerebras/SlimPajama-627B) and [StarCoder](https://huggingface.co/datasets/bigcode/starcoderdata), totaling approximately 1300 billion tokens. These tokens are distributed across three stages, each with distinct weights. ## Stage 1 During this initial stage, half of the [SlimPajama data](https://huggingface.co/datasets/cerebras/SlimPajama-627B) is utilized, equivalent to approximately 345 billion tokens. ## Stage 2 In the second stage, the remaining half of the [SlimPajama data](https://huggingface.co/datasets/cerebras/SlimPajama-627B) is employed, along with two epochs of [StarCoder data](https://huggingface.co/datasets/bigcode/starcoderdata). For the StarCoder data, we apply [FIM augmentation](https://arxiv.org/abs/2207.14255) with an FIM rate of 0.9 and an SPM rate of 0.5. The total token count for this stage is calculated as 0.5 * 690 + 2 * 291, resulting in 927 billion tokens. ## Stage 3 The third stage involves reusing Python and web-related data from the [StarCoder data](https://huggingface.co/datasets/bigcode/starcoderdata), including HTML, CSS, and JavaScript. This data is utilized for training over three epochs, with the application of FIM at a rate of 0.3 alongside an SPM rate of 0.5. The total token count for this stage is 100 billion. Additionally, a small portion of the SlimPajama dataset, excluding the Github part, is also reused, contributing around 10 billion tokens. ### Instruction tuning (Stage 3a) To enhance the model's proficiency in real chat scenarios, we utilize a diverse set of instruction tuning datasets, totaling approximately 1 billion tokens. Specifically, our data include [OASST1-guanaco](https://huggingface.co/datasets/openaccess-ai-collective/oasst1-guanaco-extended-sharegpt), [SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca), [ShareGPT_V4.3](https://huggingface.co/datasets/Aeala/ShareGPT_Vicuna_unfiltered), [Evol-ShareGPT](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k), [CodeAlpaca](https://huggingface.co/datasets/lucasmccabe-lmi/CodeAlpaca-20k), [Rosetta Code](https://github.com/sahil280114/codealpaca/blob/master/data/rosetta_alpaca.json), [Evol-CodeAlpaca 1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1), [Evol-CodeAlpaca 2](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1), and a self-generated dataset centered on website creation through the [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) pipeline. We will release the full dataset soon. # Primary Usage This dataset serves as the foundation for training CrystalCoder and supports further reproduction. For training from scratch, please refer to our [training codes](https://github.com/LLM360/crystalcoder-train). For training from middle checkpoints, please load the dataloader states in checkpoints and follow [this tutorial](https://docs.cerebras.net/en/latest/wsc/tutorials/dataloader-checkpointing.html). # License Pretraining data in langauge model mostly comes from a collection of data sources with various licenses. Any use of all or part of the data here must abide by the terms of the original licenses, including attribution clauses when relevant. We refer users to [SlimPajama dataset](https://huggingface.co/datasets/cerebras/SlimPajama-627B) and [StarCoder](https://huggingface.co/datasets/bigcode/starcoderdata) for detailed license attribution.	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Mitsuki-Sakamoto/alpaca_farm-alpaca_gpt4_preference-re-preference_test	Mitsuki-Sakamoto	2023-12-13T13:41:18Z	7	0	[ "region:us" ]	null	2023-12-01T03:44:33Z	--- dataset_info: - config_name: opt-1.3b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 326835 num_examples: 194 download_size: 218330 dataset_size: 326835 - config_name: pythia-1.3b-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 340641 num_examples: 194 download_size: 229970 dataset_size: 340641 - config_name: pythia-1.3b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 splits: - name: preference num_bytes: 333120 num_examples: 194 download_size: 213247 dataset_size: 333120 - config_name: pythia-1.3b_alpaca_farm_instructions_sft_constant_slow-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 521801 num_examples: 194 download_size: 317469 dataset_size: 521801 - config_name: pythia-1.3b_alpaca_farm_instructions_sft_slow-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 363613 num_examples: 194 download_size: 229405 dataset_size: 363613 - config_name: pythia-1.3b_alpaca_farm_instructions_sft_wo_peft-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 381652 num_examples: 194 download_size: 241724 dataset_size: 381652 - config_name: pythia-1.4b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 447991 num_examples: 194 download_size: 271136 dataset_size: 447991 - config_name: pythia-1B-response-full-static-sft-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 162223 num_examples: 194 download_size: 110142 dataset_size: 162223 - config_name: pythia-1B-static-sft-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 120611 num_examples: 194 download_size: 83257 dataset_size: 120611 - config_name: reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 splits: - name: preference num_bytes: 113541 num_examples: 194 download_size: 76166 dataset_size: 113541 configs: - config_name: opt-1.3b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2 data_files: - split: preference path: opt-1.3b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2/preference-* - config_name: pythia-1.3b-reward-model-deberta-v3-large-v2 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ayymen/Pontoon-Translations	ayymen	2023-12-14T17:28:41Z	7	0	[ "task_categories:translation", "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language:ab", "language:ace", "language:ach", "language:ady", "language:af", "language:am", "language:an", "language:ann", "language:anp", "language:ar", "language:arn", "language:as", "language:ast", "language:ay", "language:az", "language:azz", "language:ba", "language:ban", "language:bas", "language:be", "language:bg", "language:bm", "language:bn", "language:bo", "language:br", "language:brx", "language:bs", "language:bxr", "language:ca", "language:cak", "language:ceb", "language:ckb", "language:cnh", "language:co", "language:crh", "language:cs", "language:csb", "language:cv", "language:cy", "language:da", "language:dag", "language:de", "language:dsb", "language:dv", "language:dyu", "language:ee", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:ewo", "language:fa", "language:ff", "language:fi", "language:fo", "language:fr", "language:frp", "language:fuf", "language:fur", "language:fy", "language:ga", "language:gd", "language:gl", "language:gn", "language:gom", "language:gor", "language:gu", "language:guc", "language:gv", "language:ha", "language:he", "language:hi", "language:hil", "language:hr", "language:hsb", "language:ht", "language:hu", "language:hus", "language:hy", "language:hye", "language:hyw", "language:ia", "language:id", "language:ie", "language:ig", "language:ilo", "language:is", "language:it", "language:ixl", "language:ja", "language:jbo", "language:jiv", "language:jv", "language:ka", "language:kaa", "language:kab", "language:kbd", "language:kk", "language:km", "language:kmr", "language:kn", "language:ko", "language:kpv", "language:ks", "language:kw", "language:ky", "language:lb", "language:lg", "language:lij", "language:ln", "language:lo", "language:lt", "language:ltg", "language:lus", "language:lv", "language:lzz", "language:mai", "language:mdf", "language:meh", "language:mg", "language:mhr", "language:mix", "language:mk", "language:ml", "language:mn", "language:mni", "language:mr", "language:mrj", "language:ms", "language:mt", "language:my", "language:myv", "language:nan", "language:nb", "language:ne", "language:nhe", "language:nhi", "language:nia", "language:nl", "language:nn", "language:nr", "language:nso", "language:nv", "language:ny", "language:nyn", "language:oc", "language:om", "language:or", "language:os", "language:pa", "language:pai", "language:pap", "language:pl", "language:ppl", "language:ps", "language:pt", "language:quc", "language:quy", "language:qvi", "language:rm", "language:ro", "language:ru", "language:rw", "language:sah", "language:sat", "language:sc", "language:scn", "language:sco", "language:sd", "language:ses", "language:shi", "language:si", "language:sk", "language:skr", "language:sl", "language:sn", "language:snk", "language:son", "language:sq", "language:sr", "language:ss", "language:st", "language:su", "language:sv", "language:sw", "language:syr", "language:szl", "language:ta", "language:te", "language:tg", "language:th", "language:ti", "language:tig", "language:tk", "language:tl", "language:tn", "language:tok", "language:tr", "language:trs", "language:ts", "language:tsz", "language:tt", "language:tw", "language:tyv", "language:tzm", "language:udm", "language:ug", "language:uk", "language:ur", "language:uz", "language:ve", "language:vec", "language:vi", "language:vot", "language:wo", "language:xcl", "language:xh", "language:yi", "language:yo", "language:yua", "language:yue", "language:zam", "language:zgh", "language:zh", "language:zu", "language:zza", "license:mpl-2.0", "region:us" ]	[ "translation", "text2text-generation" ]	2023-12-03T21:44:52Z	--- configs: - config_name: ab-en data_files: "ab-en.tsv" - config_name: ace-en data_files: "ace-en.tsv" - config_name: ach-en data_files: "ach-en.tsv" - config_name: ady-en data_files: "ady-en.tsv" - config_name: af-en data_files: "af-en.tsv" - config_name: am-en data_files: "am-en.tsv" - config_name: an-en data_files: "an-en.tsv" - config_name: ann-en data_files: "ann-en.tsv" - config_name: anp-en data_files: "anp-en.tsv" - config_name: ar-en data_files: "ar-en.tsv" - config_name: arn-en data_files: "arn-en.tsv" - config_name: as-en data_files: "as-en.tsv" - config_name: ast-en data_files: "ast-en.tsv" - config_name: ay-en data_files: "ay-en.tsv" - config_name: az-en data_files: "az-en.tsv" - config_name: azz-en data_files: "azz-en.tsv" - config_name: ba-en data_files: "ba-en.tsv" - config_name: ban-en data_files: "ban-en.tsv" - config_name: bas-en data_files: "bas-en.tsv" - config_name: be-en data_files: "be-en.tsv" - config_name: bg-en data_files: "bg-en.tsv" - config_name: bm-en data_files: "bm-en.tsv" - config_name: bn-BD-en data_files: "bn-BD-en.tsv" - config_name: bn-IN-en data_files: "bn-IN-en.tsv" - config_name: bn-en data_files: "bn-en.tsv" - config_name: bo-en data_files: "bo-en.tsv" - config_name: br-en data_files: "br-en.tsv" - config_name: brx-en data_files: "brx-en.tsv" - config_name: bs-en data_files: "bs-en.tsv" - config_name: bxr-en data_files: "bxr-en.tsv" - config_name: ca-en data_files: "ca-en.tsv" - config_name: ca-valencia-en data_files: "ca-valencia-en.tsv" - config_name: cak-en data_files: "cak-en.tsv" - config_name: ceb-en data_files: "ceb-en.tsv" - config_name: ckb-en data_files: "ckb-en.tsv" - config_name: cnh-en data_files: "cnh-en.tsv" - config_name: co-en data_files: "co-en.tsv" - config_name: crh-en data_files: "crh-en.tsv" - config_name: cs-en data_files: "cs-en.tsv" - config_name: csb-en data_files: "csb-en.tsv" - config_name: cv-en data_files: "cv-en.tsv" - config_name: cy-en data_files: "cy-en.tsv" - config_name: da-en data_files: "da-en.tsv" - config_name: dag-en data_files: "dag-en.tsv" - config_name: de-en data_files: "de-en.tsv" - config_name: dsb-en data_files: "dsb-en.tsv" - config_name: dv-en data_files: "dv-en.tsv" - config_name: dyu-en data_files: "dyu-en.tsv" - config_name: ee-en data_files: "ee-en.tsv" - config_name: el-en data_files: "el-en.tsv" - config_name: en-CA-en data_files: "en-CA-en.tsv" - config_name: en-GB-en data_files: "en-GB-en.tsv" - config_name: en-ZA-en data_files: "en-ZA-en.tsv" - config_name: eo-en data_files: "eo-en.tsv" - config_name: es-AR-en data_files: "es-AR-en.tsv" - config_name: es-CL-en data_files: "es-CL-en.tsv" - config_name: es-ES-en data_files: "es-ES-en.tsv" - config_name: es-MX-en data_files: "es-MX-en.tsv" - config_name: es-en data_files: "es-en.tsv" - config_name: et-en data_files: "et-en.tsv" - config_name: eu-en data_files: "eu-en.tsv" - config_name: ewo-en data_files: "ewo-en.tsv" - config_name: fa-en data_files: "fa-en.tsv" - config_name: ff-en data_files: "ff-en.tsv" - config_name: fi-en data_files: "fi-en.tsv" - config_name: fo-en data_files: "fo-en.tsv" - config_name: fr-en data_files: "fr-en.tsv" - config_name: frp-en data_files: "frp-en.tsv" - config_name: fuf-en data_files: "fuf-en.tsv" - config_name: fur-en data_files: "fur-en.tsv" - config_name: fy-NL-en data_files: "fy-NL-en.tsv" - config_name: ga-IE-en data_files: "ga-IE-en.tsv" - config_name: gd-en data_files: "gd-en.tsv" - config_name: gl-en data_files: "gl-en.tsv" - config_name: gn-en data_files: "gn-en.tsv" - config_name: gom-en data_files: "gom-en.tsv" - config_name: gor-en data_files: "gor-en.tsv" - config_name: gu-IN-en data_files: "gu-IN-en.tsv" - config_name: guc-en data_files: "guc-en.tsv" - config_name: gv-en data_files: "gv-en.tsv" - config_name: ha-en data_files: "ha-en.tsv" - config_name: he-en data_files: "he-en.tsv" - config_name: hi-IN-en data_files: "hi-IN-en.tsv" - config_name: hi-en data_files: "hi-en.tsv" - config_name: hil-en data_files: "hil-en.tsv" - config_name: hr-en data_files: "hr-en.tsv" - config_name: hsb-en data_files: "hsb-en.tsv" - config_name: ht-en data_files: "ht-en.tsv" - config_name: hu-en data_files: "hu-en.tsv" - config_name: hus-en data_files: "hus-en.tsv" - config_name: hy-AM-en data_files: "hy-AM-en.tsv" - config_name: hye-en data_files: "hye-en.tsv" - config_name: hyw-en data_files: "hyw-en.tsv" - config_name: ia-en data_files: "ia-en.tsv" - config_name: id-en data_files: "id-en.tsv" - config_name: ie-en data_files: "ie-en.tsv" - config_name: ig-en data_files: "ig-en.tsv" - config_name: ilo-en data_files: "ilo-en.tsv" - config_name: is-en data_files: "is-en.tsv" - config_name: it-en data_files: "it-en.tsv" - config_name: ixl-en data_files: "ixl-en.tsv" - config_name: ja-en data_files: "ja-en.tsv" - config_name: jbo-en data_files: "jbo-en.tsv" - config_name: jiv-en data_files: "jiv-en.tsv" - config_name: jv-en data_files: "jv-en.tsv" - config_name: ka-en data_files: "ka-en.tsv" - config_name: kaa-en data_files: "kaa-en.tsv" - config_name: kab-en data_files: "kab-en.tsv" - config_name: kbd-en data_files: "kbd-en.tsv" - config_name: kk-en data_files: "kk-en.tsv" - config_name: km-en data_files: "km-en.tsv" - config_name: kmr-en data_files: "kmr-en.tsv" - config_name: kn-en data_files: "kn-en.tsv" - config_name: ko-en data_files: "ko-en.tsv" - config_name: kpv-en data_files: "kpv-en.tsv" - config_name: ks-en data_files: "ks-en.tsv" - config_name: kw-en data_files: "kw-en.tsv" - config_name: ky-en data_files: "ky-en.tsv" - config_name: lb-en data_files: "lb-en.tsv" - config_name: lg-en data_files: "lg-en.tsv" - config_name: lij-en data_files: "lij-en.tsv" - config_name: ln-en data_files: "ln-en.tsv" - config_name: lo-en data_files: "lo-en.tsv" - config_name: lt-en data_files: "lt-en.tsv" - config_name: ltg-en data_files: "ltg-en.tsv" - config_name: lus-en data_files: "lus-en.tsv" - config_name: lv-en data_files: "lv-en.tsv" - config_name: lzz-en data_files: "lzz-en.tsv" - config_name: mai-en data_files: "mai-en.tsv" - config_name: mdf-en data_files: "mdf-en.tsv" - config_name: meh-en data_files: "meh-en.tsv" - config_name: mg-en data_files: "mg-en.tsv" - config_name: mhr-en data_files: "mhr-en.tsv" - config_name: mix-en data_files: "mix-en.tsv" - config_name: mk-en data_files: "mk-en.tsv" - config_name: ml-en data_files: "ml-en.tsv" - config_name: mn-en data_files: "mn-en.tsv" - config_name: mni-en data_files: "mni-en.tsv" - config_name: mr-en data_files: "mr-en.tsv" - config_name: mrj-en data_files: "mrj-en.tsv" - config_name: ms-en data_files: "ms-en.tsv" - config_name: mt-en data_files: "mt-en.tsv" - config_name: my-en data_files: "my-en.tsv" - config_name: myv-en data_files: "myv-en.tsv" - config_name: nan-tw-en data_files: "nan-tw-en.tsv" - config_name: nb-NO-en data_files: "nb-NO-en.tsv" - config_name: ne-NP-en data_files: "ne-NP-en.tsv" - config_name: nhe-en data_files: "nhe-en.tsv" - config_name: nhi-en data_files: "nhi-en.tsv" - config_name: nia-en data_files: "nia-en.tsv" - config_name: nl-en data_files: "nl-en.tsv" - config_name: nn-NO-en data_files: "nn-NO-en.tsv" - config_name: nr-en data_files: "nr-en.tsv" - config_name: nso-en data_files: "nso-en.tsv" - config_name: nv-en data_files: "nv-en.tsv" - config_name: ny-en data_files: "ny-en.tsv" - config_name: nyn-en data_files: "nyn-en.tsv" - config_name: oc-en data_files: "oc-en.tsv" - config_name: om-en data_files: "om-en.tsv" - config_name: or-en data_files: "or-en.tsv" - config_name: os-en data_files: "os-en.tsv" - config_name: pa-IN-en data_files: "pa-IN-en.tsv" - config_name: pa-PK-en data_files: "pa-PK-en.tsv" - config_name: pai-en data_files: "pai-en.tsv" - config_name: pap-AW-en data_files: "pap-AW-en.tsv" - config_name: pl-en data_files: "pl-en.tsv" - config_name: ppl-en data_files: "ppl-en.tsv" - config_name: ps-en data_files: "ps-en.tsv" - config_name: pt-BR-en data_files: "pt-BR-en.tsv" - config_name: pt-PT-en data_files: "pt-PT-en.tsv" - config_name: pt-en data_files: "pt-en.tsv" - config_name: quc-en data_files: "quc-en.tsv" - config_name: quy-en data_files: "quy-en.tsv" - config_name: qvi-en data_files: "qvi-en.tsv" - config_name: rm-en data_files: "rm-en.tsv" - config_name: rm-sursilv-en data_files: "rm-sursilv-en.tsv" - config_name: rm-vallader-en data_files: "rm-vallader-en.tsv" - config_name: ro-en data_files: "ro-en.tsv" - config_name: ru-en data_files: "ru-en.tsv" - config_name: rw-en data_files: "rw-en.tsv" - config_name: sah-en data_files: "sah-en.tsv" - config_name: sat-en data_files: "sat-en.tsv" - config_name: sc-en data_files: "sc-en.tsv" - config_name: scn-en data_files: "scn-en.tsv" - config_name: sco-en data_files: "sco-en.tsv" - config_name: sd-en data_files: "sd-en.tsv" - config_name: ses-en data_files: "ses-en.tsv" - config_name: shi-en data_files: "shi-en.tsv" - config_name: si-en data_files: "si-en.tsv" - config_name: sk-en data_files: "sk-en.tsv" - config_name: skr-en data_files: "skr-en.tsv" - config_name: sl-en data_files: "sl-en.tsv" - config_name: sn-en data_files: "sn-en.tsv" - config_name: snk-en data_files: "snk-en.tsv" - config_name: son-en data_files: "son-en.tsv" - config_name: sq-en data_files: "sq-en.tsv" - config_name: sr-en data_files: "sr-en.tsv" - config_name: ss-en data_files: "ss-en.tsv" - config_name: st-en data_files: "st-en.tsv" - config_name: su-en data_files: "su-en.tsv" - config_name: sv-SE-en data_files: "sv-SE-en.tsv" - config_name: sw-en data_files: "sw-en.tsv" - config_name: syr-en data_files: "syr-en.tsv" - config_name: szl-en data_files: "szl-en.tsv" - config_name: ta-en data_files: "ta-en.tsv" - config_name: te-en data_files: "te-en.tsv" - config_name: tg-en data_files: "tg-en.tsv" - config_name: th-en data_files: "th-en.tsv" - config_name: ti-en data_files: "ti-en.tsv" - config_name: tig-en data_files: "tig-en.tsv" - config_name: tk-en data_files: "tk-en.tsv" - config_name: tl-en data_files: "tl-en.tsv" - config_name: tn-en data_files: "tn-en.tsv" - config_name: tok-en data_files: "tok-en.tsv" - config_name: tr-en data_files: "tr-en.tsv" - config_name: trs-en data_files: "trs-en.tsv" - config_name: ts-en data_files: "ts-en.tsv" - config_name: tsz-en data_files: "tsz-en.tsv" - config_name: tt-en data_files: "tt-en.tsv" - config_name: tw-en data_files: "tw-en.tsv" - config_name: tyv-en data_files: "tyv-en.tsv" - config_name: tzm-en data_files: "tzm-en.tsv" - config_name: udm-en data_files: "udm-en.tsv" - config_name: ug-en data_files: "ug-en.tsv" - config_name: uk-en data_files: "uk-en.tsv" - config_name: ur-en data_files: "ur-en.tsv" - config_name: uz-en data_files: "uz-en.tsv" - config_name: ve-en data_files: "ve-en.tsv" - config_name: vec-en data_files: "vec-en.tsv" - config_name: vi-en data_files: "vi-en.tsv" - config_name: vot-en data_files: "vot-en.tsv" - config_name: wo-en data_files: "wo-en.tsv" - config_name: xcl-en data_files: "xcl-en.tsv" - config_name: xh-en data_files: "xh-en.tsv" - config_name: yi-en data_files: "yi-en.tsv" - config_name: yo-en data_files: "yo-en.tsv" - config_name: yua-en data_files: "yua-en.tsv" - config_name: yue-en data_files: "yue-en.tsv" - config_name: zam-en data_files: "zam-en.tsv" - config_name: zgh-en data_files: "zgh-en.tsv" default: true - config_name: zh-CN-en data_files: "zh-CN-en.tsv" - config_name: zh-HK-en data_files: "zh-HK-en.tsv" - config_name: zh-TW-en data_files: "zh-TW-en.tsv" - config_name: zu-en data_files: "zu-en.tsv" - config_name: zza-en data_files: "zza-en.tsv" license: mpl-2.0 task_categories: - translation - text2text-generation pretty_name: Pontoon Translations annotations_creators: - crowdsourced language: - ab - ace - ach - ady - af - am - an - ann - anp - ar - arn - as - ast - ay - az - azz - ba - ban - bas - be - bg - bm - bn - bn - bn - bo - br - brx - bs - bxr - ca - ca - cak - ceb - ckb - cnh - co - crh - cs - csb - cv - cy - da - dag - de - dsb - dv - dyu - ee - el - en - en - en - en - eo - es - es - es - es - es - et - eu - ewo - fa - ff - fi - fo - fr - frp - fuf - fur - fy - ga - gd - gl - gn - gom - gor - gu - guc - gv - ha - he - hi - hi - hil - hr - hsb - ht - hu - hus - hy - hye - hyw - ia - id - ie - ig - ilo - is - it - ixl - ja - jbo - jiv - jv - ka - kaa - kab - kbd - kk - km - kmr - kn - ko - kpv - ks - kw - ky - lb - lg - lij - ln - lo - lt - ltg - lus - lv - lzz - mai - mdf - meh - mg - mhr - mix - mk - ml - mn - mni - mr - mrj - ms - mt - my - myv - nan - nb - ne - nhe - nhi - nia - nl - nn - nr - nso - nv - ny - nyn - oc - om - or - os - pa - pa - pai - pap - pl - ppl - ps - pt - pt - pt - quc - quy - qvi - rm - rm - rm - ro - ru - rw - sah - sat - sc - scn - sco - sd - ses - shi - si - sk - skr - sl - sn - snk - son - sq - sr - ss - st - su - sv - sw - syr - szl - ta - te - tg - th - ti - tig - tk - tl - tn - tok - tr - trs - ts - tsz - tt - tw - tyv - tzm - udm - ug - uk - ur - uz - ve - vec - vi - vot - wo - xcl - xh - yi - yo - yua - yue - zam - zgh - zh - zh - zh - zu - zza --- # Pontoon Translations This is a dataset containing translations from Mozilla's [Pontoon](https://pontoon.mozilla.org) localization platform for more than 200 languages. Source sentences are in English.	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SeaLLMs/Sea-bench	SeaLLMs	2023-12-12T13:34:52Z	7	8	[ "size_categories:n<1K", "language:en", "language:vi", "language:id", "language:km", "language:th", "language:lo", "language:my", "language:ms", "language:tl", "language:zh", "license:apache-2.0", "region:us" ]	null	2023-12-04T03:53:06Z	--- license: apache-2.0 language: - en - vi - id - km - th - lo - my - ms - tl - zh size_categories: - n<1K configs: - config_name: Question data_files: question.jsonl --- # Sea-bench - a benchmark for evaluating chat assistants in Southeast Asian languages. See interactive benchmark view at [Spaces/SeaLLMs/Sea-bench](https://huggingface.co/spaces/SeaLLMs/Sea-bench) While there are popular benchmarks to evaluate LLMs as helpful assistants, such as [MT-bench](https://huggingface.co/spaces/lmsys/mt-bench), they are only English-based and likely to be unsuitable to evaluate performances in low-resource languages. Due to this lack of multilingual benchmarks for assistant-style models, we engaged native linguists to build a multilingual test set with instructions that cover 9 Southeast Asian languages, called [Sea-bench](https://huggingface.co/datasets/SeaLLMs/Sea-bench). The linguists sourced such data by manually translating open-source English test sets, collecting real user questions from local forums and websites, collecting real math and reasoning questions from reputable sources, as well as writing test instructions and questions themselves. Our Sea-Bench consists of diverse categories of instructions to evaluate models, as described in the following: * Task-solving: This type of data comprises various text understanding and processing tasks that test the ability of the language model to perform certain NLP tasks such as summarization, translation, etc. * Math-reasoning: This includes math problems and logical reasoning tasks. * General-instruction data: This type of data consists of general user-centric instructions, which evaluate the model's ability in general knowledge and writing. Examples for this type can be requests for recommendations, such as "Suggest three popular books," or instructions that require the model to generate creative outputs, like "Write a short story about a dragon." * NaturalQA: This consists of queries posted by real users, often in popular local forums, involving local contexts or scenarios. The aim is to test the model's capacity to understand and respond coherently to colloquial language, idioms, and locally contextual references. * Safety: This includes both general safety and local context-related safety instructions. The instructions could involve testing the model's understanding of safe practices, its ability to advise on safety rules, or its capacity to respond to safety-related queries. While most general safety questions are translated from open sources, other local context-related safety instructions are written by linguists of each language. Safety data only covers Vietnamese, Indonesian, and Thai. The released Sea-bench test set is a small subset that contains 20 questions per task type per language. Sea-bench can be evaluated similar to MT-bench, using GPT-4 as a judge in a score-based grading metrics and a peer comparison (or pairwise comparison) manner. Instructions to evaluate models on Sea-bench (score-based grading): ```bash # Clone and install LLM judge: https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge # Download SeaLLMs/Sea-bench files to folder `Sea-bench` # Copy `Sea-bench` folder to llm `FastChat/fastchat/llm_judge` # Run generation, similar to MT-bench, e.g: python gen_model_answer.py --model-path lmsys/vicuna-7b-v1.5 --model-id vicuna-7b-v1.5 --bench-name Sea-bench # Run LLM judgement python gen_judgment.py \ --parallel 6 \ --bench-name Sea-bench \ --model-list ${YOUR_MODEL_NAME} ``` ## Evaluation results: ![fig_sea_bench_side_by_side.png](fig_sea_bench_side_by_side.png) Sea-bench is used to evaluate [SeaLLMs](https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b), a group of language models built with focus in Southeast Asian languages. ## Contribution If you have Sea-bench evaluations on your model that you would like into the aggregated results. Please kindly submit a pull request with an updated [model_judgment/gpt-4_single.jsonl ](https://huggingface.co/datasets/SeaLLMs/Sea-bench/tree/main/model_judgment/gpt-4_single.jsonl) file. Please use a different model name than the ones indicated in the file. ## Citation If you find our project useful, hope you can star our repo and cite our work as follows. Corresponding Author: [[email protected]](mailto:[email protected]) ``` @article{damonlpsg2023seallm, author = {Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing}, title = {SeaLLMs - Large Language Models for Southeast Asia}, year = 2023, Eprint = {arXiv:2312.00738}, } ```	[ -0.42806681990623474, -0.7536598443984985, 0.5414071679115295, 0.438610702753067, -0.4376358091831207, -0.21839110553264618, -0.2700510025024414, -0.5202834606170654, -0.003716719802469015, 0.6061314940452576, -0.39354178309440613, -0.5926271677017212, -0.3778674304485321, 0.12924684584140778, 0.005947394762188196, 0.7358130812644958, -0.16380810737609863, 0.19525380432605743, -0.00826159305870533, -0.4162255525588989, -0.457559734582901, -0.5304166674613953, -0.7866610884666443, 0.10128103196620941, 0.006676411721855402, 0.3992118239402771, 0.43478286266326904, 0.453288733959198, 0.5108450651168823, 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open-llm-leaderboard/details_mncai__yi-34B-v3	open-llm-leaderboard	2023-12-11T01:54:36Z	7	0	[ "region:us" ]	null	2023-12-11T01:53:57Z	--- pretty_name: Evaluation run of mncai/yi-34B-v3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mncai/yi-34B-v3](https://huggingface.co/mncai/yi-34B-v3) 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_mncai__yi-34B-v3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-11T01:51:08.694143](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__yi-34B-v3/blob/main/results_2023-12-11T01-51-08.694143.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\": 0.7536948044621744,\n\ \ \"acc_stderr\": 0.028378789321173548,\n \"acc_norm\": 0.7581198984934292,\n\ \ \"acc_norm_stderr\": 0.02891498378900509,\n \"mc1\": 0.4186046511627907,\n\ \ \"mc1_stderr\": 0.017270015284476855,\n \"mc2\": 0.5753679426280454,\n\ \ \"mc2_stderr\": 0.014962842073717312\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6390784982935154,\n \"acc_stderr\": 0.014034761386175452,\n\ \ \"acc_norm\": 0.6706484641638225,\n \"acc_norm_stderr\": 0.013734057652635476\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6487751443935471,\n\ \ \"acc_stderr\": 0.004763774981834676,\n \"acc_norm\": 0.8511252738498307,\n\ \ \"acc_norm_stderr\": 0.0035523745313052004\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.6962962962962963,\n\ \ \"acc_stderr\": 0.03972552884785137,\n \"acc_norm\": 0.6962962962962963,\n\ \ \"acc_norm_stderr\": 0.03972552884785137\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.881578947368421,\n \"acc_stderr\": 0.026293995855474935,\n\ \ \"acc_norm\": 0.881578947368421,\n \"acc_norm_stderr\": 0.026293995855474935\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.79,\n\ \ \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n \ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.8188679245283019,\n \"acc_stderr\": 0.023702963526757798,\n\ \ \"acc_norm\": 0.8188679245283019,\n \"acc_norm_stderr\": 0.023702963526757798\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.9097222222222222,\n\ \ \"acc_stderr\": 0.023964965777906935,\n \"acc_norm\": 0.9097222222222222,\n\ \ \"acc_norm_stderr\": 0.023964965777906935\n },\n \"harness\|hendrycksTest-college_chemistry\|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_computer_science\|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-college_mathematics\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness\|hendrycksTest-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\": 0.5392156862745098,\n \"acc_stderr\": 0.04959859966384181,\n\ \ \"acc_norm\": 0.5392156862745098,\n \"acc_norm_stderr\": 0.04959859966384181\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n\ \ \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.7787234042553192,\n \"acc_stderr\": 0.027136349602424056,\n\ \ \"acc_norm\": 0.7787234042553192,\n \"acc_norm_stderr\": 0.027136349602424056\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5701754385964912,\n\ \ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.5701754385964912,\n\ \ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.7379310344827587,\n \"acc_stderr\": 0.036646663372252565,\n\ \ \"acc_norm\": 0.7379310344827587,\n \"acc_norm_stderr\": 0.036646663372252565\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.6957671957671958,\n \"acc_stderr\": 0.02369541500946309,\n \"\ acc_norm\": 0.6957671957671958,\n \"acc_norm_stderr\": 0.02369541500946309\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5396825396825397,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.5396825396825397,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562427,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562427\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.9,\n\ \ \"acc_stderr\": 0.017066403719657255,\n \"acc_norm\": 0.9,\n \ \ \"acc_norm_stderr\": 0.017066403719657255\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.6699507389162561,\n \"acc_stderr\": 0.033085304262282574,\n\ \ \"acc_norm\": 0.6699507389162561,\n \"acc_norm_stderr\": 0.033085304262282574\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\"\ : 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8545454545454545,\n \"acc_stderr\": 0.027530196355066584,\n\ \ \"acc_norm\": 0.8545454545454545,\n \"acc_norm_stderr\": 0.027530196355066584\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9343434343434344,\n \"acc_stderr\": 0.01764652667723333,\n \"\ acc_norm\": 0.9343434343434344,\n \"acc_norm_stderr\": 0.01764652667723333\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n\ \ \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.8205128205128205,\n \"acc_stderr\": 0.019457390787681803,\n\ \ \"acc_norm\": 0.8205128205128205,\n \"acc_norm_stderr\": 0.019457390787681803\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.4111111111111111,\n \"acc_stderr\": 0.02999992350870669,\n \ \ \"acc_norm\": 0.4111111111111111,\n \"acc_norm_stderr\": 0.02999992350870669\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8529411764705882,\n \"acc_stderr\": 0.023005459446673964,\n\ \ \"acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.023005459446673964\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.4966887417218543,\n \"acc_stderr\": 0.04082393379449654,\n \"\ acc_norm\": 0.4966887417218543,\n \"acc_norm_stderr\": 0.04082393379449654\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.9284403669724771,\n \"acc_stderr\": 0.01105125524781546,\n \"\ acc_norm\": 0.9284403669724771,\n \"acc_norm_stderr\": 0.01105125524781546\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6620370370370371,\n \"acc_stderr\": 0.03225941352631295,\n \"\ acc_norm\": 0.6620370370370371,\n \"acc_norm_stderr\": 0.03225941352631295\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9068627450980392,\n \"acc_stderr\": 0.020397853969426994,\n \"\ acc_norm\": 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969426994\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.9156118143459916,\n \"acc_stderr\": 0.01809424711647332,\n \ \ \"acc_norm\": 0.9156118143459916,\n \"acc_norm_stderr\": 0.01809424711647332\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8026905829596412,\n\ \ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\ \ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n\ \ \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.9008264462809917,\n \"acc_stderr\": 0.02728524631275896,\n \"\ acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.02728524631275896\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8981481481481481,\n\ \ \"acc_stderr\": 0.029239272675632748,\n \"acc_norm\": 0.8981481481481481,\n\ \ \"acc_norm_stderr\": 0.029239272675632748\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8650306748466258,\n \"acc_stderr\": 0.026845765054553855,\n\ \ \"acc_norm\": 0.8650306748466258,\n \"acc_norm_stderr\": 0.026845765054553855\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5714285714285714,\n\ \ \"acc_stderr\": 0.04697113923010213,\n \"acc_norm\": 0.5714285714285714,\n\ \ \"acc_norm_stderr\": 0.04697113923010213\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n\ \ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9401709401709402,\n\ \ \"acc_stderr\": 0.015537514263253862,\n \"acc_norm\": 0.9401709401709402,\n\ \ \"acc_norm_stderr\": 0.015537514263253862\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \ \ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9029374201787995,\n\ \ \"acc_stderr\": 0.010586474712018283,\n \"acc_norm\": 0.9029374201787995,\n\ \ \"acc_norm_stderr\": 0.010586474712018283\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8121387283236994,\n \"acc_stderr\": 0.02102926975242323,\n\ \ \"acc_norm\": 0.8121387283236994,\n \"acc_norm_stderr\": 0.02102926975242323\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.7039106145251397,\n\ \ \"acc_stderr\": 0.015268677317602274,\n \"acc_norm\": 0.7039106145251397,\n\ \ \"acc_norm_stderr\": 0.015268677317602274\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8398692810457516,\n \"acc_stderr\": 0.020998740930362303,\n\ \ \"acc_norm\": 0.8398692810457516,\n \"acc_norm_stderr\": 0.020998740930362303\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.8167202572347267,\n\ \ \"acc_stderr\": 0.021974198848265812,\n \"acc_norm\": 0.8167202572347267,\n\ \ \"acc_norm_stderr\": 0.021974198848265812\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8641975308641975,\n \"acc_stderr\": 0.0190615881815054,\n\ \ \"acc_norm\": 0.8641975308641975,\n \"acc_norm_stderr\": 0.0190615881815054\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6276595744680851,\n \"acc_stderr\": 0.02883892147125145,\n \ \ \"acc_norm\": 0.6276595744680851,\n \"acc_norm_stderr\": 0.02883892147125145\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5971316818774446,\n\ \ \"acc_stderr\": 0.01252695557711801,\n \"acc_norm\": 0.5971316818774446,\n\ \ \"acc_norm_stderr\": 0.01252695557711801\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8345588235294118,\n \"acc_stderr\": 0.02257177102549474,\n\ \ \"acc_norm\": 0.8345588235294118,\n \"acc_norm_stderr\": 0.02257177102549474\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.8169934640522876,\n \"acc_stderr\": 0.015643069911273344,\n \ \ \"acc_norm\": 0.8169934640522876,\n \"acc_norm_stderr\": 0.015643069911273344\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8489795918367347,\n \"acc_stderr\": 0.022923004094736844,\n\ \ \"acc_norm\": 0.8489795918367347,\n \"acc_norm_stderr\": 0.022923004094736844\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8855721393034826,\n\ \ \"acc_stderr\": 0.022509345325101706,\n \"acc_norm\": 0.8855721393034826,\n\ \ \"acc_norm_stderr\": 0.022509345325101706\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5903614457831325,\n\ \ \"acc_stderr\": 0.038284011150790206,\n \"acc_norm\": 0.5903614457831325,\n\ \ \"acc_norm_stderr\": 0.038284011150790206\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.024648068961366152,\n\ \ \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.024648068961366152\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.4186046511627907,\n\ \ \"mc1_stderr\": 0.017270015284476855,\n \"mc2\": 0.5753679426280454,\n\ \ \"mc2_stderr\": 0.014962842073717312\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.835043409629045,\n \"acc_stderr\": 0.010430917468237419\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.645185746777862,\n \ \ \"acc_stderr\": 0.013179083387979214\n }\n}\n```" repo_url: https://huggingface.co/mncai/yi-34B-v3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|arc:challenge\|25_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-11T01-51-08.694143.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|gsm8k\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hellaswag\|10_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-11T01-51-08.694143.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-11T01-51-08.694143.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '/details_harness\|winogrande\|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-11T01-51-08.694143.parquet' - config_name: results data_files: - split: 2023_12_11T01_51_08.694143 path: - results_2023-12-11T01-51-08.694143.parquet - split: latest path: - results_2023-12-11T01-51-08.694143.parquet --- # Dataset Card for Evaluation run of mncai/yi-34B-v3 ## Dataset Description - Homepage: - Repository: https://huggingface.co/mncai/yi-34B-v3 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [mncai/yi-34B-v3](https://huggingface.co/mncai/yi-34B-v3) 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_mncai__yi-34B-v3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-11T01:51:08.694143](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__yi-34B-v3/blob/main/results_2023-12-11T01-51-08.694143.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": 0.7536948044621744, "acc_stderr": 0.028378789321173548, "acc_norm": 0.7581198984934292, "acc_norm_stderr": 0.02891498378900509, "mc1": 0.4186046511627907, "mc1_stderr": 0.017270015284476855, "mc2": 0.5753679426280454, "mc2_stderr": 0.014962842073717312 }, "harness\|arc:challenge\|25": { "acc": 0.6390784982935154, "acc_stderr": 0.014034761386175452, "acc_norm": 0.6706484641638225, "acc_norm_stderr": 0.013734057652635476 }, "harness\|hellaswag\|10": { "acc": 0.6487751443935471, "acc_stderr": 0.004763774981834676, "acc_norm": 0.8511252738498307, "acc_norm_stderr": 0.0035523745313052004 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6962962962962963, "acc_stderr": 0.03972552884785137, "acc_norm": 0.6962962962962963, "acc_norm_stderr": 0.03972552884785137 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.881578947368421, "acc_stderr": 0.026293995855474935, "acc_norm": 0.881578947368421, "acc_norm_stderr": 0.026293995855474935 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.8188679245283019, "acc_stderr": 0.023702963526757798, "acc_norm": 0.8188679245283019, "acc_norm_stderr": 0.023702963526757798 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.9097222222222222, "acc_stderr": 0.023964965777906935, "acc_norm": 0.9097222222222222, "acc_norm_stderr": 0.023964965777906935 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "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": 0.5392156862745098, "acc_stderr": 0.04959859966384181, "acc_norm": 0.5392156862745098, "acc_norm_stderr": 0.04959859966384181 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7787234042553192, "acc_stderr": 0.027136349602424056, "acc_norm": 0.7787234042553192, "acc_norm_stderr": 0.027136349602424056 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5701754385964912, "acc_stderr": 0.04657047260594963, "acc_norm": 0.5701754385964912, "acc_norm_stderr": 0.04657047260594963 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7379310344827587, "acc_stderr": 0.036646663372252565, "acc_norm": 0.7379310344827587, "acc_norm_stderr": 0.036646663372252565 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6957671957671958, "acc_stderr": 0.02369541500946309, "acc_norm": 0.6957671957671958, "acc_norm_stderr": 0.02369541500946309 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5396825396825397, "acc_stderr": 0.04458029125470973, "acc_norm": 0.5396825396825397, "acc_norm_stderr": 0.04458029125470973 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.57, "acc_stderr": 0.04975698519562427, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562427 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.9, "acc_stderr": 0.017066403719657255, "acc_norm": 0.9, "acc_norm_stderr": 0.017066403719657255 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6699507389162561, "acc_stderr": 0.033085304262282574, "acc_norm": 0.6699507389162561, "acc_norm_stderr": 0.033085304262282574 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8545454545454545, "acc_stderr": 0.027530196355066584, "acc_norm": 0.8545454545454545, "acc_norm_stderr": 0.027530196355066584 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9343434343434344, "acc_stderr": 0.01764652667723333, "acc_norm": 0.9343434343434344, "acc_norm_stderr": 0.01764652667723333 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9740932642487047, "acc_stderr": 0.01146452335695318, "acc_norm": 0.9740932642487047, "acc_norm_stderr": 0.01146452335695318 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.8205128205128205, "acc_stderr": 0.019457390787681803, "acc_norm": 0.8205128205128205, "acc_norm_stderr": 0.019457390787681803 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.4111111111111111, "acc_stderr": 0.02999992350870669, "acc_norm": 0.4111111111111111, "acc_norm_stderr": 0.02999992350870669 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8529411764705882, "acc_stderr": 0.023005459446673964, "acc_norm": 0.8529411764705882, "acc_norm_stderr": 0.023005459446673964 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4966887417218543, "acc_stderr": 0.04082393379449654, "acc_norm": 0.4966887417218543, "acc_norm_stderr": 0.04082393379449654 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9284403669724771, "acc_stderr": 0.01105125524781546, "acc_norm": 0.9284403669724771, "acc_norm_stderr": 0.01105125524781546 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6620370370370371, "acc_stderr": 0.03225941352631295, "acc_norm": 0.6620370370370371, "acc_norm_stderr": 0.03225941352631295 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9068627450980392, "acc_stderr": 0.020397853969426994, "acc_norm": 0.9068627450980392, "acc_norm_stderr": 0.020397853969426994 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9156118143459916, "acc_stderr": 0.01809424711647332, "acc_norm": 0.9156118143459916, "acc_norm_stderr": 0.01809424711647332 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8026905829596412, "acc_stderr": 0.02670985334496796, "acc_norm": 0.8026905829596412, "acc_norm_stderr": 0.02670985334496796 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8702290076335878, "acc_stderr": 0.029473649496907065, "acc_norm": 0.8702290076335878, "acc_norm_stderr": 0.029473649496907065 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.9008264462809917, "acc_stderr": 0.02728524631275896, "acc_norm": 0.9008264462809917, "acc_norm_stderr": 0.02728524631275896 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8981481481481481, "acc_stderr": 0.029239272675632748, "acc_norm": 0.8981481481481481, "acc_norm_stderr": 0.029239272675632748 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8650306748466258, "acc_stderr": 0.026845765054553855, "acc_norm": 0.8650306748466258, "acc_norm_stderr": 0.026845765054553855 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5714285714285714, "acc_stderr": 0.04697113923010213, "acc_norm": 0.5714285714285714, "acc_norm_stderr": 0.04697113923010213 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8446601941747572, "acc_stderr": 0.03586594738573974, "acc_norm": 0.8446601941747572, "acc_norm_stderr": 0.03586594738573974 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9401709401709402, "acc_stderr": 0.015537514263253862, "acc_norm": 0.9401709401709402, "acc_norm_stderr": 0.015537514263253862 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9029374201787995, "acc_stderr": 0.010586474712018283, "acc_norm": 0.9029374201787995, "acc_norm_stderr": 0.010586474712018283 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8121387283236994, "acc_stderr": 0.02102926975242323, "acc_norm": 0.8121387283236994, "acc_norm_stderr": 0.02102926975242323 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.7039106145251397, "acc_stderr": 0.015268677317602274, "acc_norm": 0.7039106145251397, "acc_norm_stderr": 0.015268677317602274 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8398692810457516, "acc_stderr": 0.020998740930362303, "acc_norm": 0.8398692810457516, "acc_norm_stderr": 0.020998740930362303 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.8167202572347267, "acc_stderr": 0.021974198848265812, "acc_norm": 0.8167202572347267, "acc_norm_stderr": 0.021974198848265812 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8641975308641975, "acc_stderr": 0.0190615881815054, "acc_norm": 0.8641975308641975, "acc_norm_stderr": 0.0190615881815054 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.6276595744680851, "acc_stderr": 0.02883892147125145, "acc_norm": 0.6276595744680851, "acc_norm_stderr": 0.02883892147125145 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5971316818774446, "acc_stderr": 0.01252695557711801, "acc_norm": 0.5971316818774446, "acc_norm_stderr": 0.01252695557711801 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8345588235294118, "acc_stderr": 0.02257177102549474, "acc_norm": 0.8345588235294118, "acc_norm_stderr": 0.02257177102549474 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.8169934640522876, "acc_stderr": 0.015643069911273344, "acc_norm": 0.8169934640522876, "acc_norm_stderr": 0.015643069911273344 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7181818181818181, "acc_stderr": 0.04309118709946458, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.04309118709946458 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.8489795918367347, "acc_stderr": 0.022923004094736844, "acc_norm": 0.8489795918367347, "acc_norm_stderr": 0.022923004094736844 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101706, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101706 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5903614457831325, "acc_stderr": 0.038284011150790206, "acc_norm": 0.5903614457831325, "acc_norm_stderr": 0.038284011150790206 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8830409356725146, "acc_stderr": 0.024648068961366152, "acc_norm": 0.8830409356725146, "acc_norm_stderr": 0.024648068961366152 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.4186046511627907, "mc1_stderr": 0.017270015284476855, "mc2": 0.5753679426280454, "mc2_stderr": 0.014962842073717312 }, "harness\|winogrande\|5": { "acc": 0.835043409629045, "acc_stderr": 0.010430917468237419 }, "harness\|gsm8k\|5": { "acc": 0.645185746777862, "acc_stderr": 0.013179083387979214 } } ``` ### 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]	[ -0.7341963052749634, -0.8561139702796936, 0.26726287603378296, 0.25223106145858765, -0.16682946681976318, -0.06875399500131607, 0.054495275020599365, -0.1997600942850113, 0.5900561213493347, -0.04007305949926376, -0.5305846333503723, -0.7273930907249451, -0.41382476687431335, 0.2235555648803711, 0.010303404182195663, 0.8161659836769104, -0.1735188513994217, -0.1377994567155838, 0.1031884029507637, -0.037864554673433304, -0.26400992274284363, -0.34010741114616394, -0.5456295013427734, 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kpriyanshu256/semeval-task-8-b-v2-test-paraphrase-2	kpriyanshu256	2023-12-11T03:26:21Z	7	0	[ "region:us" ]	null	2023-12-11T03:26:20Z	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: model dtype: string - name: source dtype: string - name: label dtype: int64 - name: id dtype: int64 - name: paraphrase dtype: string - name: paraphrase2 dtype: string splits: - name: test num_bytes: 11109023 num_examples: 3000 download_size: 5184022 dataset_size: 11109023 --- # Dataset Card for "semeval-task-8-b-v2-test-paraphrase-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.1619366705417633, -0.4838148355484009, 0.4253115952014923, 0.45246970653533936, -0.45558443665504456, -0.3902171850204468, 0.3627721667289734, 0.05648307129740715, 0.5401670932769775, 0.8682225942611694, -0.6715478301048279, -0.4733535945415497, 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Lkhagvasurenam/STTmn	Lkhagvasurenam	2023-12-11T04:10:17Z	7	0	[ "region:us" ]	null	2023-12-11T04:10:02Z	--- dataset_info: features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_bytes: 7998858.1 num_examples: 9 - name: test num_bytes: 1145369.0 num_examples: 1 download_size: 9066089 dataset_size: 9144227.1 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---	[ -0.12853407859802246, -0.18616771697998047, 0.652912974357605, 0.4943622350692749, -0.1931931972503662, 0.23607467114925385, 0.36071979999542236, 0.05056376755237579, 0.5793653726577759, 0.740013837814331, -0.6508100032806396, -0.23783957958221436, -0.7102252244949341, -0.04782591760158539, -0.3894752264022827, 0.8470760583877563, -0.0959823876619339, 0.02400447428226471, 0.04711972177028656, -0.14317813515663147, -0.6121035814285278, -0.047717154026031494, -1.0524539947509766, 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open-llm-leaderboard/details_mistralai__Mixtral-8x7B-v0.1	open-llm-leaderboard	2023-12-15T14:38:08Z	7	0	[ "region:us" ]	null	2023-12-11T18:07:04Z	--- pretty_name: Evaluation run of mistralai/Mixtral-8x7B-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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_mistralai__Mixtral-8x7B-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-15T14:35:04.630519](https://huggingface.co/datasets/open-llm-leaderboard/details_mistralai__Mixtral-8x7B-v0.1/blob/main/results_2023-12-15T14-35-04.630519.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\": 0.6972963513811701,\n\ \ \"acc_stderr\": 0.03075228309763406,\n \"acc_norm\": 0.7033766203346898,\n\ \ \"acc_norm_stderr\": 0.03134647583855853,\n \"mc1\": 0.3268053855569155,\n\ \ \"mc1_stderr\": 0.01641987473113503,\n \"mc2\": 0.47623983056821834,\n\ \ \"mc2_stderr\": 0.01423216788461584\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6109215017064846,\n \"acc_stderr\": 0.014247309976045607,\n\ \ \"acc_norm\": 0.643344709897611,\n \"acc_norm_stderr\": 0.01399805690262019\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6548496315475005,\n\ \ \"acc_stderr\": 0.004744456628455121,\n \"acc_norm\": 0.8570005974905397,\n\ \ \"acc_norm_stderr\": 0.00349356791409329\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.674074074074074,\n\ \ \"acc_stderr\": 0.040491220417025055,\n \"acc_norm\": 0.674074074074074,\n\ \ \"acc_norm_stderr\": 0.040491220417025055\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.8026315789473685,\n \"acc_stderr\": 0.03238981601699397,\n\ \ \"acc_norm\": 0.8026315789473685,\n \"acc_norm_stderr\": 0.03238981601699397\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.67,\n\ \ \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\": 0.67,\n \ \ \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.769811320754717,\n \"acc_stderr\": 0.025907897122408173,\n\ \ \"acc_norm\": 0.769811320754717,\n \"acc_norm_stderr\": 0.025907897122408173\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8472222222222222,\n\ \ \"acc_stderr\": 0.030085743248565666,\n \"acc_norm\": 0.8472222222222222,\n\ \ \"acc_norm_stderr\": 0.030085743248565666\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n\ \ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness\|hendrycksTest-college_mathematics\|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_medicine\|5\": {\n \"acc\": 0.6878612716763006,\n\ \ \"acc_stderr\": 0.03533133389323657,\n \"acc_norm\": 0.6878612716763006,\n\ \ \"acc_norm_stderr\": 0.03533133389323657\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.47058823529411764,\n \"acc_stderr\": 0.049665709039785295,\n\ \ \"acc_norm\": 0.47058823529411764,\n \"acc_norm_stderr\": 0.049665709039785295\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.6680851063829787,\n \"acc_stderr\": 0.030783736757745647,\n\ \ \"acc_norm\": 0.6680851063829787,\n \"acc_norm_stderr\": 0.030783736757745647\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.6228070175438597,\n\ \ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.6228070175438597,\n\ \ \"acc_norm_stderr\": 0.04559522141958216\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\"\ : 0.4417989417989418,\n \"acc_stderr\": 0.025576257061253833,\n \"\ acc_norm\": 0.4417989417989418,\n \"acc_norm_stderr\": 0.025576257061253833\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5079365079365079,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \ \ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.8290322580645161,\n \"acc_stderr\": 0.021417242936321582,\n \"\ acc_norm\": 0.8290322580645161,\n \"acc_norm_stderr\": 0.021417242936321582\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.6157635467980296,\n \"acc_stderr\": 0.034223985656575515,\n \"\ acc_norm\": 0.6157635467980296,\n \"acc_norm_stderr\": 0.034223985656575515\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|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-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\"\ : 0.8484848484848485,\n \"acc_stderr\": 0.025545650426603613,\n \"\ acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.025545650426603613\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n\ \ \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.7051282051282052,\n \"acc_stderr\": 0.023119362758232304,\n\ \ \"acc_norm\": 0.7051282051282052,\n \"acc_norm_stderr\": 0.023119362758232304\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.35185185185185186,\n \"acc_stderr\": 0.02911661760608301,\n \ \ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.02911661760608301\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7605042016806722,\n \"acc_stderr\": 0.027722065493361252,\n\ \ \"acc_norm\": 0.7605042016806722,\n \"acc_norm_stderr\": 0.027722065493361252\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.5165562913907285,\n \"acc_stderr\": 0.04080244185628972,\n \"\ acc_norm\": 0.5165562913907285,\n \"acc_norm_stderr\": 0.04080244185628972\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8678899082568807,\n \"acc_stderr\": 0.014517801914598238,\n \"\ acc_norm\": 0.8678899082568807,\n \"acc_norm_stderr\": 0.014517801914598238\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6203703703703703,\n \"acc_stderr\": 0.03309682581119035,\n \"\ acc_norm\": 0.6203703703703703,\n \"acc_norm_stderr\": 0.03309682581119035\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.024509803921568606,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.024509803921568606\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8734177215189873,\n \"acc_stderr\": 0.021644195727955173,\n \ \ \"acc_norm\": 0.8734177215189873,\n \"acc_norm_stderr\": 0.021644195727955173\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7443946188340808,\n\ \ \"acc_stderr\": 0.029275891003969927,\n \"acc_norm\": 0.7443946188340808,\n\ \ \"acc_norm_stderr\": 0.029275891003969927\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.03336820338476076,\n\ \ \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.03336820338476076\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035202,\n \"\ acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035202\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8240740740740741,\n\ \ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\ \ \"acc_norm_stderr\": 0.036809181416738807\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\": 0.5089285714285714,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026622,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026622\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9273504273504274,\n\ \ \"acc_stderr\": 0.017004368568132366,\n \"acc_norm\": 0.9273504273504274,\n\ \ \"acc_norm_stderr\": 0.017004368568132366\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \ \ \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932263\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8773946360153256,\n\ \ \"acc_stderr\": 0.011728672144131563,\n \"acc_norm\": 0.8773946360153256,\n\ \ \"acc_norm_stderr\": 0.011728672144131563\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.791907514450867,\n \"acc_stderr\": 0.021855255263421795,\n\ \ \"acc_norm\": 0.791907514450867,\n \"acc_norm_stderr\": 0.021855255263421795\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.37318435754189944,\n\ \ \"acc_stderr\": 0.01617569201338196,\n \"acc_norm\": 0.37318435754189944,\n\ \ \"acc_norm_stderr\": 0.01617569201338196\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.0242886194660461,\n\ \ \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.0242886194660461\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7620578778135049,\n\ \ \"acc_stderr\": 0.024185150647818707,\n \"acc_norm\": 0.7620578778135049,\n\ \ \"acc_norm_stderr\": 0.024185150647818707\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8364197530864198,\n \"acc_stderr\": 0.02058146613825715,\n\ \ \"acc_norm\": 0.8364197530864198,\n \"acc_norm_stderr\": 0.02058146613825715\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.549645390070922,\n \"acc_stderr\": 0.02968010556502904,\n \ \ \"acc_norm\": 0.549645390070922,\n \"acc_norm_stderr\": 0.02968010556502904\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5215123859191656,\n\ \ \"acc_stderr\": 0.012758410941038925,\n \"acc_norm\": 0.5215123859191656,\n\ \ \"acc_norm_stderr\": 0.012758410941038925\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7904411764705882,\n \"acc_stderr\": 0.024723110407677055,\n\ \ \"acc_norm\": 0.7904411764705882,\n \"acc_norm_stderr\": 0.024723110407677055\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.761437908496732,\n \"acc_stderr\": 0.017242385828779613,\n \ \ \"acc_norm\": 0.761437908496732,\n \"acc_norm_stderr\": 0.017242385828779613\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7795918367346939,\n \"acc_stderr\": 0.02653704531214529,\n\ \ \"acc_norm\": 0.7795918367346939,\n \"acc_norm_stderr\": 0.02653704531214529\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8557213930348259,\n\ \ \"acc_stderr\": 0.024845753212306046,\n \"acc_norm\": 0.8557213930348259,\n\ \ \"acc_norm_stderr\": 0.024845753212306046\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061452,\n\ \ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061452\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3268053855569155,\n\ \ \"mc1_stderr\": 0.01641987473113503,\n \"mc2\": 0.47623983056821834,\n\ \ \"mc2_stderr\": 0.01423216788461584\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8018942383583267,\n \"acc_stderr\": 0.011201862744487047\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.46853677028051555,\n \ \ \"acc_stderr\": 0.01374518994845042\n }\n}\n```" repo_url: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|arc:challenge\|25_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|arc:challenge\|25_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-15T14-35-04.630519.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|gsm8k\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|gsm8k\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hellaswag\|10_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hellaswag\|10_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-11T18-04-02.035270.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-15T14-35-04.630519.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-15T14-35-04.630519.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '/details_harness\|winogrande\|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '/details_harness\|winogrande\|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-15T14-35-04.630519.parquet' - config_name: results data_files: - split: 2023_12_11T18_04_02.035270 path: - results_2023-12-11T18-04-02.035270.parquet - split: 2023_12_15T14_35_04.630519 path: - results_2023-12-15T14-35-04.630519.parquet - split: latest path: - results_2023-12-15T14-35-04.630519.parquet --- # Dataset Card for Evaluation run of mistralai/Mixtral-8x7B-v0.1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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_mistralai__Mixtral-8x7B-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-15T14:35:04.630519](https://huggingface.co/datasets/open-llm-leaderboard/details_mistralai__Mixtral-8x7B-v0.1/blob/main/results_2023-12-15T14-35-04.630519.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": 0.6972963513811701, "acc_stderr": 0.03075228309763406, "acc_norm": 0.7033766203346898, "acc_norm_stderr": 0.03134647583855853, "mc1": 0.3268053855569155, "mc1_stderr": 0.01641987473113503, "mc2": 0.47623983056821834, "mc2_stderr": 0.01423216788461584 }, "harness\|arc:challenge\|25": { "acc": 0.6109215017064846, "acc_stderr": 0.014247309976045607, "acc_norm": 0.643344709897611, "acc_norm_stderr": 0.01399805690262019 }, "harness\|hellaswag\|10": { "acc": 0.6548496315475005, "acc_stderr": 0.004744456628455121, "acc_norm": 0.8570005974905397, "acc_norm_stderr": 0.00349356791409329 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.674074074074074, "acc_stderr": 0.040491220417025055, "acc_norm": 0.674074074074074, "acc_norm_stderr": 0.040491220417025055 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8026315789473685, "acc_stderr": 0.03238981601699397, "acc_norm": 0.8026315789473685, "acc_norm_stderr": 0.03238981601699397 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.769811320754717, "acc_stderr": 0.025907897122408173, "acc_norm": 0.769811320754717, "acc_norm_stderr": 0.025907897122408173 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8472222222222222, "acc_stderr": 0.030085743248565666, "acc_norm": 0.8472222222222222, "acc_norm_stderr": 0.030085743248565666 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6878612716763006, "acc_stderr": 0.03533133389323657, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.03533133389323657 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.47058823529411764, "acc_stderr": 0.049665709039785295, "acc_norm": 0.47058823529411764, "acc_norm_stderr": 0.049665709039785295 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6680851063829787, "acc_stderr": 0.030783736757745647, "acc_norm": 0.6680851063829787, "acc_norm_stderr": 0.030783736757745647 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.6228070175438597, "acc_stderr": 0.04559522141958216, "acc_norm": 0.6228070175438597, "acc_norm_stderr": 0.04559522141958216 }, "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": 0.4417989417989418, "acc_stderr": 0.025576257061253833, "acc_norm": 0.4417989417989418, "acc_norm_stderr": 0.025576257061253833 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8290322580645161, "acc_stderr": 0.021417242936321582, "acc_norm": 0.8290322580645161, "acc_norm_stderr": 0.021417242936321582 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6157635467980296, "acc_stderr": 0.034223985656575515, "acc_norm": 0.6157635467980296, "acc_norm_stderr": 0.034223985656575515 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "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": 0.8484848484848485, "acc_stderr": 0.025545650426603613, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.025545650426603613 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.917098445595855, "acc_stderr": 0.01989934131572178, "acc_norm": 0.917098445595855, "acc_norm_stderr": 0.01989934131572178 }, 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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]	[ -0.7630723714828491, -0.8515834808349609, 0.2542460560798645, 0.23814265429973602, -0.1609029471874237, -0.1226654127240181, 0.047803863883018494, -0.2179674655199051, 0.5735198855400085, -0.08353186398744583, -0.48984986543655396, -0.6630760431289673, -0.45486578345298767, 0.23456549644470215, -0.05236740782856941, 0.8356100916862488, -0.18195600807666779, 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GroupSix/common-voice-en-sv	GroupSix	2023-12-11T20:29:46Z	7	0	[ "region:us" ]	null	2023-12-11T19:34:59Z	--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 12090945008 num_examples: 12588 - name: test num_bytes: 4937998648 num_examples: 5141 download_size: 2508578885 dataset_size: 17028943656 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---	[ -0.128533735871315, -0.1861678659915924, 0.6529127359390259, 0.4943625032901764, -0.19319316744804382, 0.23607438802719116, 0.36071979999542236, 0.05056331306695938, 0.5793653130531311, 0.740013837814331, -0.6508103609085083, -0.23783963918685913, -0.710224986076355, -0.047825887799263, -0.389475017786026, 0.8470761775970459, -0.09598281234502792, 0.024005085229873657, 0.047120101749897, -0.14317834377288818, -0.6121039390563965, -0.047717198729515076, -1.052453637123108, -0.06787455081939697, 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faizalnf1800/Writing_Prompt_FAIR	faizalnf1800	2023-12-15T15:31:59Z	7	0	[ "arxiv:1805.04833", "region:us" ]	null	2023-12-12T12:04:38Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 406584542 num_examples: 3504584 - name: test num_bytes: 22558960 num_examples: 193806 - name: valid num_bytes: 12839086.633194694 num_examples: 111038 download_size: 311122593 dataset_size: 441982588.6331947 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* --- This is Dataset for this paper [Hierarchical Neural Story Generation](https://arxiv.org/pdf/1805.04833v1.pdf)	[ 0.04578341543674469, -0.6356837153434753, 0.342232346534729, 0.2994612455368042, 0.196011021733284, -0.0724322721362114, 0.30236947536468506, -0.3563258945941925, 0.17469485104084015, 0.3909876346588135, -1.2264560461044312, -0.43276506662368774, -0.09027156978845596, -0.2880704998970032, -0.40625321865081787, 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DragonLine/ksponspeech	DragonLine	2023-12-12T16:51:10Z	7	0	[ "region:us" ]	null	2023-12-12T14:05:24Z	--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcripts dtype: string splits: - name: train num_bytes: 53133867240.215996 num_examples: 299636 - name: test num_bytes: 6736967357.531417 num_examples: 37455 - name: valid num_bytes: 6484620568.886582 num_examples: 37454 download_size: 62734833313 dataset_size: 66355455166.633995 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 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tli489/LongAlpaca-100	tli489	2023-12-12T19:50:48Z	7	0	[ "region:us" ]	null	2023-12-12T19:47:58Z	--- dataset_info: features: - name: instruction dtype: string - name: file dtype: string - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 6804562 num_examples: 100 download_size: 3626767 dataset_size: 6804562 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, -0.06787490844726562, 0.3002279996871948, 0.5120974779129028, 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cointegrated/dral_lite	cointegrated	2023-12-13T16:01:28Z	7	0	[ "region:us" ]	null	2023-12-13T15:57:18Z	--- dataset_info: features: - name: id dtype: string - name: conv_id dtype: string - name: original_or_reenacted dtype: string - name: time_start dtype: string - name: time_end dtype: string - name: duration dtype: string - name: trans_id dtype: string - name: wav_orig sequence: float32 - name: wav_trans sequence: float32 splits: - name: v2_short num_bytes: 347182958 num_examples: 1173 download_size: 349676470 dataset_size: 347182958 configs: - config_name: default data_files: - split: v2_short path: data/v2_short-* --- This is a copy of DRAL-2.0 dataset (https://www.cs.utep.edu/nigel/dral/), with only short fragments and only those where the audio on the left is original.	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madhaviit/NHSFT10k	madhaviit	2023-12-13T21:51:35Z	7	0	[ "region:us" ]	null	2023-12-13T21:51:32Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 14141408 num_examples: 10000 download_size: 5006544 dataset_size: 14141408 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.12853382527828217, -0.18616774678230286, 0.6529128551483154, 0.49436265230178833, -0.19319304823875427, 0.23607459664344788, 0.3607199192047119, 0.050563305616378784, 0.5793652534484863, 0.7400139570236206, -0.650810182094574, -0.2378396987915039, -0.710224986076355, -0.04782576858997345, -0.3894750773906708, 0.8470762372016907, -0.09598264843225479, 0.02400507777929306, 0.04712019860744476, -0.1431785225868225, -0.6121039390563965, -0.0477173775434494, -1.0524537563323975, -0.06787477433681488, 0.3002280592918396, 0.5120973587036133, 0.8275896310806274, 0.3960292339324951, 0.5030565857887268, 1.7515558004379272, -0.08836926519870758, 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Erynan/eval_deon_100	Erynan	2023-12-13T22:03:21Z	7	0	[ "region:us" ]	null	2023-12-13T22:03:21Z	--- dataset_info: features: - name: prompt dtype: string - name: response_a dtype: string - name: response_b dtype: string - name: more_reasonable dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 27529 num_examples: 100 download_size: 16292 dataset_size: 27529 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.12853391468524933, -0.18616794049739838, 0.652912974357605, 0.4943625330924988, -0.19319303333759308, 0.23607459664344788, 0.3607197701931, 0.05056367069482803, 0.5793653726577759, 0.7400139570236206, -0.6508100628852844, -0.23783954977989197, -0.710224986076355, -0.04782590642571449, -0.38947534561157227, 0.8470762968063354, -0.09598267078399658, 0.02400510013103485, 0.047119930386543274, -0.14317823946475983, -0.6121041774749756, -0.04771709442138672, -1.0524539947509766, -0.0678747296333313, 0.3002283275127411, 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aisuko/vqa	aisuko	2023-12-14T03:46:32Z	7	0	[ "license:apache-2.0", "region:us" ]	null	2023-12-14T03:37:03Z	--- license: apache-2.0 --- # Overview The original code is from https://huggingface.co/datasets/Graphcore/vqa/tree/main Adaptered by: Aisuko # How to use it ```python from datasets import load_dataset dataset = load_dataset("aisuko/vqa", split="validation[:200]") dataset ``` ``` Dataset({ features: ['question', 'question_type', 'question_id', 'image_id', 'answer_type', 'label'], num_rows: 200 }) ``` ## Remove the label column ```python dataset = dataset.remove_columns(['question_type', 'question_id', 'answer_type']) ``` ## Check the image ```python from PIL import Image image = Image.open(dataset[0]['image_id']) image ```	[ -0.4410795271396637, -0.6281914114952087, 0.10398450493812561, -0.08561055362224579, -0.19916276633739471, 0.03407394886016846, 0.21442252397537231, -0.09666509926319122, 0.14025770127773285, 0.4777134358882904, -0.7009156346321106, -0.7955249547958374, 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khanzaid/sentiment_review	khanzaid	2023-12-14T06:31:35Z	7	0	[ "region:us" ]	null	2023-12-14T06:31:33Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 792620 num_examples: 4846 download_size: 412816 dataset_size: 792620 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, -0.06787490844726562, 0.3002279996871948, 0.5120974779129028, 0.8275896310806274, 0.3960292339324951, 0.5030564665794373, 1.7515558004379272, -0.08836928009986877, 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Seongill/NQ_5_missing_adv_test	Seongill	2023-12-14T06:36:56Z	7	0	[ "region:us" ]	null	2023-12-14T06:36:52Z	--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: has_answer dtype: bool - name: similar_sub dtype: string - name: ctxs list: - name: answer_sent sequence: string - name: hasanswer dtype: bool - name: id dtype: string - name: is_adv dtype: bool - name: new_answer_sent dtype: string - name: original_text dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: status dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1993029 num_examples: 450 download_size: 1138217 dataset_size: 1993029 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 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HowMannyMore/romanurdu-sentiment-dataset	HowMannyMore	2023-12-14T11:51:44Z	7	0	[ "region:us" ]	null	2023-12-14T11:51:41Z	--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 4797399 num_examples: 53869 - name: test num_bytes: 990904 num_examples: 11185 - name: valid num_bytes: 849266 num_examples: 9507 download_size: 4449642 dataset_size: 6637569 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---	[ -0.1285340040922165, -0.1861676573753357, 0.6529127955436707, 0.49436259269714355, -0.19319328665733337, 0.23607435822486877, 0.36072009801864624, 0.05056355893611908, 0.579365611076355, 0.7400140166282654, -0.6508103609085083, -0.23783960938453674, -0.7102246284484863, -0.04782567173242569, -0.3894752860069275, 0.8470766544342041, -0.09598266333341599, 0.024004675447940826, 0.04712015390396118, -0.14317838847637177, 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open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-v3	open-llm-leaderboard	2023-12-14T12:17:46Z	7	0	[ "region:us" ]	null	2023-12-14T12:17:00Z	--- pretty_name: Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-v3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [kyujinpy/PlatYi-34B-Llama-Q-v3](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-v3)\ \ 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_kyujinpy__PlatYi-34B-Llama-Q-v3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-14T12:14:12.357754](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-v3/blob/main/results_2023-12-14T12-14-12.357754.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\": 0.7362231043895405,\n\ \ \"acc_stderr\": 0.028525669907782977,\n \"acc_norm\": 0.7496962088802717,\n\ \ \"acc_norm_stderr\": 0.02917819886610155,\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5180149372938835,\n\ \ \"mc2_stderr\": 0.014666631083011332\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6117747440273038,\n \"acc_stderr\": 0.014241614207414046,\n\ \ \"acc_norm\": 0.643344709897611,\n \"acc_norm_stderr\": 0.013998056902620192\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6490738896634136,\n\ \ \"acc_stderr\": 0.004762844770909867,\n \"acc_norm\": 0.8488348934475204,\n\ \ \"acc_norm_stderr\": 0.0035747765941085046\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.03785714465066652,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.03785714465066652\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.8618421052631579,\n \"acc_stderr\": 0.028081042939576552,\n\ \ \"acc_norm\": 0.8618421052631579,\n \"acc_norm_stderr\": 0.028081042939576552\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.83,\n\ \ \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.83,\n \ \ \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7849056603773585,\n \"acc_stderr\": 0.025288394502891363,\n\ \ \"acc_norm\": 0.7849056603773585,\n \"acc_norm_stderr\": 0.025288394502891363\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8611111111111112,\n\ \ \"acc_stderr\": 0.028919802956134926,\n \"acc_norm\": 0.8611111111111112,\n\ \ \"acc_norm_stderr\": 0.028919802956134926\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\ acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6994219653179191,\n\ \ \"acc_stderr\": 0.03496101481191179,\n \"acc_norm\": 0.6994219653179191,\n\ \ \"acc_norm_stderr\": 0.03496101481191179\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4803921568627451,\n \"acc_stderr\": 0.04971358884367406,\n\ \ \"acc_norm\": 0.4803921568627451,\n \"acc_norm_stderr\": 0.04971358884367406\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.7446808510638298,\n \"acc_stderr\": 0.028504856470514255,\n\ \ \"acc_norm\": 0.7446808510638298,\n \"acc_norm_stderr\": 0.028504856470514255\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.6052631578947368,\n\ \ \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.6052631578947368,\n\ \ \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.7517241379310344,\n \"acc_stderr\": 0.036001056927277716,\n\ \ \"acc_norm\": 0.7517241379310344,\n \"acc_norm_stderr\": 0.036001056927277716\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.6481481481481481,\n \"acc_stderr\": 0.024594975128920938,\n \"\ acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.024594975128920938\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.6031746031746031,\n\ \ \"acc_stderr\": 0.04375888492727059,\n \"acc_norm\": 0.6031746031746031,\n\ \ \"acc_norm_stderr\": 0.04375888492727059\n },\n \"harness\|hendrycksTest-global_facts\|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-high_school_biology\|5\": {\n \"acc\": 0.8903225806451613,\n\ \ \"acc_stderr\": 0.01777677870048519,\n \"acc_norm\": 0.8903225806451613,\n\ \ \"acc_norm_stderr\": 0.01777677870048519\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.6108374384236454,\n \"acc_stderr\": 0.034304624161038716,\n\ \ \"acc_norm\": 0.6108374384236454,\n \"acc_norm_stderr\": 0.034304624161038716\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\"\ : 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865394,\n\ \ \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865394\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.9040404040404041,\n \"acc_stderr\": 0.020984808610047933,\n \"\ acc_norm\": 0.9040404040404041,\n \"acc_norm_stderr\": 0.020984808610047933\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527026,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527026\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.7794871794871795,\n \"acc_stderr\": 0.0210206726808279,\n \ \ \"acc_norm\": 0.7794871794871795,\n \"acc_norm_stderr\": 0.0210206726808279\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.4111111111111111,\n \"acc_stderr\": 0.02999992350870668,\n \ \ \"acc_norm\": 0.4111111111111111,\n \"acc_norm_stderr\": 0.02999992350870668\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.8403361344537815,\n \"acc_stderr\": 0.0237933539975288,\n \ \ \"acc_norm\": 0.8403361344537815,\n \"acc_norm_stderr\": 0.0237933539975288\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.4768211920529801,\n \"acc_stderr\": 0.04078093859163083,\n \"\ acc_norm\": 0.4768211920529801,\n \"acc_norm_stderr\": 0.04078093859163083\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.9174311926605505,\n \"acc_stderr\": 0.011800361363016569,\n \"\ acc_norm\": 0.9174311926605505,\n \"acc_norm_stderr\": 0.011800361363016569\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6342592592592593,\n \"acc_stderr\": 0.032847388576472056,\n \"\ acc_norm\": 0.6342592592592593,\n \"acc_norm_stderr\": 0.032847388576472056\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"\ acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.9240506329113924,\n \"acc_stderr\": 0.017244633251065702,\n \ \ \"acc_norm\": 0.9240506329113924,\n \"acc_norm_stderr\": 0.017244633251065702\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.8026905829596412,\n\ \ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\ \ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8473282442748091,\n \"acc_stderr\": 0.03154521672005471,\n\ \ \"acc_norm\": 0.8473282442748091,\n \"acc_norm_stderr\": 0.03154521672005471\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.9090909090909091,\n \"acc_stderr\": 0.02624319405407388,\n \"\ acc_norm\": 0.9090909090909091,\n \"acc_norm_stderr\": 0.02624319405407388\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.030381596756651655,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.030381596756651655\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8895705521472392,\n \"acc_stderr\": 0.024624937788941318,\n\ \ \"acc_norm\": 0.8895705521472392,\n \"acc_norm_stderr\": 0.024624937788941318\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.6607142857142857,\n\ \ \"acc_stderr\": 0.04493949068613541,\n \"acc_norm\": 0.6607142857142857,\n\ \ \"acc_norm_stderr\": 0.04493949068613541\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8737864077669902,\n \"acc_stderr\": 0.03288180278808628,\n\ \ \"acc_norm\": 0.8737864077669902,\n \"acc_norm_stderr\": 0.03288180278808628\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9273504273504274,\n\ \ \"acc_stderr\": 0.017004368568132342,\n \"acc_norm\": 0.9273504273504274,\n\ \ \"acc_norm_stderr\": 0.017004368568132342\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.036845294917747094,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.036845294917747094\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.9067688378033205,\n\ \ \"acc_stderr\": 0.010397417087292847,\n \"acc_norm\": 0.9067688378033205,\n\ \ \"acc_norm_stderr\": 0.010397417087292847\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8179190751445087,\n \"acc_stderr\": 0.020776761102512982,\n\ \ \"acc_norm\": 0.8179190751445087,\n \"acc_norm_stderr\": 0.020776761102512982\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.5687150837988827,\n\ \ \"acc_stderr\": 0.016563829399047714,\n \"acc_norm\": 0.5687150837988827,\n\ \ \"acc_norm_stderr\": 0.016563829399047714\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8202614379084967,\n \"acc_stderr\": 0.02198603218206415,\n\ \ \"acc_norm\": 0.8202614379084967,\n \"acc_norm_stderr\": 0.02198603218206415\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.819935691318328,\n\ \ \"acc_stderr\": 0.02182342285774494,\n \"acc_norm\": 0.819935691318328,\n\ \ \"acc_norm_stderr\": 0.02182342285774494\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8487654320987654,\n \"acc_stderr\": 0.019935086092149886,\n\ \ \"acc_norm\": 0.8487654320987654,\n \"acc_norm_stderr\": 0.019935086092149886\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.6134751773049646,\n \"acc_stderr\": 0.029049190342543465,\n \ \ \"acc_norm\": 0.6134751773049646,\n \"acc_norm_stderr\": 0.029049190342543465\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5821382007822686,\n\ \ \"acc_stderr\": 0.012596744108998565,\n \"acc_norm\": 0.5821382007822686,\n\ \ \"acc_norm_stderr\": 0.012596744108998565\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8051470588235294,\n \"acc_stderr\": 0.02406059942348742,\n\ \ \"acc_norm\": 0.8051470588235294,\n \"acc_norm_stderr\": 0.02406059942348742\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.8071895424836601,\n \"acc_stderr\": 0.015959983971206744,\n \ \ \"acc_norm\": 0.8071895424836601,\n \"acc_norm_stderr\": 0.015959983971206744\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.8285714285714286,\n \"acc_stderr\": 0.024127463462650163,\n\ \ \"acc_norm\": 0.8285714285714286,\n \"acc_norm_stderr\": 0.024127463462650163\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8855721393034826,\n\ \ \"acc_stderr\": 0.022509345325101716,\n \"acc_norm\": 0.8855721393034826,\n\ \ \"acc_norm_stderr\": 0.022509345325101716\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\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8654970760233918,\n \"acc_stderr\": 0.026168221344662297,\n\ \ \"acc_norm\": 0.8654970760233918,\n \"acc_norm_stderr\": 0.026168221344662297\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5180149372938835,\n\ \ \"mc2_stderr\": 0.014666631083011332\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8421468034727704,\n \"acc_stderr\": 0.010247165248719764\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0667172100075815,\n \ \ \"acc_stderr\": 0.006873340544455133\n }\n}\n```" repo_url: https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-v3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|arc:challenge\|25_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-14T12-14-12.357754.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|gsm8k\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hellaswag\|10_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-14T12-14-12.357754.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-14T12-14-12.357754.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '/details_harness\|winogrande\|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-14T12-14-12.357754.parquet' - config_name: results data_files: - split: 2023_12_14T12_14_12.357754 path: - results_2023-12-14T12-14-12.357754.parquet - split: latest path: - results_2023-12-14T12-14-12.357754.parquet --- # Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-v3 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-Llama-Q-v3](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-v3) 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_kyujinpy__PlatYi-34B-Llama-Q-v3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-14T12:14:12.357754](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-v3/blob/main/results_2023-12-14T12-14-12.357754.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": 0.7362231043895405, "acc_stderr": 0.028525669907782977, "acc_norm": 0.7496962088802717, "acc_norm_stderr": 0.02917819886610155, "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5180149372938835, "mc2_stderr": 0.014666631083011332 }, "harness\|arc:challenge\|25": { "acc": 0.6117747440273038, "acc_stderr": 0.014241614207414046, "acc_norm": 0.643344709897611, "acc_norm_stderr": 0.013998056902620192 }, "harness\|hellaswag\|10": { "acc": 0.6490738896634136, "acc_stderr": 0.004762844770909867, "acc_norm": 0.8488348934475204, "acc_norm_stderr": 0.0035747765941085046 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066652, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066652 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8618421052631579, "acc_stderr": 0.028081042939576552, "acc_norm": 0.8618421052631579, "acc_norm_stderr": 0.028081042939576552 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7849056603773585, "acc_stderr": 0.025288394502891363, "acc_norm": 0.7849056603773585, "acc_norm_stderr": 0.025288394502891363 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8611111111111112, "acc_stderr": 0.028919802956134926, "acc_norm": 0.8611111111111112, "acc_norm_stderr": 0.028919802956134926 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6994219653179191, "acc_stderr": 0.03496101481191179, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.03496101481191179 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4803921568627451, "acc_stderr": 0.04971358884367406, "acc_norm": 0.4803921568627451, "acc_norm_stderr": 0.04971358884367406 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7446808510638298, "acc_stderr": 0.028504856470514255, "acc_norm": 0.7446808510638298, "acc_norm_stderr": 0.028504856470514255 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.6052631578947368, "acc_stderr": 0.045981880578165414, "acc_norm": 0.6052631578947368, "acc_norm_stderr": 0.045981880578165414 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7517241379310344, "acc_stderr": 0.036001056927277716, "acc_norm": 0.7517241379310344, "acc_norm_stderr": 0.036001056927277716 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.6481481481481481, "acc_stderr": 0.024594975128920938, "acc_norm": 0.6481481481481481, "acc_norm_stderr": 0.024594975128920938 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.6031746031746031, "acc_stderr": 0.04375888492727059, "acc_norm": 0.6031746031746031, "acc_norm_stderr": 0.04375888492727059 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8903225806451613, "acc_stderr": 0.01777677870048519, "acc_norm": 0.8903225806451613, "acc_norm_stderr": 0.01777677870048519 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6108374384236454, "acc_stderr": 0.034304624161038716, "acc_norm": 0.6108374384236454, "acc_norm_stderr": 0.034304624161038716 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865394, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865394 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.9040404040404041, "acc_stderr": 0.020984808610047933, "acc_norm": 0.9040404040404041, "acc_norm_stderr": 0.020984808610047933 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527026, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527026 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7794871794871795, "acc_stderr": 0.0210206726808279, "acc_norm": 0.7794871794871795, "acc_norm_stderr": 0.0210206726808279 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.4111111111111111, "acc_stderr": 0.02999992350870668, "acc_norm": 0.4111111111111111, "acc_norm_stderr": 0.02999992350870668 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.8403361344537815, "acc_stderr": 0.0237933539975288, "acc_norm": 0.8403361344537815, "acc_norm_stderr": 0.0237933539975288 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4768211920529801, "acc_stderr": 0.04078093859163083, "acc_norm": 0.4768211920529801, "acc_norm_stderr": 0.04078093859163083 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.9174311926605505, "acc_stderr": 0.011800361363016569, "acc_norm": 0.9174311926605505, "acc_norm_stderr": 0.011800361363016569 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6342592592592593, "acc_stderr": 0.032847388576472056, "acc_norm": 0.6342592592592593, "acc_norm_stderr": 0.032847388576472056 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658928, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658928 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.9240506329113924, "acc_stderr": 0.017244633251065702, "acc_norm": 0.9240506329113924, "acc_norm_stderr": 0.017244633251065702 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.8026905829596412, "acc_stderr": 0.02670985334496796, "acc_norm": 0.8026905829596412, "acc_norm_stderr": 0.02670985334496796 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8473282442748091, "acc_stderr": 0.03154521672005471, "acc_norm": 0.8473282442748091, "acc_norm_stderr": 0.03154521672005471 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.9090909090909091, "acc_stderr": 0.02624319405407388, "acc_norm": 0.9090909090909091, "acc_norm_stderr": 0.02624319405407388 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.030381596756651655, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.030381596756651655 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8895705521472392, "acc_stderr": 0.024624937788941318, "acc_norm": 0.8895705521472392, "acc_norm_stderr": 0.024624937788941318 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6607142857142857, "acc_stderr": 0.04493949068613541, "acc_norm": 0.6607142857142857, "acc_norm_stderr": 0.04493949068613541 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8737864077669902, "acc_stderr": 0.03288180278808628, "acc_norm": 0.8737864077669902, "acc_norm_stderr": 0.03288180278808628 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9273504273504274, "acc_stderr": 0.017004368568132342, "acc_norm": 0.9273504273504274, "acc_norm_stderr": 0.017004368568132342 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.84, "acc_stderr": 0.036845294917747094, "acc_norm": 0.84, "acc_norm_stderr": 0.036845294917747094 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.9067688378033205, "acc_stderr": 0.010397417087292847, "acc_norm": 0.9067688378033205, "acc_norm_stderr": 0.010397417087292847 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8179190751445087, "acc_stderr": 0.020776761102512982, "acc_norm": 0.8179190751445087, "acc_norm_stderr": 0.020776761102512982 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.5687150837988827, "acc_stderr": 0.016563829399047714, "acc_norm": 0.5687150837988827, "acc_norm_stderr": 0.016563829399047714 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8202614379084967, "acc_stderr": 0.02198603218206415, "acc_norm": 0.8202614379084967, "acc_norm_stderr": 0.02198603218206415 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.819935691318328, 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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]	[ -0.7321614623069763, -0.8506631851196289, 0.2864162027835846, 0.23115284740924835, -0.18566939234733582, -0.055686451494693756, 0.0363822765648365, -0.2119244784116745, 0.5922031402587891, -0.05290033668279648, -0.4761205017566681, -0.7045602202415466, -0.4435993731021881, 0.2559356093406677, -0.03663814440369606, 0.8525470495223999, -0.1786852329969406, 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BlahBlah314/AcolyteExP	BlahBlah314	2023-12-14T16:22:23Z	7	0	[ "region:us" ]	null	2023-12-14T16:22:22Z	--- dataset_info: features: - name: instruction dtype: string - name: enhanced dtype: string - name: prescription dtype: string splits: - name: train num_bytes: 419197 num_examples: 191 download_size: 47308 dataset_size: 419197 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.12853382527828217, -0.18616774678230286, 0.6529128551483154, 0.49436265230178833, -0.19319304823875427, 0.23607459664344788, 0.3607199192047119, 0.050563305616378784, 0.5793652534484863, 0.7400139570236206, -0.650810182094574, -0.2378396987915039, -0.710224986076355, -0.04782576858997345, -0.3894750773906708, 0.8470762372016907, -0.09598264843225479, 0.02400507777929306, 0.04712019860744476, -0.1431785225868225, -0.6121039390563965, -0.0477173775434494, -1.0524537563323975, -0.06787477433681488, 0.3002280592918396, 0.5120973587036133, 0.8275896310806274, 0.3960292339324951, 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beratcmn/turkish-poems-cleaned	beratcmn	2023-12-14T20:13:06Z	7	0	[ "license:apache-2.0", "region:us" ]	null	2023-12-14T19:05:44Z	--- license: apache-2.0 --- HTML tags removed and overall cleaned version of okg/turkish-poems. Original: https://huggingface.co/datasets/okg/turkish-poems Data için teşekkür ederim okg <3	[ -0.5652459859848022, -0.9790675640106201, 0.10808341205120087, -0.1610230654478073, -0.5891176462173462, -0.1976248323917389, -0.1523628532886505, -0.7582502365112305, 0.6489995718002319, 0.8253260254859924, -0.562073290348053, -0.5511205792427063, -0.44872358441352844, 0.10275193303823471, -0.29090815782546997, 1.3254879713058472, -0.3396555781364441, 0.07418942451477051, 0.03796030953526497, -0.19919107854366302, 0.04417213425040245, 0.30350348353385925, -1.376236081123352, -0.40194761753082275, 0.45156604051589966, 0.7977567911148071, 0.4627654254436493, 0.9746543169021606, 0.632631242275238, 0.09329439699649811, -0.014640008099377155, -0.37303030490875244, -0.43517595529556274, 0.11202339082956314, -0.1844819188117981, -0.19993025064468384, -0.8116483092308044, 0.41616323590278625, 0.8466446399688721, 0.458117812871933, -0.45170336961746216, 0.6124818325042725, 0.11794645339250565, 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nlplabtdtu/edu_child_v2	nlplabtdtu	2023-12-15T08:01:29Z	7	0	[ "region:us" ]	null	2023-12-15T08:01:25Z	--- dataset_info: features: - name: content dtype: string - name: metadata struct: - name: metadata struct: - name: id dtype: string - name: seq_num dtype: int64 - name: source dtype: string - name: vector sequence: float64 splits: - name: train num_bytes: 3914344 num_examples: 302 download_size: 3072056 dataset_size: 3914344 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, 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LabHC/Bias_in_Bios_stratify	LabHC	2023-12-15T09:37:49Z	7	0	[ "region:us" ]	null	2023-12-15T09:30:43Z	--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: gender dtype: int64 splits: - name: train num_bytes: 41703222 num_examples: 100000 - name: test num_bytes: 848564 num_examples: 2000 - name: dev num_bytes: 836655 num_examples: 2000 download_size: 26189142 dataset_size: 43388441 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, 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jlbaker361/wikiart-balanced25	jlbaker361	2023-12-15T17:06:52Z	7	0	[ "region:us" ]	null	2023-12-15T17:06:50Z	--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: style dtype: string - name: name dtype: string - name: gen_style dtype: string splits: - name: train num_bytes: 23078706.68 num_examples: 607 - name: test num_bytes: 2987451.32 num_examples: 68 download_size: 26031378 dataset_size: 26066158.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---	[ -0.12853379547595978, -0.1861676573753357, 0.6529129147529602, 0.49436256289482117, -0.1931929588317871, 0.2360745668411255, 0.3607199192047119, 0.05056329071521759, 0.5793652534484863, 0.7400139570236206, -0.6508101224899292, -0.2378396987915039, -0.7102251052856445, -0.047825805842876434, -0.3894752264022827, 0.8470761775970459, -0.09598271548748016, 0.024005070328712463, 0.04712017625570297, -0.14317847788333893, -0.6121039390563965, 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syp1229/Y_total_freq	syp1229	2023-12-15T18:34:31Z	7	0	[ "region:us" ]	null	2023-12-15T18:22:41Z	--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sample_rate dtype: int64 - name: text dtype: string - name: scriptId dtype: int64 - name: fileNm dtype: string - name: recrdTime dtype: float64 - name: recrdQuality dtype: int64 - name: recrdDt dtype: string - name: scriptSetNo dtype: string - name: recrdEnvrn dtype: string - name: colctUnitCode dtype: string - name: cityCode dtype: string - name: recrdUnit dtype: string - name: convrsThema dtype: string - name: gender dtype: string - name: recorderId dtype: string - name: age dtype: int64 splits: - name: train num_bytes: 9454815261 num_examples: 12401 - name: test num_bytes: 504186814 num_examples: 605 download_size: 6285334123 dataset_size: 9959002075 --- # Dataset Card for "Y_total_freq" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6808745861053467, -0.34543588757514954, 0.06028275191783905, 0.1488773673772812, -0.26733753085136414, -0.09768974781036377, 0.45295414328575134, -0.24510923027992249, 1.0009478330612183, 0.6500617861747742, -0.9793391227722168, -0.7152205109596252, -0.5180101990699768, -0.2801065444946289, -0.4217005968093872, 1.300032615661621, 0.35890769958496094, -0.03690456598997116, -0.684095025062561, -0.32098427414894104, -0.5486053228378296, -0.5747758150100708, -0.4437585473060608, -0.6377108693122864, 0.9076318740844727, 0.7193207740783691, 0.2783956229686737, 0.23729842901229858, 0.7137851119041443, 0.18585491180419922, 0.3154642581939697, -0.23257917165756226, -0.6657266616821289, -0.03568066284060478, 0.009020388126373291, -0.5982252359390259, -1.0584802627563477, 0.0663159117102623, 0.7373491525650024, 0.7551095485687256, -0.3259032666683197, 0.7845212817192078, 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vivekdugale/llama2_mental_health_dataset_172	vivekdugale	2023-12-16T06:04:32Z	7	0	[ "region:us" ]	null	2023-12-16T06:03:27Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 189265 num_examples: 172 download_size: 102246 dataset_size: 189265 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, -0.06787490844726562, 0.3002279996871948, 0.5120974779129028, 0.8275896310806274, 0.3960292339324951, 0.5030564665794373, 1.7515558004379272, 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nam194/codesum_java_512_128	nam194	2023-12-16T09:05:51Z	7	0	[ "region:us" ]	null	2023-12-16T08:42:37Z	--- dataset_info: features: - name: hexsha dtype: string - name: repo dtype: string - name: path dtype: string - name: license sequence: string - name: language dtype: string - name: identifier dtype: string - name: return_type dtype: string - name: original_string dtype: string - name: original_docstring dtype: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: code dtype: string - name: code_tokens sequence: string - name: short_docstring dtype: string - name: short_docstring_tokens sequence: string - name: comment sequence: string - name: parameters list: - name: param dtype: string - name: type dtype: string - name: docstring_params struct: - name: returns list: - name: docstring dtype: string - name: docstring_tokens sequence: string - name: type dtype: string - name: raises list: - name: docstring dtype: string - name: docstring_tokens sequence: string - name: type dtype: string - name: params list: - name: identifier dtype: string - name: type dtype: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: default dtype: string - name: is_optional dtype: bool - name: outlier_params list: - name: identifier dtype: string - name: type dtype: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: default dtype: string - name: is_optional dtype: bool - name: others list: - name: identifier dtype: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 49797821738 num_examples: 6629193 download_size: 9683784722 dataset_size: 49797821738 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, 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ticoAg/cotinus-poem	ticoAg	2023-12-16T13:57:41Z	7	0	[ "license:apache-2.0", "region:us" ]	null	2023-12-16T13:47:43Z	--- license: apache-2.0 ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 0.024004995822906494, 0.0471201166510582, -0.14317826926708221, -0.6121037602424622, -0.04771745204925537, -1.0524537563323975, -0.06787490844726562, 0.3002279996871948, 0.5120974779129028, 0.8275896310806274, 0.3960292339324951, 0.5030564665794373, 1.7515558004379272, -0.08836928009986877, -0.22754442691802979, -0.4589202404022217, 0.42230692505836487, -0.33277368545532227, -0.4213372468948364, -0.26241639256477356, -0.07449338585138321, 0.32380396127700806, 0.7903713583946228, 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anurag2op/pcam-augmented-train-test-val-50k	anurag2op	2023-12-16T13:55:24Z	7	0	[ "region:us" ]	null	2023-12-16T13:51:24Z	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': non_tumor '1': tumor splits: - name: train num_bytes: 630998760.0 num_examples: 30000 - name: validation num_bytes: 210044410.0 num_examples: 10000 - name: test num_bytes: 211024140.0 num_examples: 10000 download_size: 1050350091 dataset_size: 1052067310.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---	[ -0.1285337507724762, -0.186167910695076, 0.6529128551483154, 0.4943626821041107, -0.1931932419538498, 0.23607468605041504, 0.36071985960006714, 0.05056324973702431, 0.5793652534484863, 0.740013837814331, -0.6508103013038635, -0.23783960938453674, -0.710224986076355, -0.04782586544752121, -0.3894752264022827, 0.8470760583877563, -0.09598266333341599, 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RikoteMaster/emotion_recog_sample	RikoteMaster	2023-12-17T18:18:18Z	7	0	[ "region:us" ]	null	2023-12-17T18:18:16Z	--- dataset_info: features: - name: Text_processed dtype: string - name: Emotion dtype: string - name: Augmented dtype: bool - name: text dtype: string splits: - name: train num_bytes: 35573 num_examples: 63 download_size: 18616 dataset_size: 35573 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.12853381037712097, -0.1861676573753357, 0.6529128551483154, 0.49436283111572266, -0.19319342076778412, 0.236074298620224, 0.3607199192047119, 0.05056328698992729, 0.579365611076355, 0.7400140166282654, -0.6508103609085083, -0.23783956468105316, -0.7102248668670654, -0.04782595485448837, -0.38947513699531555, 0.8470762968063354, -0.09598278254270554, 0.024005025625228882, 0.04711998254060745, -0.14317822456359863, -0.6121044754981995, -0.04771703481674194, -1.0524537563323975, -0.067874476313591, 0.3002280592918396, 0.5120974779129028, 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Rootreck/so-vits-svc-4.0-ru-The_Witcher_3_Wild_Hunt	Rootreck	2023-12-15T22:40:36Z	6	0	[ "language:ru", "region:us" ]	null	2023-08-01T07:17:28Z	--- language: - ru --- Это тренировочные данные моделей голосов персонажей из "Ведьмак 3: Дикая охота" для so-vits-svc-4.1.1	[ -0.08843633532524109, -0.4755905568599701, 0.5487224459648132, 0.2729912996292114, -0.6056276559829712, -0.19090920686721802, 0.5659838318824768, -0.13809141516685486, 0.9648236632347107, 0.22244444489479065, -1.2467750310897827, -0.5445683002471924, -0.22594986855983734, -0.06289854645729065, -0.4034777879714966, 0.7234519124031067, -0.07728651911020279, 0.3868637681007385, -0.5165520906448364, 0.021143198013305664, -0.4890950322151184, -0.4143894612789154, -0.23739181458950043, -0.0870508998632431, 0.09759724140167236, 0.5010711550712585, 0.2387215495109558, -0.5161626935005188, 0.8193671107292175, 0.5013566017150879, 0.43039369583129883, -0.5281846523284912, -0.06709570437669754, 0.5568045973777771, 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TinyPixel/lima	TinyPixel	2023-12-15T05:20:55Z	6	0	[ "region:us" ]	null	2023-10-02T06:24:35Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2904960 num_examples: 1030 download_size: 1695740 dataset_size: 2904960 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "lima" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5389965772628784, -0.36255785822868347, 0.38189685344696045, 0.5671199560165405, -0.5118902921676636, -0.18525081872940063, 0.4728897213935852, -0.2817007601261139, 1.0606367588043213, 0.6405720710754395, -0.8715969324111938, -0.8793647885322571, -0.9019495844841003, -0.1909424066543579, -0.4748120903968811, 1.3828206062316895, 0.047562360763549805, 0.027316328138113022, -0.004850602243095636, -0.2774559557437897, -0.27200639247894287, -0.501759946346283, -0.9096890091896057, -0.2341575026512146, 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laion/tripple-ocr	laion	2023-12-15T16:54:09Z	6	8	[ "source_datasets:wendlerc/RenderedText", "source_datasets:wendlerc/CaptionedSynthText", "source_datasets:AtlasUnified/atlas-pdf-img-cluster", "license:cc0-1.0", "region:us" ]	null	2023-10-24T12:16:51Z	--- license: cc0-1.0 dataset_info: features: - name: key dtype: string - name: image dtype: image - name: ocr_annotation_text sequence: string - name: ocr_annotation_bb_relative sequence: sequence: sequence: float64 - name: caption dtype: string - name: source_tar dtype: string splits: - name: train num_examples: 20 source_datasets: - wendlerc/RenderedText - wendlerc/CaptionedSynthText - AtlasUnified/atlas-pdf-img-cluster configs: - config_name: default data_files: - split: train path: data/*.parquet ---	[ -0.12853379547595978, -0.1861676573753357, 0.6529129147529602, 0.49436256289482117, -0.1931929588317871, 0.2360745668411255, 0.3607199192047119, 0.05056329071521759, 0.5793652534484863, 0.7400139570236206, -0.6508101224899292, 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MohamedRashad/multilingual-tts	MohamedRashad	2023-12-12T21:04:06Z	6	1	[ "task_categories:text-to-speech", "size_categories:10K<n<100K", "language:ar", "language:en", "language:zh", "language:es", "language:fr", "language:hi", "language:ru", "language:pt", "language:ja", "language:de", "language:tr", "language:bn", "language:id", "language:ur", "language:vi", "license:gpl-3.0", "region:us" ]	[ "text-to-speech" ]	2023-12-01T23:46:06Z	--- license: gpl-3.0 dataset_info: features: - name: text dtype: string - name: speaker dtype: string - name: languages dtype: string - name: audio dtype: audio splits: - name: train num_bytes: 1561588634.72 num_examples: 25540 download_size: 1548036818 dataset_size: 1561588634.72 task_categories: - text-to-speech language: - ar - en - zh - es - fr - hi - ru - pt - ja - de - tr - bn - id - ur - vi pretty_name: Multilingual TTS size_categories: - 10K<n<100K --- # Before Anything and Everything ⚱ _In the time of writing this Dataset Card, ~17,490~ 18,412 civilian has been killed in Palestine (~7,870~ 8,000 are children and ~6,121~ 6,200 are women)._ Seek any non-profit organization to help them with what you can (For myself, [I use Mersal](https://www.every.org/mersal/f/support-humanitarian)) 🇵🇸 ## Dataset Description The Multilingual TTS dataset is an exceptional compilation of text-to-speech (TTS) samples, meticulously crafted to showcase the richness and diversity of human languages. This dataset encompasses a variety of real-world sentences in fifteen prominent languages, carefully chosen to reflect global linguistic diversity. Each sample is accompanied by its corresponding high-quality audio output. <style> .image-container { display: flex; justify-content: center; align-items: center; height: 65vh; margin: 0; } .image-container img { max-width: 48%; /* Adjust the width as needed / height: auto; } </style> <div class="image-container"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6116d0584ef9fdfbf45dc4d9/UX0s8S2yWSJ3NbbvmOJOi.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6116d0584ef9fdfbf45dc4d9/zIyPCWH7Y58gLVCeIfq4n.png"> </div> ## Key Features: 1. Language Diversity: The dataset covers a spectrum of languages, including Bengali, Mandarin Chinese, Turkish, Hindi, French, Vietnamese, Portuguese, Spanish, Japanese, German, Russian, Indonesian, Standard Arabic, English, and Urdu. This wide linguistic representation ensures inclusivity and applicability to a global audience. 3. Real-World Sentences: Comprising 25,000 samples, the dataset mirrors authentic communication scenarios. Sentences span diverse topics, ranging from everyday conversations to informative texts and news snippets, providing a comprehensive linguistic landscape. 4. Multilingual Sentences: A distinctive feature of this dataset is its inclusion of sentences that seamlessly integrate multiple languages. Each sample combines at least two languages, capturing the intricate dynamics of multilingual communication and rendering the dataset particularly valuable for training and evaluating multilingual TTS systems. 5. Audio Quality: Special attention has been given to the audio quality of each sample. The audio outputs are meticulously designed to be clear, natural-sounding, and faithful representations of the corresponding text, ensuring a rich auditory experience. 6. Generated by GPT-4 and elevenlabs: The dataset is the result of a collaboration between GPT-4 and elevenlabs, combining cutting-edge language generation capabilities with domain expertise. This collaboration guarantees a high level of accuracy, coherence, and linguistic nuance in both the text and audio components. ## Potential Use Cases: 1. Multilingual TTS Model Training: Researchers and developers can leverage this dataset to train and refine multilingual TTS models, enhancing their proficiency across a diverse array of languages. 2. Cross-Language Evaluation: The dataset serves as a valuable resource for evaluating TTS systems in handling multilingual scenarios, offering a benchmark for assessing model capabilities across different languages. 3. Language Integration Testing: Developers working on applications requiring multilingual TTS functionality can utilize this dataset to test and optimize language integration, ensuring a seamless user experience across various linguistic contexts. ## Acknowledgments: The creation of the Multilingual TTS dataset was made possible through the collaborative efforts of OpenAI's GPT-4* and the expertise of Elevenlabs Multilingual V2. We extend our gratitude to the AI and language processing communities for their continuous support in advancing the field of multilingual TTS. This dataset stands as a significant contribution, fostering innovation and progress in language technologies.	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jlbaker361/wikiart	jlbaker361	2023-12-15T14:29:19Z	6	0	[ "region:us" ]	null	2023-12-08T02:32:30Z	--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: style dtype: string - name: name dtype: string - name: gen_style dtype: string splits: - name: train num_bytes: 2702920462.8247585 num_examples: 73304 - name: test num_bytes: 295758759.14224136 num_examples: 8145 download_size: 2970796741 dataset_size: 2998679221.967 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---	[ -0.12853394448757172, -0.1861671805381775, 0.6529130339622498, 0.49436283111572266, -0.1931932270526886, 0.23607474565505981, 0.3607197403907776, 0.05056331306695938, 0.5793652534484863, 0.7400139570236206, -0.6508102416992188, -0.23783963918685913, -0.7102248668670654, -0.04782587289810181, -0.3894752860069275, 0.8470762968063354, -0.09598271548748016, 0.02400510013103485, 0.047119975090026855, -0.14317838847637177, 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kanishka/counterfactual_babylm_aann_indef_naan	kanishka	2023-12-13T01:24:56Z	6	0	[ "region:us" ]	null	2023-12-11T22:28:37Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 581833803 num_examples: 11632617 - name: validation num_bytes: 56120230 num_examples: 1026747 download_size: 0 dataset_size: 637954033 --- # Dataset Card for "counterfactual_babylm_aann_indef_naan" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.5666398406028748, -0.3432304561138153, -0.09990571439266205, 0.4291342496871948, -0.27132970094680786, -0.20861968398094177, 0.47791364789009094, -0.21584080159664154, 0.8246402740478516, 0.3734784722328186, -1.0357749462127686, -0.5340412855148315, -0.6240774393081665, -0.22339779138565063, -0.41906049847602844, 1.2610633373260498, 0.24228432774543762, 0.14970828592777252, -0.3653731048107147, -0.3768642246723175, -0.4723348319530487, 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pankajemplay/mistral-intent-data-1816	pankajemplay	2023-12-12T02:26:38Z	6	0	[ "region:us" ]	null	2023-12-12T02:26:35Z	--- dataset_info: features: - name: User Query dtype: string - name: Intent dtype: string - name: id type dtype: string - name: id value dtype: string - name: id slot filled dtype: bool - name: Task dtype: string - name: task slot filled dtype: bool - name: Bot Response dtype: string - name: text dtype: string - name: instruction dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1155635 num_examples: 1816 download_size: 210883 dataset_size: 1155635 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339742898941, -0.18616768717765808, 0.6529124975204468, 0.4943625330924988, -0.1931932270526886, 0.2360745370388031, 0.3607196509838104, 0.05056314915418625, 0.579365611076355, 0.7400140166282654, -0.6508101224899292, -0.23783965408802032, -0.7102248072624207, -0.047825951129198074, 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ouvic215/Soldering-Data-pix2pix-1209-white-2	ouvic215	2023-12-12T07:44:45Z	6	0	[ "region:us" ]	null	2023-12-12T07:40:53Z	--- dataset_info: features: - name: mask_image dtype: image - name: text dtype: string - name: image dtype: image splits: - name: train num_bytes: 2087059454.5 num_examples: 25950 download_size: 1724200255 dataset_size: 2087059454.5 --- # Dataset Card for "Soldering-Data-pix2pix-1209-white-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.24845366179943085, -0.12908603250980377, 0.4194631278514862, 0.2108626812696457, -0.19623619318008423, -0.06763909012079239, 0.2585945129394531, 0.3703473210334778, 0.5813180208206177, 0.377646803855896, -0.86158287525177, -0.5781551003456116, -0.5416748523712158, -0.6692975759506226, -0.011847306042909622, 1.2155489921569824, 0.0706801488995552, -0.17309677600860596, -0.34609556198120117, 0.08423150330781937, -0.6010160446166992, 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ArasAyen/pc512	ArasAyen	2023-12-12T09:27:56Z	6	0	[ "region:us" ]	null	2023-12-12T09:27:40Z	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 124752550.0 num_examples: 302 download_size: 124617017 dataset_size: 124752550.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "pc512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6634642481803894, -0.1258493959903717, 0.360552042722702, 0.37894687056541443, -0.31751900911331177, -0.043437350541353226, 0.18076498806476593, -0.2529746890068054, 0.8683624863624573, 0.4364704489707947, -0.9693418741226196, -0.5789530277252197, -0.4061506390571594, -0.23016873002052307, -0.14234285056591034, 1.10293710231781, 0.03291754424571991, 0.10883936285972595, -0.36748793721199036, 0.2608577013015747, -0.4446844160556793, -0.648817777633667, 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kristmh/high_vs_random	kristmh	2023-12-12T10:50:24Z	6	0	[ "region:us" ]	null	2023-12-12T10:49:18Z	--- configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validate path: data/validate-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: text_clean dtype: string - name: labels dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: test num_bytes: 35090618 num_examples: 42053 - name: train num_bytes: 284101969 num_examples: 336420 - name: validate num_bytes: 36222999 num_examples: 42052 download_size: 175883924 dataset_size: 355415586 --- # Dataset Card for "high_vs_random" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	[ -0.6577766537666321, -0.531603217124939, 0.05903734266757965, 0.3513995110988617, -0.09766983240842819, -0.14650197327136993, 0.031069984659552574, -0.26709333062171936, 0.9614413976669312, 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shyzii/exrcise-llama2-converted	shyzii	2023-12-12T11:19:26Z	6	0	[ "region:us" ]	null	2023-12-12T11:07:12Z	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 7877 num_examples: 48 download_size: 3063 dataset_size: 7877 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.12853379547595978, -0.1861676573753357, 0.6529129147529602, 0.49436256289482117, -0.1931929588317871, 0.2360745668411255, 0.3607199192047119, 0.05056329071521759, 0.5793652534484863, 0.7400139570236206, -0.6508101224899292, -0.2378396987915039, -0.7102251052856445, -0.047825805842876434, -0.3894752264022827, 0.8470761775970459, -0.09598271548748016, 0.024005070328712463, 0.04712017625570297, -0.14317847788333893, -0.6121039390563965, -0.04771736264228821, -1.052453637123108, -0.06787490844726562, 0.3002278804779053, 0.5120974779129028, 0.8275895118713379, 0.39602914452552795, 0.5030565857887268, 1.7515559196472168, -0.0883692055940628, 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mlsa-iai-msu-lab/ru_sci_bench	mlsa-iai-msu-lab	2023-12-17T23:23:47Z	6	0	[ "size_categories:100K<n<1M", "language:ru", "language:en", "license:mit", "region:us" ]	null	2023-12-12T15:43:37Z	--- license: mit dataset_info: features: - name: paper_id dtype: int64 - name: title dtype: string - name: abstract dtype: string splits: - name: en num_bytes: 170801402 num_examples: 182436 - name: ru num_bytes: 312579538 num_examples: 194071 download_size: 246316956 dataset_size: 483380940 configs: - config_name: default data_files: - split: en path: data/en-* - split: ru path: data/ru-* language: - ru - en pretty_name: ruSciBench size_categories: - 100K<n<1M --- # ruSciBench text data Titles and abstracts of research papers in english and russian for ruSciBench benchmark. [Habr post](https://habr.com/ru/articles/781032/) [Benchmark code](https://github.com/mlsa-iai-msu-lab/ru-sci-bench) [Google Colab example](https://colab.research.google.com/drive/1S04UOLRYuI2J5qM2QkoWqSbv6GO94YrP?usp=sharing) ### Authors Benchmark developed by MLSA Lab of Institute for AI, MSU. ### Acknowledgement The research is part of the project #23-Ш05-21 SES MSU "Development of mathematical methods of machine learning for processing large-volume textual scientific information". We would like to thank [eLibrary](https://elibrary.ru/) for provided datasets. ### Contacts Nikolai Gerasimenko ([email protected]), Alexey Vatolin ([email protected])	[ 0.11943270266056061, -0.39327186346054077, 0.4845738112926483, 0.4576851725578308, -0.22264492511749268, 0.13632427155971527, -0.15766258537769318, -0.36999598145484924, 0.06786233931779861, 0.057665031403303146, -0.22930872440338135, -0.7584489583969116, -0.4443807005882263, 0.4363544285297394, -0.3249189555644989, 1.243370771408081, -0.22583544254302979, 0.7691501975059509, -0.7404705882072449, -0.04937325417995453, 0.16626711189746857, -0.2062406986951828, -0.40936607122421265, -0.35444656014442444, 0.2651919424533844, 0.28717875480651855, 0.26915380358695984, 0.6193535923957825, 0.7658087015151978, 0.3525244891643524, 0.008264337666332722, -0.1343429982662201, -0.22713623940944672, 0.29135075211524963, -0.19687359035015106, -0.37198251485824585, -0.6242656707763672, -0.29209020733833313, 0.9599493741989136, 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Seongill/NQ_5_adversary_v2	Seongill	2023-12-13T06:34:34Z	6	0	[ "region:us" ]	null	2023-12-13T06:34:29Z	--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: answer_sent sequence: string - name: hasanswer dtype: bool - name: id dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: has_answer dtype: bool - name: random_sub dtype: string - name: similar_sub dtype: string - name: ent_type dtype: string - name: new_ctxs list: - name: answer_sent sequence: string - name: hasanswer dtype: bool - name: id dtype: string - name: is_adv dtype: bool - name: new_answer_sent dtype: string - name: original_text dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: num_advs dtype: int64 - name: num_ctxs dtype: int64 splits: - name: train num_bytes: 27255069 num_examples: 3610 download_size: 15221336 dataset_size: 27255069 configs: - 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MMoin/mini-platypus	MMoin	2023-12-14T11:27:27Z	6	0	[ "region:us" ]	null	2023-12-13T11:25:14Z	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4186564 num_examples: 1000 download_size: 2245925 dataset_size: 4186564 configs: - config_name: default data_files: - split: train path: data/train-* ---	[ -0.1285339742898941, -0.18616768717765808, 0.6529124975204468, 0.4943625330924988, -0.1931932270526886, 0.2360745370388031, 0.3607196509838104, 0.05056314915418625, 0.579365611076355, 0.7400140166282654, -0.6508101224899292, -0.23783965408802032, -0.7102248072624207, -0.047825951129198074, -0.389475017786026, 0.8470762968063354, -0.09598258882761002, 0.024005070328712463, 0.047120027244091034, -0.1431782841682434, -0.612104058265686, -0.047717005014419556, -1.0524537563323975, -0.06787464022636414, 0.3002280592918396, 0.5120973587036133, 0.8275895118713379, 0.39602944254875183, 0.5030564069747925, 1.7515558004379272, 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open-llm-leaderboard/details_upstage__SOLAR-10.7B-Instruct-v1.0

open-llm-leaderboard

2023-12-13T21:06:09Z

9

0

[ "region:us" ]

null

2023-12-13T21:05:25Z

--- pretty_name: Evaluation run of upstage/SOLAR-10.7B-Instruct-v1.0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)\ \ 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_upstage__SOLAR-10.7B-Instruct-v1.0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-13T21:02:33.929144](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__SOLAR-10.7B-Instruct-v1.0/blob/main/results_2023-12-13T21-02-33.929144.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\": 0.6657586984797939,\n\ \ \"acc_stderr\": 0.03165995758526614,\n \"acc_norm\": 0.6666511531376961,\n\ \ \"acc_norm_stderr\": 0.0323050384069596,\n \"mc1\": 0.5667074663402693,\n\ \ \"mc1_stderr\": 0.017347024450107485,\n \"mc2\": 0.7142943510205136,\n\ \ \"mc2_stderr\": 0.015024530295000761\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6808873720136519,\n \"acc_stderr\": 0.013621696119173307,\n\ \ \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.01325001257939344\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7070304720175263,\n\ \ \"acc_stderr\": 0.004541944342035901,\n \"acc_norm\": 0.8815972913762199,\n\ \ \"acc_norm_stderr\": 0.003224240722351317\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \ \ },\n \"harness|hendrycksTest-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\": 0.7368421052631579,\n \"acc_stderr\": 0.03583496176361072,\n\ \ \"acc_norm\": 0.7368421052631579,\n \"acc_norm_stderr\": 0.03583496176361072\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.74,\n\ \ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \ \ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.02872750295788027,\n\ \ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.02872750295788027\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\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \ \ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.52,\n \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\": 0.52,\n\ \ \"acc_norm_stderr\": 0.05021167315686779\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \ \ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\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\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6297872340425532,\n \"acc_stderr\": 0.03156564682236785,\n\ \ \"acc_norm\": 0.6297872340425532,\n \"acc_norm_stderr\": 0.03156564682236785\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6413793103448275,\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\"\ : 0.47883597883597884,\n \"acc_stderr\": 0.025728230952130726,\n \"\ acc_norm\": 0.47883597883597884,\n \"acc_norm_stderr\": 0.025728230952130726\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.8032258064516129,\n \"acc_stderr\": 0.022616409420742025,\n \"\ acc_norm\": 0.8032258064516129,\n \"acc_norm_stderr\": 0.022616409420742025\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5172413793103449,\n \"acc_stderr\": 0.03515895551165698,\n \"\ acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.03515895551165698\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\": 0.8,\n \"acc_stderr\": 0.031234752377721175,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721175\n \ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8737373737373737,\n \"acc_stderr\": 0.02366435940288023,\n \"\ acc_norm\": 0.8737373737373737,\n \"acc_norm_stderr\": 0.02366435940288023\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3814814814814815,\n \"acc_stderr\": 0.029616718927497593,\n \ \ \"acc_norm\": 0.3814814814814815,\n \"acc_norm_stderr\": 0.029616718927497593\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7184873949579832,\n \"acc_stderr\": 0.02921354941437217,\n \ \ \"acc_norm\": 0.7184873949579832,\n \"acc_norm_stderr\": 0.02921354941437217\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\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5555555555555556,\n \"acc_stderr\": 0.03388857118502325,\n \"\ acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.03388857118502325\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.0251956584289318,\n \"acc_norm\"\ : 0.8480392156862745,\n \"acc_norm_stderr\": 0.0251956584289318\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\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n\ \ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\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\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\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\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8033205619412516,\n\ \ \"acc_stderr\": 0.014214138556913917,\n \"acc_norm\": 0.8033205619412516,\n\ \ \"acc_norm_stderr\": 0.014214138556913917\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7601156069364162,\n \"acc_stderr\": 0.022989592543123567,\n\ \ \"acc_norm\": 0.7601156069364162,\n \"acc_norm_stderr\": 0.022989592543123567\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39329608938547483,\n\ \ \"acc_stderr\": 0.016337268694270112,\n \"acc_norm\": 0.39329608938547483,\n\ \ \"acc_norm_stderr\": 0.016337268694270112\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\": 0.729903536977492,\n\ \ \"acc_stderr\": 0.02521804037341062,\n \"acc_norm\": 0.729903536977492,\n\ \ \"acc_norm_stderr\": 0.02521804037341062\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7901234567901234,\n \"acc_stderr\": 0.02265834408598137,\n\ \ \"acc_norm\": 0.7901234567901234,\n \"acc_norm_stderr\": 0.02265834408598137\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4934810951760104,\n\ \ \"acc_stderr\": 0.012769150688867503,\n \"acc_norm\": 0.4934810951760104,\n\ \ \"acc_norm_stderr\": 0.012769150688867503\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.026679252270103135,\n\ \ \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.026679252270103135\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6911764705882353,\n \"acc_stderr\": 0.018690850273595294,\n \ \ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.018690850273595294\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.0282638899437846,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.0282638899437846\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598052,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598052\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03126781714663179,\n\ \ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03126781714663179\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5667074663402693,\n\ \ \"mc1_stderr\": 0.017347024450107485,\n \"mc2\": 0.7142943510205136,\n\ \ \"mc2_stderr\": 0.015024530295000761\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8358326756116812,\n \"acc_stderr\": 0.01041084977522279\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6474601971190296,\n \ \ \"acc_stderr\": 0.013159909755930337\n }\n}\n```" repo_url: https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|arc:challenge|25_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-13T21-02-33.929144.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|gsm8k|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hellaswag|10_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-management|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|truthfulqa:mc|0_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-13T21-02-33.929144.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|winogrande|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-13T21-02-33.929144.parquet' - config_name: results data_files: - split: 2023_12_13T21_02_33.929144 path: - results_2023-12-13T21-02-33.929144.parquet - split: latest path: - results_2023-12-13T21-02-33.929144.parquet --- # Dataset Card for Evaluation run of upstage/SOLAR-10.7B-Instruct-v1.0  Dataset automatically created during the evaluation run of model [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) 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_upstage__SOLAR-10.7B-Instruct-v1.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-13T21:02:33.929144](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__SOLAR-10.7B-Instruct-v1.0/blob/main/results_2023-12-13T21-02-33.929144.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": 0.6657586984797939, "acc_stderr": 0.03165995758526614, "acc_norm": 0.6666511531376961, "acc_norm_stderr": 0.0323050384069596, "mc1": 0.5667074663402693, "mc1_stderr": 0.017347024450107485, "mc2": 0.7142943510205136, "mc2_stderr": 0.015024530295000761 }, "harness|arc:challenge|25": { "acc": 0.6808873720136519, "acc_stderr": 0.013621696119173307, "acc_norm": 0.7107508532423208, "acc_norm_stderr": 0.01325001257939344 }, "harness|hellaswag|10": { "acc": 0.7070304720175263, "acc_stderr": 0.004541944342035901, "acc_norm": 0.8815972913762199, "acc_norm_stderr": 0.003224240722351317 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "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": 0.7368421052631579, "acc_stderr": 0.03583496176361072, "acc_norm": 0.7368421052631579, "acc_norm_stderr": 0.03583496176361072 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6792452830188679, "acc_stderr": 0.02872750295788027, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.02872750295788027 }, "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": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.05021167315686779, "acc_norm": 0.52, "acc_norm_stderr": 0.05021167315686779 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6647398843930635, "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": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6297872340425532, "acc_stderr": 0.03156564682236785, "acc_norm": 0.6297872340425532, "acc_norm_stderr": 0.03156564682236785 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6413793103448275, "acc_stderr": 0.039966295748767186, "acc_norm": 0.6413793103448275, "acc_norm_stderr": 0.039966295748767186 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.47883597883597884, "acc_stderr": 0.025728230952130726, "acc_norm": 0.47883597883597884, "acc_norm_stderr": 0.025728230952130726 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4444444444444444, "acc_stderr": 0.044444444444444495, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.044444444444444495 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8032258064516129, "acc_stderr": 0.022616409420742025, "acc_norm": 0.8032258064516129, "acc_norm_stderr": 0.022616409420742025 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.03515895551165698, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.03515895551165698 }, "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": 0.8, "acc_stderr": 0.031234752377721175, "acc_norm": 0.8, "acc_norm_stderr": 0.031234752377721175 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8737373737373737, "acc_stderr": 0.02366435940288023, "acc_norm": 0.8737373737373737, "acc_norm_stderr": 0.02366435940288023 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328973, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328973 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3814814814814815, "acc_stderr": 0.029616718927497593, "acc_norm": 0.3814814814814815, "acc_norm_stderr": 0.029616718927497593 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7184873949579832, "acc_stderr": 0.02921354941437217, "acc_norm": 0.7184873949579832, "acc_norm_stderr": 0.02921354941437217 }, "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": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5555555555555556, "acc_stderr": 0.03388857118502325, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.03388857118502325 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8480392156862745, "acc_stderr": 0.0251956584289318, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.0251956584289318 }, "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": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7480916030534351, "acc_stderr": 0.03807387116306086, "acc_norm": 0.7480916030534351, "acc_norm_stderr": 0.03807387116306086 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8055555555555556, "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": 0.44642857142857145, "acc_stderr": 0.047184714852195886, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.047184714852195886 }, "harness|hendrycksTest-management|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8589743589743589, "acc_stderr": 0.02280138253459753, "acc_norm": 0.8589743589743589, "acc_norm_stderr": 0.02280138253459753 }, "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": 0.8033205619412516, "acc_stderr": 0.014214138556913917, "acc_norm": 0.8033205619412516, "acc_norm_stderr": 0.014214138556913917 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7601156069364162, "acc_stderr": 0.022989592543123567, "acc_norm": 0.7601156069364162, "acc_norm_stderr": 0.022989592543123567 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.39329608938547483, "acc_stderr": 0.016337268694270112, "acc_norm": 0.39329608938547483, "acc_norm_stderr": 0.016337268694270112 }, "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": 0.729903536977492, "acc_stderr": 0.02521804037341062, "acc_norm": 0.729903536977492, "acc_norm_stderr": 0.02521804037341062 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7901234567901234, "acc_stderr": 0.02265834408598137, "acc_norm": 0.7901234567901234, "acc_norm_stderr": 0.02265834408598137 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4934810951760104, "acc_stderr": 0.012769150688867503, "acc_norm": 0.4934810951760104, "acc_norm_stderr": 0.012769150688867503 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7389705882352942, "acc_stderr": 0.026679252270103135, "acc_norm": 0.7389705882352942, "acc_norm_stderr": 0.026679252270103135 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6911764705882353, "acc_stderr": 0.018690850273595294, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.018690850273595294 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7346938775510204, "acc_stderr": 0.0282638899437846, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.0282638899437846 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616913, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616913 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.9, "acc_stderr": 0.030151134457776334, "acc_norm": 0.9, "acc_norm_stderr": 0.030151134457776334 }, "harness|hendrycksTest-virology|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598052, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598052 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7894736842105263, "acc_stderr": 0.03126781714663179, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.03126781714663179 }, "harness|truthfulqa:mc|0": { "mc1": 0.5667074663402693, "mc1_stderr": 0.017347024450107485, "mc2": 0.7142943510205136, "mc2_stderr": 0.015024530295000761 }, "harness|winogrande|5": { "acc": 0.8358326756116812, "acc_stderr": 0.01041084977522279 }, "harness|gsm8k|5": { "acc": 0.6474601971190296, "acc_stderr": 0.013159909755930337 } } ``` ## 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]

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T1m3y/CodeparrotV1

T1m3y

2023-12-14T09:38:30Z

9

0

[ "region:us" ]

null

2023-12-14T09:37:07Z

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: assembly dtype: string - name: c_source_code dtype: string splits: - name: train num_bytes: 427678057 num_examples: 231628 - name: val num_bytes: 108906658 num_examples: 57908 - name: test num_bytes: 134041825 num_examples: 72384 download_size: 181124979 dataset_size: 670626540 --- # Dataset Card for "CodeparrotV1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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abhishek-mungoli/srk_wittiness

abhishek-mungoli

2023-12-14T15:03:02Z

9

1

[ "license:cc", "region:us" ]

null

2023-12-14T14:51:50Z

--- license: cc --- # Shah Rukh Khan Wittiness Reply DataSet Shah Rukh Khan is known for his wit, humor, and charm. I have carefully crafted some questions and imagined how SRK would have replied to them using Chat-GPT. You can use this data to train a smaller open-source language model or for any other use case you have in mind.

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kevind13/vuejs-codellama

kevind13

2023-12-14T15:28:57Z

9

0

[ "region:us" ]

null

2023-12-14T15:28:56Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 620238 num_examples: 52 download_size: 322188 dataset_size: 620238 configs: - config_name: default data_files: - split: train path: data/train-* ---

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sproos/twitter-pairclass-es

sproos

2023-12-14T18:47:30Z

9

0

[ "region:us" ]

null

2023-12-14T17:14:54Z

--- dataset_info: features: - name: sent1 sequence: string - name: sent2 sequence: string - name: labels sequence: int64 splits: - name: train num_bytes: 11427395 num_examples: 1 download_size: 4228525 dataset_size: 11427395 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter-pairclass-es" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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MarkusStoll/cifar10-enrichments

MarkusStoll

2023-12-15T06:57:57Z

9

0

[ "region:us" ]

null

2023-12-15T06:57:45Z

--- dataset_info: features: - name: embedding sequence: float32 splits: - name: test num_bytes: 30760000 num_examples: 10000 download_size: 36966542 dataset_size: 30760000 configs: - config_name: default data_files: - split: test path: data/test-* --- # Dataset Card for "cifar10-enrichments" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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nlplabtdtu/edu_parent_v2

nlplabtdtu

2023-12-15T08:00:36Z

9

0

[ "region:us" ]

null

2023-12-15T08:00:32Z

--- dataset_info: features: - name: content dtype: string - name: metadata struct: - name: id dtype: string - name: seq_num dtype: int64 - name: source dtype: string splits: - name: train num_bytes: 165190 num_examples: 78 download_size: 82925 dataset_size: 165190 configs: - config_name: default data_files: - split: train path: data/train-* ---

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mayolore/wiki-knowledge

mayolore

2023-12-15T09:14:11Z

9

0

[ "license:mit", "region:us" ]

null

2023-12-15T08:55:29Z

--- license: mit ---

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thorirhrafn/rmh_ds2

thorirhrafn

2023-12-15T10:59:32Z

9

0

[ "region:us" ]

null

2023-12-15T10:59:22Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 127179121 num_examples: 56000 - name: test num_bytes: 50199223 num_examples: 16800 - name: valid num_bytes: 3436783.6 num_examples: 1440 download_size: 109255791 dataset_size: 180815127.6 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---

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Isotonic/pii-masking-200k

Isotonic

2023-12-15T16:43:25Z

9

0

[ "task_categories:conversational", "task_categories:text-classification", "task_categories:token-classification", "task_categories:table-question-answering", "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:summarization", "task_categories:feature-extraction", "task_categories:text-generation", "task_categories:text2text-generation", "multilinguality:multilingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "language:fr", "language:de", "language:it", "license:apache-2.0", "legal", "business", "psychology", "privacy", "region:us" ]

[ "conversational", "text-classification", "token-classification", "table-question-answering", "question-answering", "zero-shot-classification", "summarization", "feature-extraction", "text-generation", "text2text-generation" ]

2023-12-15T12:27:53Z

--- language: - en - fr - de - it license: apache-2.0 multilinguality: - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - conversational - text-classification - token-classification - table-question-answering - question-answering - zero-shot-classification - summarization - feature-extraction - text-generation - text2text-generation pretty_name: Ai4Privacy PII200k Dataset configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: masked_text dtype: string - name: unmasked_text dtype: string - name: privacy_mask dtype: string - name: span_labels dtype: string - name: bio_labels sequence: string - name: tokenised_text sequence: string - name: language dtype: string splits: - name: train num_bytes: 315574161 num_examples: 209261 download_size: 0 dataset_size: 315574161 tags: - legal - business - psychology - privacy --- # Purpose and Features World's largest open source privacy dataset. The purpose of the dataset is to train models to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs. The example texts have **54 PII classes** (types of sensitive data), targeting **229 discussion subjects / use cases** split across business, education, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...). Key facts: - Size: 13.6m text tokens in ~209k examples with 649k PII tokens (see [summary.json](summary.json)) - 4 languages, more to come! - English - French - German - Italian - Synthetic data generated using proprietary algorithms - No privacy violations! - Human-in-the-loop validated high quality dataset # Getting started Option 1: Python ```terminal pip install datasets ``` ```python from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k", data_files=["*.jsonl"]) ``` or ```python from datasets import load_dataset dataset = load_dataset("Isotonic/pii-masking-200k") # use "language" column ``` # Token distribution across PII classes We have taken steps to balance the token distribution across PII classes covered by the dataset. This graph shows the distribution of observations across the different PII classes in this release: ![Token distribution across PII classes](pii_class_count_histogram.png) There is 1 class that is still overrepresented in the dataset: firstname. We will further improve the balance with future dataset releases. This is the token distribution excluding the FIRSTNAME class: ![Token distribution across PII classes excluding `FIRSTNAME`](pii_class_count_histogram_without_FIRSTNAME.png) # Compatible Machine Learning Tasks: - Tokenclassification. Check out a HuggingFace's [guide on token classification](https://huggingface.co/docs/transformers/tasks/token_classification). - [ALBERT](https://huggingface.co/docs/transformers/model_doc/albert), [BERT](https://huggingface.co/docs/transformers/model_doc/bert), [BigBird](https://huggingface.co/docs/transformers/model_doc/big_bird), [BioGpt](https://huggingface.co/docs/transformers/model_doc/biogpt), [BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom), [BROS](https://huggingface.co/docs/transformers/model_doc/bros), [CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert), [CANINE](https://huggingface.co/docs/transformers/model_doc/canine), [ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert), [Data2VecText](https://huggingface.co/docs/transformers/model_doc/data2vec-text), [DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta), [DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2), [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert), [ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra), [ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie), [ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m), [ESM](https://huggingface.co/docs/transformers/model_doc/esm), [Falcon](https://huggingface.co/docs/transformers/model_doc/falcon), [FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert), [FNet](https://huggingface.co/docs/transformers/model_doc/fnet), [Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel), [GPT-Sw3](https://huggingface.co/docs/transformers/model_doc/gpt-sw3), [OpenAI GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2), [GPTBigCode](https://huggingface.co/docs/transformers/model_doc/gpt_bigcode), [GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo), [GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox), [I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert), [LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm), [LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2), [LayoutLMv3](https://huggingface.co/docs/transformers/model_doc/layoutlmv3), [LiLT](https://huggingface.co/docs/transformers/model_doc/lilt), [Longformer](https://huggingface.co/docs/transformers/model_doc/longformer), [LUKE](https://huggingface.co/docs/transformers/model_doc/luke), [MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm), [MEGA](https://huggingface.co/docs/transformers/model_doc/mega), [Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert), [MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert), [MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet), [MPT](https://huggingface.co/docs/transformers/model_doc/mpt), [MRA](https://huggingface.co/docs/transformers/model_doc/mra), [Nezha](https://huggingface.co/docs/transformers/model_doc/nezha), [Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer), [QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert), [RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert), [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta), [RoBERTa-PreLayerNorm](https://huggingface.co/docs/transformers/model_doc/roberta-prelayernorm), [RoCBert](https://huggingface.co/docs/transformers/model_doc/roc_bert), [RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer), [SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm), [XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta), [XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl), [XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet), [X-MOD](https://huggingface.co/docs/transformers/model_doc/xmod), [YOSO](https://huggingface.co/docs/transformers/model_doc/yoso) - Text Generation: Mapping the unmasked_text to to the masked_text or privacy_mask attributes. Check out HuggingFace's [guide to fine-tunning](https://huggingface.co/docs/transformers/v4.15.0/training) - [T5 Family](https://huggingface.co/docs/transformers/model_doc/t5), [Llama2](https://huggingface.co/docs/transformers/main/model_doc/llama2) # Information regarding the rows: - Each row represents a json object with a natural language text that includes placeholders for PII (and could plausibly be written by a human to an AI assistant). - Sample row: - "masked_text" contains a PII free natural text - "Product officially launching in [COUNTY_1]. Estimate profit of [CURRENCYSYMBOL_1][AMOUNT_1]. Expenses by [ACCOUNTNAME_1].", - "unmasked_text" shows a natural sentence containing PII - "Product officially launching in Washington County. Estimate profit of $488293.16. Expenses by Checking Account." - "privacy_mask" indicates the mapping between the privacy token instances and the string within the natural text.* - "{'[COUNTY_1]': 'Washington County', '[CURRENCYSYMBOL_1]': '$', '[AMOUNT_1]': '488293.16', '[ACCOUNTNAME_1]': 'Checking Account'}" - "span_labels" is an array of arrays formatted in the following way [start, end, pii token instance].* - "[[0, 32, 'O'], [32, 49, 'COUNTY_1'], [49, 70, 'O'], [70, 71, 'CURRENCYSYMBOL_1'], [71, 80, 'AMOUNT_1'], [80, 94, 'O'], [94, 110, 'ACCOUNTNAME_1'], [110, 111, 'O']]", - "bio_labels" follows the common place notation for "beginning", "inside" and "outside" of where each private tokens starts.[original paper](https://arxiv.org/abs/cmp-lg/9505040) -["O", "O", "O", "O", "B-COUNTY", "I-COUNTY", "O", "O", "O", "O", "B-CURRENCYSYMBOL", "O", "O", "I-AMOUNT", "I-AMOUNT", "I-AMOUNT", "I-AMOUNT", "O", "O", "O", "B-ACCOUNTNAME", "I-ACCOUNTNAME", "O"], - "tokenised_text" breaks down the unmasked sentence into tokens using Bert Family tokeniser to help fine-tune large language models. - ["product", "officially", "launching", "in", "washington", "county", ".", "estimate", "profit", "of", "$", "48", "##8", "##29", "##3", ".", "16", ".", "expenses", "by", "checking", "account", "."] *note for the nested objects, we store them as string to maximise compability between various software. *Note: the bio_labels and tokenised_text have been created using [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) # About Us: At Ai4Privacy, we are commited to building the global seatbelt of the 21st century for Artificial Intelligence to help fight against potential risks of personal information being integrated into data pipelines. Newsletter & updates: [www.Ai4Privacy.com](www.Ai4Privacy.com) - Looking for ML engineers, developers, beta-testers, human in the loop validators (all languages) - Integrations with already existing open source solutions - Ask us a question on discord: [https://discord.gg/kxSbJrUQZF](https://discord.gg/kxSbJrUQZF) # Roadmap and Future Development - Carbon Neutral - Benchmarking - Better multilingual and especially localisation - Extended integrations - Continuously increase the training set - Further optimisation to the model to reduce size and increase generalisability - Next released major update is planned for the 14th of December 2023 (subscribe to newsletter for updates) # Use Cases and Applications **Chatbots**: Incorporating a PII masking model into chatbot systems can ensure the privacy and security of user conversations by automatically redacting sensitive information such as names, addresses, phone numbers, and email addresses. **Customer Support Systems**: When interacting with customers through support tickets or live chats, masking PII can help protect sensitive customer data, enabling support agents to handle inquiries without the risk of exposing personal information. **Email Filtering**: Email providers can utilize a PII masking model to automatically detect and redact PII from incoming and outgoing emails, reducing the chances of accidental disclosure of sensitive information. **Data Anonymization**: Organizations dealing with large datasets containing PII, such as medical or financial records, can leverage a PII masking model to anonymize the data before sharing it for research, analysis, or collaboration purposes. **Social Media Platforms**: Integrating PII masking capabilities into social media platforms can help users protect their personal information from unauthorized access, ensuring a safer online environment. **Content Moderation**: PII masking can assist content moderation systems in automatically detecting and blurring or redacting sensitive information in user-generated content, preventing the accidental sharing of personal details. **Online Forms**: Web applications that collect user data through online forms, such as registration forms or surveys, can employ a PII masking model to anonymize or mask the collected information in real-time, enhancing privacy and data protection. **Collaborative Document Editing**: Collaboration platforms and document editing tools can use a PII masking model to automatically mask or redact sensitive information when multiple users are working on shared documents. **Research and Data Sharing**: Researchers and institutions can leverage a PII masking model to ensure privacy and confidentiality when sharing datasets for collaboration, analysis, or publication purposes, reducing the risk of data breaches or identity theft. **Content Generation**: Content generation systems, such as article generators or language models, can benefit from PII masking to automatically mask or generate fictional PII when creating sample texts or examples, safeguarding the privacy of individuals. (...and whatever else your creative mind can think of) # Support and Maintenance AI4Privacy is a project affiliated with [AISuisse SA](https://www.aisuisse.com/).

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NAYEIRN23/CASOSPJ

NAYEIRN23

2023-12-15T20:01:05Z

9

0

[ "license:llama2", "region:us" ]

null

2023-12-15T19:06:36Z

--- license: llama2 dataset_info: features: - name: EXPEDIENTE dtype: string - name: ' JUZGADO' dtype: string - name: ' F.INICIO' dtype: string - name: ' MOTIVO ING' dtype: string - name: ' JUEZ' dtype: string - name: ' ESP.LEGAL' dtype: string - name: ' ESTADO' dtype: string - name: ' DEMANDADO' dtype: string - name: ' DEMANDANTE' dtype: string - name: 'SUMILLA ' dtype: string - name: TITULO dtype: string splits: - name: train num_bytes: 6816 num_examples: 14 - name: validation num_bytes: 1015 num_examples: 2 - name: test num_bytes: 747 num_examples: 2 download_size: 26817 dataset_size: 8578 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

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deokhk/te_wiki_sentences_1000000

deokhk

2023-12-17T12:06:54Z

9

0

[ "region:us" ]

null

2023-12-17T12:06:46Z

--- dataset_info: features: - name: sentence dtype: string splits: - name: train num_bytes: 189344044 num_examples: 1000000 - name: dev num_bytes: 166164 num_examples: 1000 download_size: 43341997 dataset_size: 189510208 --- # Dataset Card for "te_wiki_sentences_1000000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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zjguoHF/tokenized_TinyShakespear_dataset

zjguoHF

2023-12-17T16:16:25Z

9

0

[ "region:us" ]

null

2023-12-17T16:16:22Z

--- dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: special_tokens_mask sequence: int8 splits: - name: train num_bytes: 2638965 num_examples: 32592 - name: validation num_bytes: 223829 num_examples: 2795 - name: test num_bytes: 358179 num_examples: 4611 download_size: 988180 dataset_size: 3220973 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

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justinian336/salvadoran-news-edh

justinian336

2023-12-18T01:03:22Z

8

0

[ "region:us" ]

null

2023-02-15T14:34:40Z

--- dataset_info: features: - name: image_src dtype: string - name: title dtype: string - name: content dtype: string - name: category dtype: class_label: names: '0': deportes '1': videos '2': opinion '3': deportes/zona-mundialista '4': opinion/caricaturas '5': vida '6': entretenimiento '7': null '8': noticias '9': fotogalerias - name: link dtype: string splits: - name: train num_bytes: 196407515 num_examples: 55345 download_size: 111585539 dataset_size: 196407515 --- # Dataset Card for "salvadoran-news-edh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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TheLastBen/RNPD

TheLastBen

2023-12-14T10:35:07Z

8

5

[ "license:cc-by-nc-4.0", "region:us" ]

null

2023-02-15T23:44:26Z

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

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mikehemberger/plantnet300K

mikehemberger

2023-12-14T15:25:38Z

8

1

[ "region:us" ]

null

2023-08-06T17:04:42Z

--- 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: label dtype: class_label: names: '0': '1355868' '1': '1355920' '2': '1355932' '3': '1355936' '4': '1355937' '5': '1355955' '6': '1355959' '7': '1355961' '8': '1355978' '9': '1355990' '10': '1356003' '11': '1356022' '12': '1356037' '13': '1356055' '14': '1356075' '15': '1356076' '16': '1356111' '17': '1356126' '18': '1356138' '19': '1356257' '20': '1356278' '21': '1356279' '22': '1356309' '23': '1356379' '24': '1356380' '25': '1356382' '26': '1356420' '27': '1356421' '28': '1356428' '29': '1356469' '30': '1356692' '31': '1356781' '32': '1356816' '33': '1356847' '34': '1356901' '35': '1357330' '36': '1357331' '37': '1357367' '38': '1357379' '39': '1357506' '40': '1357635' '41': '1357652' '42': '1357677' '43': '1357681' '44': '1357682' '45': '1357705' '46': '1358094' '47': '1358095' '48': '1358096' '49': '1358097' '50': '1358099' '51': '1358101' '52': '1358102' '53': '1358103' '54': '1358105' '55': '1358108' '56': '1358112' '57': '1358119' '58': '1358127' '59': '1358132' '60': '1358133' '61': '1358150' '62': '1358193' '63': '1358302' '64': '1358365' '65': '1358519' '66': '1358605' '67': '1358608' '68': '1358689' '69': '1358690' '70': '1358691' '71': '1358695' '72': '1358699' '73': '1358700' '74': '1358701' '75': '1358703' '76': '1358704' '77': '1358706' '78': '1358710' '79': '1358711' '80': '1358748' '81': '1358749' '82': '1358750' '83': '1358751' '84': '1358752' '85': '1358755' '86': '1358759' '87': '1358760' '88': '1358766' '89': '1359060' '90': '1359064' '91': '1359065' '92': '1359197' '93': '1359331' '94': '1359332' '95': '1359359' '96': '1359364' '97': '1359483' '98': '1359485' '99': '1359486' '100': '1359488' '101': '1359489' '102': '1359495' '103': '1359497' '104': '1359498' '105': '1359502' '106': '1359505' '107': '1359506' '108': '1359508' '109': '1359510' '110': '1359513' '111': '1359514' '112': '1359517' '113': '1359518' '114': '1359519' '115': '1359521' '116': '1359523' '117': '1359524' '118': '1359525' '119': '1359526' '120': '1359528' '121': '1359530' '122': '1359616' '123': '1359620' '124': '1359622' '125': '1359625' '126': '1359669' '127': '1359806' '128': '1359815' '129': '1359821' '130': '1360004' '131': '1360147' '132': '1360148' '133': '1360150' '134': '1360152' '135': '1360153' '136': '1360154' '137': '1360427' '138': '1360550' '139': '1360555' '140': '1360588' '141': '1360590' '142': '1360591' '143': '1360618' '144': '1360671' '145': '1360687' '146': '1360759' '147': '1360808' '148': '1360811' '149': '1360835' '150': '1360838' '151': '1360976' '152': '1360978' '153': '1360998' '154': '1361024' '155': '1361233' '156': '1361316' '157': '1361357' '158': '1361507' '159': '1361508' '160': '1361524' '161': '1361655' '162': '1361656' '163': '1361658' '164': '1361660' '165': '1361663' '166': '1361666' '167': '1361668' '168': '1361672' '169': '1361704' '170': '1361745' '171': '1361759' '172': '1361823' '173': '1361824' '174': '1361847' '175': '1361850' '176': '1361891' '177': '1362024' '178': '1362064' '179': '1362080' '180': '1362192' '181': '1362294' '182': '1362385' '183': '1362398' '184': '1362434' '185': '1362435' '186': '1362489' '187': '1362490' '188': '1362516' '189': '1362582' '190': '1362834' '191': '1362927' '192': '1362928' '193': '1362954' '194': '1363016' '195': '1363019' '196': '1363021' '197': '1363110' '198': '1363117' '199': '1363126' '200': '1363127' '201': '1363128' '202': '1363129' '203': '1363130' '204': '1363227' '205': '1363336' '206': '1363343' '207': '1363359' '208': '1363451' '209': '1363463' '210': '1363464' '211': '1363489' '212': '1363490' '213': '1363491' '214': '1363492' '215': '1363493' '216': '1363613' '217': '1363688' '218': '1363699' '219': '1363700' '220': '1363701' '221': '1363703' '222': '1363735' '223': '1363737' '224': '1363738' '225': '1363739' '226': '1363740' '227': '1363743' '228': '1363749' '229': '1363750' '230': '1363764' '231': '1363769' '232': '1363770' '233': '1363772' '234': '1363778' '235': '1363871' '236': '1363872' '237': '1363970' '238': '1363991' '239': '1364000' '240': '1364099' '241': '1364112' '242': '1364158' '243': '1364159' '244': '1364161' '245': '1364164' '246': '1364165' '247': '1364172' '248': '1364173' '249': '1364440' '250': '1364512' '251': '1365712' '252': '1365961' '253': '1366875' '254': '1366898' '255': '1366899' '256': '1366997' '257': '1367060' '258': '1367087' '259': '1367267' '260': '1367343' '261': '1367369' '262': '1367431' '263': '1367432' '264': '1367781' '265': '1367784' '266': '1367921' '267': '1367938' '268': '1367949' '269': '1367998' '270': '1369472' '271': '1369887' '272': '1369960' '273': '1370279' '274': '1370991' '275': '1370993' '276': '1371680' '277': '1371691' '278': '1372016' '279': '1372602' '280': '1373151' '281': '1373231' '282': '1373259' '283': '1373530' '284': '1373535' '285': '1374048' '286': '1374448' '287': '1374450' '288': '1374466' '289': '1374933' '290': '1375776' '291': '1375797' '292': '1375895' '293': '1375954' '294': '1375965' '295': '1375966' '296': '1376366' '297': '1376703' '298': '1376726' '299': '1379303' '300': '1379513' '301': '1379556' '302': '1379585' '303': '1380874' '304': '1380904' '305': '1381173' '306': '1381468' '307': '1382099' '308': '1383149' '309': '1383772' '310': '1383774' '311': '1384199' '312': '1384482' '313': '1384485' '314': '1384497' '315': '1384499' '316': '1384500' '317': '1384760' '318': '1385700' '319': '1385701' '320': '1385749' '321': '1385853' '322': '1385937' '323': '1387107' '324': '1387284' '325': '1387687' '326': '1387727' '327': '1388905' '328': '1388911' '329': '1389016' '330': '1389031' '331': '1389044' '332': '1389078' '333': '1389230' '334': '1389231' '335': '1389294' '336': '1389297' '337': '1389307' '338': '1389308' '339': '1389426' '340': '1389442' '341': '1389492' '342': '1389510' '343': '1389553' '344': '1389567' '345': '1389628' '346': '1389630' '347': '1389633' '348': '1389921' '349': '1389966' '350': '1390617' '351': '1390637' '352': '1390956' '353': '1390957' '354': '1391110' '355': '1391112' '356': '1391113' '357': '1391167' '358': '1391184' '359': '1391190' '360': '1391192' '361': '1391193' '362': '1391197' '363': '1391198' '364': '1391206' '365': '1391209' '366': '1391221' '367': '1391226' '368': '1391235' '369': '1391250' '370': '1391326' '371': '1391483' '372': '1391498' '373': '1391499' '374': '1391529' '375': '1391530' '376': '1391648' '377': '1391649' '378': '1391652' '379': '1391654' '380': '1391762' '381': '1391797' '382': '1391805' '383': '1391807' '384': '1391809' '385': '1391810' '386': '1391821' '387': '1391868' '388': '1391953' '389': '1391960' '390': '1391961' '391': '1391962' '392': '1391963' '393': '1391964' '394': '1391967' '395': '1392093' '396': '1392094' '397': '1392096' '398': '1392254' '399': '1392361' '400': '1392365' '401': '1392418' '402': '1392475' '403': '1392601' '404': '1392653' '405': '1392654' '406': '1392658' '407': '1392689' '408': '1392690' '409': '1392691' '410': '1392695' '411': '1392730' '412': '1392777' '413': '1393241' '414': '1393242' '415': '1393245' '416': '1393248' '417': '1393250' '418': '1393253' '419': '1393254' '420': '1393257' '421': '1393294' '422': '1393322' '423': '1393393' '424': '1393414' '425': '1393416' '426': '1393418' '427': '1393423' '428': '1393425' '429': '1393449' '430': '1393450' '431': '1393465' '432': '1393466' '433': '1393537' '434': '1393614' '435': '1393654' '436': '1393656' '437': '1393725' '438': '1393789' '439': '1393792' '440': '1393796' '441': '1393818' '442': '1393823' '443': '1393846' '444': '1393850' '445': '1393851' '446': '1393933' '447': '1393945' '448': '1393946' '449': '1393965' '450': '1393967' '451': '1393968' '452': '1393980' '453': '1393981' '454': '1394072' '455': '1394073' '456': '1394077' '457': '1394078' '458': '1394082' '459': '1394088' '460': '1394114' '461': '1394120' '462': '1394128' '463': '1394143' '464': '1394153' '465': '1394164' '466': '1394169' '467': '1394183' '468': '1394189' '469': '1394313' '470': '1394378' '471': '1394382' '472': '1394383' '473': '1394394' '474': '1394396' '475': '1394399' '476': '1394401' '477': '1394404' '478': '1394405' '479': '1394406' '480': '1394418' '481': '1394420' '482': '1394453' '483': '1394454' '484': '1394455' '485': '1394457' '486': '1394460' '487': '1394489' '488': '1394498' '489': '1394502' '490': '1394504' '491': '1394508' '492': '1394513' '493': '1394549' '494': '1394580' '495': '1394591' '496': '1394648' '497': '1394714' '498': '1394759' '499': '1394994' '500': '1394995' '501': '1395391' '502': '1395720' '503': '1395721' '504': '1395722' '505': '1396133' '506': '1396134' '507': '1396143' '508': '1396144' '509': '1396145' '510': '1396156' '511': '1396159' '512': '1396161' '513': '1396165' '514': '1396708' '515': '1396713' '516': '1396754' '517': '1396758' '518': '1396823' '519': '1396824' '520': '1396842' '521': '1396843' '522': '1396858' '523': '1396867' '524': '1396869' '525': '1397208' '526': '1397209' '527': '1397268' '528': '1397269' '529': '1397303' '530': '1397305' '531': '1397311' '532': '1397312' '533': '1397313' '534': '1397349' '535': '1397351' '536': '1397352' '537': '1397364' '538': '1397379' '539': '1397387' '540': '1397403' '541': '1397407' '542': '1397408' '543': '1397420' '544': '1397491' '545': '1397510' '546': '1397514' '547': '1397515' '548': '1397550' '549': '1397551' '550': '1397556' '551': '1397585' '552': '1397598' '553': '1397613' '554': '1397841' '555': '1397959' '556': '1398041' '557': '1398111' '558': '1398125' '559': '1398128' '560': '1398130' '561': '1398178' '562': '1398196' '563': '1398274' '564': '1398326' '565': '1398333' '566': '1398355' '567': '1398444' '568': '1398447' '569': '1398469' '570': '1398480' '571': '1398494' '572': '1398513' '573': '1398515' '574': '1398526' '575': '1398567' '576': '1398592' '577': '1398608' '578': '1398615' '579': '1398700' '580': '1398805' '581': '1398830' '582': '1399063' '583': '1399141' '584': '1399145' '585': '1399157' '586': '1399231' '587': '1399338' '588': '1399384' '589': '1399667' '590': '1399783' '591': '1399803' '592': '1399804' '593': '1399920' '594': '1400060' '595': '1400080' '596': '1400081' '597': '1400096' '598': '1400100' '599': '1400104' '600': '1400110' '601': '1400471' '602': '1400478' '603': '1400798' '604': '1400844' '605': '1401141' '606': '1401148' '607': '1401211' '608': '1401216' '609': '1401222' '610': '1401242' '611': '1401701' '612': '1401711' '613': '1401718' '614': '1401735' '615': '1401736' '616': '1401737' '617': '1401883' '618': '1402303' '619': '1402356' '620': '1402423' '621': '1402433' '622': '1402464' '623': '1402576' '624': '1402811' '625': '1402920' '626': '1402921' '627': '1402924' '628': '1402925' '629': '1402926' '630': '1403272' '631': '1403479' '632': '1403654' '633': '1403826' '634': '1403967' '635': '1404002' '636': '1404481' '637': '1404745' '638': '1404776' '639': '1404804' '640': '1405061' '641': '1405088' '642': '1405252' '643': '1405641' '644': '1405653' '645': '1405685' '646': '1405686' '647': '1405703' '648': '1406426' '649': '1406486' '650': '1406756' '651': '1406863' '652': '1407077' '653': '1407379' '654': '1407873' '655': '1407874' '656': '1408034' '657': '1408037' '658': '1408041' '659': '1408045' '660': '1408066' '661': '1408071' '662': '1408092' '663': '1408107' '664': '1408160' '665': '1408214' '666': '1408227' '667': '1408284' '668': '1408386' '669': '1408401' '670': '1408421' '671': '1408451' '672': '1408468' '673': '1408490' '674': '1408502' '675': '1408507' '676': '1408508' '677': '1408509' '678': '1408557' '679': '1408594' '680': '1408657' '681': '1408688' '682': '1408718' '683': '1408727' '684': '1408734' '685': '1408738' '686': '1408774' '687': '1408788' '688': '1408810' '689': '1408812' '690': '1408845' '691': '1408869' '692': '1408874' '693': '1408953' '694': '1408961' '695': '1408979' '696': '1409185' '697': '1409191' '698': '1409195' '699': '1409214' '700': '1409215' '701': '1409216' '702': '1409238' '703': '1409239' '704': '1409283' '705': '1409292' '706': '1409295' '707': '1409296' '708': '1409299' '709': '1409328' '710': '1409407' '711': '1409521' '712': '1409525' '713': '1409550' '714': '1409551' '715': '1409552' '716': '1409586' '717': '1409642' '718': '1409705' '719': '1409746' '720': '1409824' '721': '1409834' '722': '1409839' '723': '1409918' '724': '1409919' '725': '1409932' '726': '1410024' '727': '1410025' '728': '1410147' '729': '1410168' '730': '1411423' '731': '1411462' '732': '1411466' '733': '1411469' '734': '1411472' '735': '1411520' '736': '1411524' '737': '1411687' '738': '1411700' '739': '1411760' '740': '1411827' '741': '1411829' '742': '1411832' '743': '1411833' '744': '1411834' '745': '1411925' '746': '1412311' '747': '1412313' '748': '1412314' '749': '1412337' '750': '1412344' '751': '1412368' '752': '1412410' '753': '1412412' '754': '1412413' '755': '1412418' '756': '1412444' '757': '1412445' '758': '1412595' '759': '1412596' '760': '1412659' '761': '1412661' '762': '1412662' '763': '1412663' '764': '1412697' '765': '1412698' '766': '1412699' '767': '1412740' '768': '1412745' '769': '1412831' '770': '1412833' '771': '1412834' '772': '1412888' '773': '1412889' '774': '1412946' '775': '1412992' '776': '1413002' '777': '1413013' '778': '1413391' '779': '1413751' '780': '1413752' '781': '1413753' '782': '1413755' '783': '1413757' '784': '1413832' '785': '1413835' '786': '1414057' '787': '1414058' '788': '1414272' '789': '1414275' '790': '1414276' '791': '1414397' '792': '1414746' '793': '1414808' '794': '1414903' '795': '1414924' '796': '1415584' '797': '1415593' '798': '1415911' '799': '1416420' '800': '1416509' '801': '1417117' '802': '1417506' '803': '1417546' '804': '1417900' '805': '1417916' '806': '1418033' '807': '1418061' '808': '1418084' '809': '1418102' '810': '1418140' '811': '1418146' '812': '1418191' '813': '1418192' '814': '1418295' '815': '1418345' '816': '1418475' '817': '1418545' '818': '1418546' '819': '1418547' '820': '1418563' '821': '1418576' '822': '1418653' '823': '1418655' '824': '1418659' '825': '1418665' '826': '1418732' '827': '1418984' '828': '1419077' '829': '1419086' '830': '1419091' '831': '1419112' '832': '1419115' '833': '1419173' '834': '1419197' '835': '1419334' '836': '1419352' '837': '1419595' '838': '1419598' '839': '1419612' '840': '1419713' '841': '1419720' '842': '1419807' '843': '1419864' '844': '1419874' '845': '1419923' '846': '1419924' '847': '1419942' '848': '1419949' '849': '1420092' '850': '1420288' '851': '1420364' '852': '1420365' '853': '1420496' '854': '1420544' '855': '1420653' '856': '1420700' '857': '1420767' '858': '1420781' '859': '1420787' '860': '1420792' '861': '1420795' '862': '1420796' '863': '1420863' '864': '1420896' '865': '1420929' '866': '1420931' '867': '1421001' '868': '1421011' '869': '1421021' '870': '1421026' '871': '1421107' '872': '1421386' '873': '1421523' '874': '1421628' '875': '1422105' '876': '1422636' '877': '1422712' '878': '1422721' '879': '1423286' '880': '1423469' '881': '1423633' '882': '1423932' '883': '1424003' '884': '1424005' '885': '1424377' '886': '1425433' '887': '1425899' '888': '1427043' '889': '1427659' '890': '1427821' '891': '1428059' '892': '1430280' '893': '1430287' '894': '1430515' '895': '1431222' '896': '1431925' '897': '1432545' '898': '1432783' '899': '1432786' '900': '1432824' '901': '1433158' '902': '1433176' '903': '1433496' '904': '1434011' '905': '1434232' '906': '1434249' '907': '1434258' '908': '1434267' '909': '1434320' '910': '1434326' '911': '1434383' '912': '1434584' '913': '1434594' '914': '1434712' '915': '1435225' '916': '1435228' '917': '1435260' '918': '1435709' '919': '1435714' '920': '1436922' '921': '1436923' '922': '1437724' '923': '1438033' '924': '1438041' '925': '1438043' '926': '1438045' '927': '1438049' '928': '1438052' '929': '1438056' '930': '1438352' '931': '1438354' '932': '1438358' '933': '1438616' '934': '1438868' '935': '1439145' '936': '1439703' '937': '1440273' '938': '1440412' '939': '1440416' '940': '1440423' '941': '1440425' '942': '1440440' '943': '1440446' '944': '1440476' '945': '1441099' '946': '1441349' '947': '1441375' '948': '1441410' '949': '1442541' '950': '1442833' '951': '1442865' '952': '1442930' '953': '1444049' '954': '1444156' '955': '1444195' '956': '1444216' '957': '1447962' '958': '1449139' '959': '1449149' '960': '1453004' '961': '1455408' '962': '1460343' '963': '1463177' '964': '1464023' '965': '1464054' '966': '1464075' '967': '1464086' '968': '1464088' '969': '1464459' '970': '1464567' '971': '1465016' '972': '1465026' '973': '1465149' '974': '1465170' '975': '1470435' '976': '1486475' '977': '1486501' '978': '1486655' '979': '1488001' '980': '1488026' '981': '1488171' '982': '1488206' '983': '1488285' '984': '1491513' '985': '1494482' '986': '1497434' '987': '1497630' '988': '1497667' '989': '1503981' '990': '1506976' '991': '1513142' '992': '1513780' '993': '1514035' '994': '1514627' '995': '1516297' '996': '1518683' '997': '1522375' '998': '1522377' '999': '1529081' '1000': '1529084' '1001': '1529107' '1002': '1529124' '1003': '1529128' '1004': '1529144' '1005': '1529148' '1006': '1529179' '1007': '1529205' '1008': '1529212' '1009': '1529242' '1010': '1529265' '1011': '1529305' '1012': '1529328' '1013': '1535203' '1014': '1538569' '1015': '1542499' '1016': '1550658' '1017': '1550692' '1018': '1550785' '1019': '1550799' '1020': '1560405' '1021': '1567995' '1022': '1568056' '1023': '1568102' '1024': '1568134' '1025': '1568144' '1026': '1568799' '1027': '1568914' '1028': '1576296' '1029': '1580985' '1030': '1584568' '1031': '1600531' '1032': '1608001' '1033': '1608003' '1034': '1608023' '1035': '1608027' '1036': '1608034' '1037': '1608440' '1038': '1617292' '1039': '1617904' '1040': '1618100' '1041': '1618570' '1042': '1618661' '1043': '1621823' '1044': '1638998' '1045': '1643184' '1046': '1643341' '1047': '1643349' '1048': '1643491' '1049': '1643553' '1050': '1643690' '1051': '1643819' '1052': '1643866' '1053': '1643887' '1054': '1644145' '1055': '1644223' '1056': '1646149' '1057': '1662426' '1058': '1663341' '1059': '1663381' '1060': '1664348' '1061': '1667445' '1062': '1675513' '1063': '1682159' '1064': '1689963' '1065': '1694631' '1066': '1697960' '1067': '1697991' '1068': '1698065' '1069': '1711751' '1070': '1711753' '1071': '1711775' '1072': '1711787' '1073': '1711873' '1074': '1712003' '1075': '1712019' '1076': '1712365' '1077': '1713198' '1078': '1713199' '1079': '1717570' '1080': '1718287' splits: - name: train num_bytes: 25342011377.02 num_examples: 243916 - name: validation num_bytes: 3577279070.02 num_examples: 31118 - name: test num_bytes: 3537246514.632 num_examples: 31112 download_size: 3223619352 dataset_size: 32456536961.672 --- # Dataset Card for "plantnet300K" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) Original Work is here: https://github.com/plantnet/PlantNet-300K @inproceedings{plantnet-300k, author = {C. Garcin and A. Joly and P. Bonnet and A. Affouard and \JC Lombardo and M. Chouet and M. Servajean and T. Lorieul and J. Salmon}, booktitle = {NeurIPS Datasets and Benchmarks 2021}, title = {{Pl@ntNet-300K}: a plant image dataset with high label ambiguity and a long-tailed distribution}, year = {2021}, }

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macarious/en_corpora_parliament_processed

macarious

2023-12-15T01:13:56Z

8

0

[ "region:us" ]

null

2023-09-16T00:52:40Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 309185247 num_examples: 2051014 download_size: 171553321 dataset_size: 309185247 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "en_corpora_parliament_processed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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rohit901/nlp_proj_llm_hallucination

rohit901

2023-12-16T06:41:42Z

8

0

[ "region:us" ]

null

2023-10-27T10:49:03Z

--- configs: - config_name: default data_files: - split: gpt3_5 path: "gpt3_5_hallucination.json" - split: gpt4 path: "gpt4_hallucination.json" - split: vicuna path: "vicuna_hallucination.json" - split: open_orca_mistral path: "open_orca_mistral_hallucination.json" ---

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deepghs/csip

deepghs

2023-12-11T11:54:47Z

8

1

[ "task_categories:zero-shot-classification", "size_categories:1M<n<10M", "license:openrail", "art", "region:us" ]

[ "zero-shot-classification" ]

2023-11-16T14:17:28Z

--- license: openrail task_categories: - zero-shot-classification tags: - art size_categories: - 1M<n<10M ---

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CarlosBuiol/testimage

CarlosBuiol

2023-12-17T14:17:01Z

8

0

[ "license:mit", "region:us" ]

null

2023-11-25T16:26:54Z

--- license: mit ---

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arm/illumicore-llama2-1k

arm

2023-12-14T03:51:21Z

8

1

[ "task_categories:text-generation", "task_categories:summarization", "size_categories:1K<n<10K", "language:en", "license:llama2", "region:us" ]

[ "text-generation", "summarization" ]

2023-12-07T04:38:29Z

--- language: - en license: llama2 size_categories: - 1K<n<10K task_categories: - text-generation - summarization pretty_name: IllumiCore-Llama2-1k dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 12515626 num_examples: 1000 download_size: 2510028 dataset_size: 12515626 configs: - config_name: default data_files: - split: train path: data/train-* --- # IllumiCore-1k: Llama2 Formatting This is a VNF resource allocation dataset (1000 samples) generated by IllumiCore [1], processed to match Llama 2's prompt format [2]: ``` <s>[INST] <<SYS>> {{ system_prompt }} <</SYS>> {{ user_msg_1 }} [/INST] {{ model_answer_1 }} </s><s>[INST] {{ user_msg_2 }} [/INST] {{ model_answer_1 }} </s> ``` Here is an example of a dataset record: ``` <s>[INST] <<SYS>> As a telecommunication realm expert with professional knowledge of network function virtualization and software-defined networks, your task is to assist the user in answering questions related to network functions virtualization infrastructure. Please provide clear and detailed explanations, drawing upon your expertise in the field to address the user's inquiries effectively. Your responses should be informative and accurate, catering to the user's specific questions and providing valuable insights into network function virtualization infrastructure. Ensure that your explanations are accessible to users with varying levels of familiarity with the subject matter, and feel free to incorporate relevant examples or analogies to enhance understanding. Here is the current NFVI topology tree: NFVI └── Core Switch ├── Data Center 1 │ ├── Aggregation Switch 1 │ │ ├── +---------------+ │ │ │ | Top of Rack 1 | │ │ │ +---------------+ │ │ ├── +---------------+ │ │ │ | Top of Rack 2 | │ │ │ +---------------+ │ │ └── +---------------+ │ │ | Top of Rack 3 | │ │ +---------------+ │ ├── Aggregation Switch 2 │ │ ├── +---------------+ │ │ │ | Top of Rack 1 | │ │ │ +---------------+ │ │ ├── +---------------+ │ │ │ | Top of Rack 2 | │ │ │ +---------------+ │ │ └── +---------------+ │ │ | Top of Rack 3 | │ │ +---------------+ │ ├── Rack 1 │ │ ├── Top of Rack 1 │ │ ├── ╔═══════════════╗ │ │ │ ║ Server 1 ║ │ │ │ ║ | ║ │ │ │ ║ CH1 ║ │ │ │ ║ CPU: 48 Cores ║ │ │ │ ║ Mem: 408 GB ║ │ │ │ ║ Net: 1 GB ║ │ │ │ ╚═══════════════╝ │ │ ├── ╔═══════════════╗ │ │ │ ║ Server 2 ║ │ │ │ ║ | ║ │ │ │ ║ CH2 ║ │ │ │ ║ CPU: 40 Cores ║ │ │ │ ║ Mem: 348 GB ║ │ │ │ ║ Net: 9 GB ║ │ │ │ ╚═══════════════╝ │ │ └── ╔═══════════════╗ │ │ ║ Server 3 ║ │ │ ║ | ║ │ │ ║ CH3 ║ │ │ ║ CPU: 50 Cores ║ │ │ ║ Mem: 392 GB ║ │ │ ║ Net: 3 GB ║ │ │ ╚═══════════════╝ │ ├── Rack 2 │ │ ├── Top of Rack 2 │ │ ├── ╔═══════════════╗ │ │ │ ║ Server 4 ║ │ │ │ ║ | ║ │ │ │ ║ CH4 ║ │ │ │ ║ CPU: 64 Cores ║ │ │ │ ║ Mem: 354 GB ║ │ │ │ ║ Net: 3 GB ║ │ │ │ ╚═══════════════╝ │ │ ├── ╔═══════════════╗ │ │ │ ║ Server 5 ║ │ │ │ ║ | ║ │ │ │ ║ CH5 ║ │ │ │ ║ CPU: 48 Cores ║ │ │ │ ║ Mem: 408 GB ║ │ │ │ ║ Net: 5 GB ║ │ │ │ ╚═══════════════╝ │ │ └── ╔═══════════════╗ │ │ ║ Server 6 ║ │ │ ║ | ║ │ │ ║ CH6 ║ │ │ ║ CPU: 54 Cores ║ │ │ ║ Mem: 206 GB ║ │ │ ║ Net: 1 GB ║ │ │ ╚═══════════════╝ │ └── Rack 3 │ ├── Top of Rack 3 │ ├── ╔═══════════════╗ │ │ ║ Server 7 ║ │ │ ║ | ║ │ │ ║ CH7 ║ │ │ ║ CPU: 64 Cores ║ │ │ ║ Mem: 412 GB ║ │ │ ║ Net: 9 GB ║ │ │ ╚═══════════════╝ │ ├── ╔═══════════════╗ │ │ ║ Server 8 ║ │ │ ║ | ║ │ │ ║ CH8 ║ │ │ ║ CPU: 40 Cores ║ │ │ ║ Mem: 314 GB ║ │ │ ║ Net: 5 GB ║ │ │ ╚═══════════════╝ │ └── ╔═══════════════╗ │ ║ Server 9 ║ │ ║ | ║ │ ║ CH9 ║ │ ║ CPU: 60 Cores ║ │ ║ Mem: 402 GB ║ │ ║ Net: 3 GB ║ │ ╚═══════════════╝ └── Data Center 2 ├── Aggregation Switch 3 │ ├── +---------------+ │ │ | Top of Rack 4 | │ │ +---------------+ │ ├── +---------------+ │ │ | Top of Rack 5 | │ │ +---------------+ │ └── +---------------+ │ | Top of Rack 6 | │ +---------------+ ├── Aggregation Switch 4 │ ├── +---------------+ │ │ | Top of Rack 4 | │ │ +---------------+ │ ├── +---------------+ │ │ | Top of Rack 5 | │ │ +---------------+ │ └── +---------------+ │ | Top of Rack 6 | │ +---------------+ ├── Rack 4 │ ├── Top of Rack 4 │ ├── ╔═══════════════╗ │ │ ║ Server 10 ║ │ │ ║ | ║ │ │ ║ CH10 ║ │ │ ║ CPU: 58 Cores ║ │ │ ║ Mem: 350 GB ║ │ │ ║ Net: 7 GB ║ │ │ ╚═══════════════╝ │ ├── ╔═══════════════╗ │ │ ║ Server 11 ║ │ │ ║ | ║ │ │ ║ CH11 ║ │ │ ║ CPU: 46 Cores ║ │ │ ║ Mem: 198 GB ║ │ │ ║ Net: 5 GB ║ │ │ ╚═══════════════╝ │ └── ╔═══════════════╗ │ ║ Server 12 ║ │ ║ | ║ │ ║ CH12 ║ │ ║ CPU: 58 Cores ║ │ ║ Mem: 234 GB ║ │ ║ Net: 1 GB ║ │ ╚═══════════════╝ ├── Rack 5 │ ├── Top of Rack 5 │ ├── ╔═══════════════╗ │ │ ║ Server 13 ║ │ │ ║ | ║ │ │ ║ CH13 ║ │ │ ║ CPU: 42 Cores ║ │ │ ║ Mem: 348 GB ║ │ │ ║ Net: 7 GB ║ │ │ ╚═══════════════╝ │ ├── ╔═══════════════╗ │ │ ║ Server 14 ║ │ │ ║ | ║ │ │ ║ CH14 ║ │ │ ║ CPU: 56 Cores ║ │ │ ║ Mem: 180 GB ║ │ │ ║ Net: 3 GB ║ │ │ ╚═══════════════╝ │ └── ╔═══════════════╗ │ ║ Server 15 ║ │ ║ | ║ │ ║ CH15 ║ │ ║ CPU: 44 Cores ║ │ ║ Mem: 310 GB ║ │ ║ Net: 1 GB ║ │ ╚═══════════════╝ └── Rack 6 ├── Top of Rack 6 ├── ╔═══════════════╗ │ ║ Server 16 ║ │ ║ | ║ │ ║ CH16 ║ │ ║ CPU: 50 Cores ║ │ ║ Mem: 254 GB ║ │ ║ Net: 1 GB ║ │ ╚═══════════════╝ ├── ╔═══════════════╗ │ ║ Server 17 ║ │ ║ | ║ │ ║ CH17 ║ │ ║ CPU: 34 Cores ║ │ ║ Mem: 478 GB ║ │ ║ Net: 7 GB ║ │ ╚═══════════════╝ └── ╔═══════════════╗ ║ Server 18 ║ ║ | ║ ║ CH18 ║ ║ CPU: 46 Cores ║ ║ Mem: 454 GB ║ ║ Net: 3 GB ║ ╚═══════════════╝ <</SYS>> Can you show me the current NFVI network topology diagram? [/INST] The Network Functions Virtualization Infrastructure (NFVI) consists of a core switch that connects two data centers. Data Center 1 comprises three racks, each containing servers with varying configurations. Each rack is connected to the network via two aggregation switches. The first rack houses three servers with different core counts, memory capacities, and network speeds. The same configuration exists for the second and third racks. Similarly, Data Center 2 is designed with the same architecture, hosting three more racks, each connected via two additional aggregation switches and containing three servers each. The servers across all racks display a diverse range of core counts, memory capacities, and network speeds, providing a robust and versatile NFVI. </s><s>[INST] Can I see a sample of the current VNF resource allocation? [/INST] The resource allocation results indicate that the Network Functions Virtualization Infrastructure Management (VIM) has allocated servers to top of rack (TOR) switches optimally. The servers 1 to 6, 7 to 12, and 13 to 18 are allocated to VIM 1, 2, and 3 respectively. Most servers remain unoccupied, except for servers 13 and 14. Server 13, with a total capacity of 42 CPU cores, 348 GB memory, and 7 GB network, has four virtual machines (VMs) assigned to it, consuming 36 cores, 99 GB memory, and 4 GB network. The remaining capacity is 6 cores, 249 GB memory, and 3 GB network. Server 14, with a total capacity of 56 cores, 180 GB memory, and 3 GB network, has two VMs assigned, consuming 25 cores, 71 GB memory, and 2 GB network. The remaining capacity is 31 cores, 109 GB memory, and 1 GB network. The allocation has achieved an optimal status, demonstrating efficient resource distribution. </s> ``` --- [1] L. Popokh, J. Su, S. Nair and E. Olinick, "IllumiCore: Optimization Modeling and Implementation for Efficient VNF Placement," 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Hvar, Croatia, 2021, pp. 1-7, doi: 10.23919/SoftCOM52868.2021.9559076. [2] [How to Prompt Llama 2](https://huggingface.co/blog/llama2#how-to-prompt-llama-2).

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ZeeshanChaudharee/example

ZeeshanChaudharee

2023-12-12T11:55:27Z

8

0

[ "license:apache-2.0", "region:us" ]

null

2023-12-07T13:01:26Z

--- license: apache-2.0 ---

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kpriyanshu256/semeval-task-8-b-v2-test-paraphrase-2-mistral-7b

kpriyanshu256

2023-12-11T15:29:04Z

8

0

[ "region:us" ]

null

2023-12-11T03:56:09Z

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: model dtype: string - name: source dtype: string - name: label dtype: int64 - name: id dtype: int64 - name: paraphrase dtype: string - name: paraphrase2 dtype: string - name: mistral-7b_estimated_loss dtype: float64 - name: mistral-7b_mean_lowest25 dtype: float64 - name: mistral-7b_mean_highest25 dtype: float64 - name: mistral-7b_max dtype: float64 - name: mistral-7b_min dtype: float64 - name: mistral-7b_range dtype: float64 - name: mistral-7b_mean dtype: float64 - name: mistral-7b_std dtype: float64 - name: mistral-7b_entropy dtype: float64 - name: mistral-7b_kurtosis dtype: float64 - name: mistral-7b_skewness dtype: float64 - name: mistral-7b_perplexity dtype: float64 splits: - name: test num_bytes: 11397023 num_examples: 3000 download_size: 5515131 dataset_size: 11397023 --- # Dataset Card for "semeval-task-8-b-v2-test-paraphrase-2-mistral-7b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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DanKamNdi/law-test

DanKamNdi

2023-12-11T09:02:42Z

8

0

[ "task_categories:question-answering", "size_categories:n<1K", "language:en", "legal", "doi:10.57967/hf/1449", "region:us" ]

[ "question-answering" ]

2023-12-11T08:36:39Z

--- task_categories: - question-answering language: - en tags: - legal size_categories: - n<1K ---

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kardosdrur/dawiki_title_content

kardosdrur

2023-12-11T10:00:44Z

8

0

[ "region:us" ]

null

2023-12-11T09:58:04Z

--- dataset_info: features: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 375216220.38822436 num_examples: 559975 - name: test num_bytes: 93804222.61177567 num_examples: 139994 download_size: 316070037 dataset_size: 469020443.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

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yuasosnin/imdb-dpo

yuasosnin

2023-12-11T11:32:25Z

8

0

[ "region:us" ]

null

2023-12-11T11:32:19Z

--- dataset_info: features: - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: chosen_reward dtype: float32 - name: rejected_reward dtype: float32 splits: - name: train num_bytes: 5978519.1 num_examples: 9000 - name: test num_bytes: 664279.9 num_examples: 1000 download_size: 3876556 dataset_size: 6642799.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

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dputilov/generatemidas24ai

dputilov

2023-12-11T21:12:15Z

8

0

[ "region:us" ]

null

2023-12-11T20:39:20Z

--- dataset_info: features: - name: image dtype: image - name: conditioning_image dtype: image - name: mask dtype: image - name: text dtype: string splits: - name: train num_bytes: 2390531232.212 num_examples: 8682 download_size: 3277241869 dataset_size: 2390531232.212 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "generatemidas24ai" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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faizalnf1800/SciFi_Stories_Text_Corpus

faizalnf1800

2023-12-12T13:37:31Z

8

0

[ "region:us" ]

null

2023-12-12T12:38:09Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 149918239 num_examples: 207993 download_size: 95836490 dataset_size: 149918239 configs: - config_name: default data_files: - split: train path: data/train-* ---

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zwr-shu/docreICL

zwr-shu

2023-12-12T13:51:50Z

8

0

[ "license:apache-2.0", "region:us" ]

null

2023-12-12T13:45:09Z

--- license: apache-2.0 ---

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TeamSODA/ae-awgn_commonvoice

TeamSODA

2023-12-13T05:33:44Z

8

0

[ "region:us" ]

null

2023-12-12T14:11:31Z

--- dataset_info: features: - name: audio_1 dtype: audio - name: audio_0 dtype: audio - name: snr dtype: int64 splits: - name: SNR_0 num_bytes: 41266161.0 num_examples: 50 - name: SNR_5 num_bytes: 41266161.0 num_examples: 50 - name: SNR_10 num_bytes: 41266111.0 num_examples: 50 - name: SNR_20 num_bytes: 41266111.0 num_examples: 50 - name: SNR_30 num_bytes: 41266111.0 num_examples: 50 download_size: 206321474 dataset_size: 206330655.0 configs: - config_name: default data_files: - split: SNR_0 path: data/SNR_0-* - split: SNR_5 path: data/SNR_5-* - split: SNR_10 path: data/SNR_10-* - split: SNR_20 path: data/SNR_20-* - split: SNR_30 path: data/SNR_30-* ---

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Hunzla/xls-r-crema-d-300-each-train

Hunzla

2023-12-13T09:48:12Z

8

0

[ "region:us" ]

null

2023-12-13T09:47:53Z

--- dataset_info: features: - name: emotionG_label dtype: string - name: gender dtype: string - name: emotion dtype: string - name: path dtype: string - name: label sequence: int64 - name: input_values sequence: sequence: float32 - name: input_length dtype: int64 splits: - name: train num_bytes: 415072955 num_examples: 1296 download_size: 367342063 dataset_size: 415072955 configs: - config_name: default data_files: - split: train path: data/train-* ---

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prhegde/preference-data-math-stack-exchange

prhegde

2023-12-13T10:16:15Z

8

1

[ "license:apache-2.0", "region:us" ]

null

2023-12-13T10:07:23Z

--- license: apache-2.0 --- The preference dataset is derived from the [stack exchange dataset](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences) which contains questions and answers from the Stack Overflow Data Dump. This contains questions and answers for various topics. For this work, we used only question and answers from [math.stackexchange.com](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences/tree/main/data/math.meta.stackexchange.com) sub-folder. The questions are grouped with answers that are assigned a score corresponding to the Anthropic paper: ``` score = log2 (1 + upvotes) rounded to the nearest integer, plus 1 if the answer was accepted by the questioner (we assign a score of −1 if the number of upvotes is negative). ``` We performed following processing to derive the final dataset. 1) Basic pre-processing ([code](https://github.com/PraveenSH/dpo-arithmo-mistral-7B/blob/main/src/data_processing/stack_exchange_data.py)) to clean the text 2) Filter Mathematical question using regex based detector ([code](https://github.com/PraveenSH/dpo-arithmo-mistral-7B/blob/main/src/data_processing/stack_exchange_data.py)) 3) For each question, extract 2 answers - one with highest score and one with the lowest score. Former is used as Preferred response and latter is used as the rejected response ## References ``` @online{h4stackexchange, author = {Lambert, Nathan and Tunstall, Lewis and Rajani, Nazneen and Thrush, Tristan}, title = {HuggingFace H4 Stack Exchange Preference Dataset}, year = 2023, url = {https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences}, } ```

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Seongill/Trivia_missing_5_full_substitution

Seongill

2023-12-13T10:24:10Z

8

0

[ "region:us" ]

null

2023-12-13T10:24:04Z

--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: hasanswer dtype: bool - name: id dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: has_answer dtype: bool - name: random_sub dtype: string - name: similar_sub dtype: string - name: ent_type dtype: string splits: - name: train num_bytes: 41367743 num_examples: 11313 download_size: 25016105 dataset_size: 41367743 configs: - config_name: default data_files: - split: train path: data/train-* ---

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Seongill/Trivia_5_only_adversary_1159

Seongill

2023-12-13T10:39:48Z

8

0

[ "region:us" ]

null

2023-12-13T10:37:15Z

--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: has_answer dtype: bool - name: similar_sub dtype: string - name: ctxs list: - name: answer_sent sequence: string - name: hasanswer dtype: bool - name: id dtype: string - name: is_adv dtype: bool - name: new_answer_sent dtype: string - name: original_text dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: num_advs dtype: int64 splits: - name: train num_bytes: 7404322 num_examples: 1159 download_size: 3197268 dataset_size: 7404322 configs: - config_name: default data_files: - split: train path: data/train-* ---

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krishan-CSE/Unified_Dataset_BERT

krishan-CSE

2023-12-13T13:19:45Z

8

0

[ "license:apache-2.0", "region:us" ]

null

2023-12-13T12:20:15Z

--- license: apache-2.0 ---

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Erynan/eval_util_100

Erynan

2023-12-13T22:05:10Z

8

0

[ "region:us" ]

null

2023-12-13T22:05:09Z

--- dataset_info: features: - name: prompt dtype: string - name: response_a dtype: string - name: response_b dtype: string - name: more_reasonable dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 23557 num_examples: 100 download_size: 15532 dataset_size: 23557 configs: - config_name: default data_files: - split: train path: data/train-* ---

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Yunij/testing_kaggle_ai_detection_dataset

Yunij

2023-12-14T06:21:30Z

8

0

[ "license:apache-2.0", "region:us" ]

null

2023-12-14T05:43:14Z

--- license: apache-2.0 dataset_info: features: - name: prompt dtype: string - name: text dtype: string - name: source dtype: string - name: label dtype: int64 - name: perplexity dtype: float64 - name: input_ids sequence: int32 - name: attention_mask sequence: int8 splits: - name: train num_bytes: 1244922869 num_examples: 330345 - name: test num_bytes: 65575515 num_examples: 17387 download_size: 435690981 dataset_size: 1310498384 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

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Tippawan/test2-data-semi-trainulb-r3

Tippawan

2023-12-14T09:54:31Z

8

0

[ "region:us" ]

null

2023-12-14T09:54:25Z

--- dataset_info: features: - name: tokens sequence: string - name: ner_tags sequence: int64 - name: prob sequence: float64 - name: ifpass sequence: int64 - name: pred dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 202202230 num_examples: 41738 download_size: 31409433 dataset_size: 202202230 configs: - config_name: default data_files: - split: train path: data/train-* ---

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ScandEval/cnn-dailymail-mini

ScandEval

2023-12-14T10:09:01Z

8

0

[ "region:us" ]

null

2023-12-14T10:08:56Z

--- dataset_info: features: - name: text dtype: string - name: target_text dtype: string - name: id dtype: string splits: - name: train num_bytes: 4443630 num_examples: 1024 - name: val num_bytes: 1104312 num_examples: 256 - name: test num_bytes: 8831952 num_examples: 2048 download_size: 8836565 dataset_size: 14379894 configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* ---

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huggingface/autotrain-data-8xid-g6rv-rkuc

huggingface

2023-12-14T23:04:02Z

8

0

[ "region:us" ]

null

2023-12-14T23:04:00Z

Invalid username or password.

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huggingface/autotrain-data-8xid-g6rv-rkuc2

huggingface

2023-12-14T23:08:28Z

8

0

[ "region:us" ]

null

2023-12-14T23:08:26Z

Invalid username or password.

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huggingface/autotrain-data-8xid-g6rv-rkuc5

huggingface

2023-12-14T23:19:04Z

8

0

[ "region:us" ]

null

2023-12-14T23:19:02Z

Invalid username or password.

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huggingface/autotrain-data-lw1q-vmpq-ylvw2

huggingface

2023-12-14T23:48:40Z

8

0

[ "region:us" ]

null

2023-12-14T23:48:37Z

Invalid username or password.

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huggingface/autotrain-data-4v9x-3cwh-jsa8

huggingface

2023-12-15T01:54:00Z

8

0

[ "region:us" ]

null

2023-12-15T01:53:31Z

Invalid username or password.

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huggingface/autotrain-data-4v9x-3cwh-jsa81

huggingface

2023-12-15T02:04:38Z

8

0

[ "region:us" ]

null

2023-12-15T02:04:09Z

Invalid username or password.

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huggingface/autotrain-data-yrsq-dnj7-jghjk3

huggingface

2023-12-15T02:33:37Z

8

0

[ "region:us" ]

null

2023-12-15T02:33:35Z

Invalid username or password.

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Raziullah/asr_finetune_cv_13_dv

Raziullah

2023-12-15T06:38:03Z

8

0

[ "license:unknown", "region:us" ]

null

2023-12-15T06:31:48Z

--- license: unknown dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 48000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string splits: - name: train num_bytes: 167429327.552 num_examples: 4904 - name: test num_bytes: 88593702.704 num_examples: 2212 download_size: 262021485 dataset_size: 256023030.25599998 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

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Mursel/mlsum-tr-gpt2-tokenized

Mursel

2023-12-15T18:41:15Z

8

0

[ "region:us" ]

null

2023-12-15T18:39:31Z

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 5314170808 num_examples: 398842 - name: test num_bytes: 1328562688 num_examples: 99712 download_size: 456145997 dataset_size: 6642733496 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

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Jiahuan/teach_action

Jiahuan

2023-12-15T21:26:13Z

8

0

[ "region:us" ]

null

2023-12-15T21:26:03Z

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 605833565 num_examples: 231272 - name: validation num_bytes: 76965684 num_examples: 28960 - name: test num_bytes: 208815535 num_examples: 91903 download_size: 65220573 dataset_size: 891614784 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

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adamzinebi/mmm_track_lmd_8bars_nots

adamzinebi

2023-12-16T13:30:35Z

8

0

[ "region:us" ]

null

2023-12-15T21:35:59Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 74262767 num_examples: 3764 download_size: 12045427 dataset_size: 74262767 configs: - config_name: default data_files: - split: train path: data/train-* ---

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MohamedRashad/arabic-billion-words

MohamedRashad

2023-12-16T02:47:39Z

8

0

[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:ar", "region:us" ]

[ "text-generation" ]

2023-12-16T01:01:47Z

--- language: - ar size_categories: - 1M<n<10M task_categories: - text-generation pretty_name: Arabic Billion Words dataset_info: features: - name: url dtype: string - name: head_line dtype: string - name: date dtype: string - name: text dtype: string splits: - name: train num_bytes: 17017097763 num_examples: 5222964 download_size: 8050780387 dataset_size: 17017097763 configs: - config_name: default data_files: - split: train path: data/train-* --- # Arabic Billion Words Dataset 🌕 The Abu El-Khair Arabic News Corpus (arabic-billion-words) is a comprehensive collection of Arabic text, encompassing over five million newspaper articles. The corpus is rich in linguistic diversity, containing more than a billion and a half words, with approximately three million unique words. The text is encoded in two formats: UTF-8 and Windows CP-1256, and marked up using two markup languages: SGML and XML. ## Data Example An example from the "Almasryalyoum" configuration subset: ```python { "url": "http://today.almasryalyoum.com/printerfriendly.aspx?ArticleID=61300", "head_line": "رئيس وزراء المجر: عنصرية جماهير أوجبيست جلبت العار للبلاد", "date": "19/5/2007", "text": """قال متحدث باسم الحكومة المجرية: إن رئيس الوزراء فيرنك جيوركساني رحب بقرار اتحاد كرة القدم المجري بخصم ثلاث نقاط من نادي أوجبيست بسبب السلوك العنصري الذي صدر من جماهيره. ... يذكر أن المجر بها مجموعة من مشجعي كرة القدم المشاغبين «الهوليجانز»، وشارك الكثير منهم في أعمال شغب معادية للحكومة في العام الماضي.""", } ``` ## Data Fields The dataset comprises the following fields: - "url": string, original URL of the article. - "head_line": string, headline of the article. - "date": string, date of the article. - "text": string, textual content of the article. ## Data Splits The dataset is divided into a single "training" split, with the following distribution of examples across different configuration subsets: | Configuration Subset | Number of Examples | |:---------------------|-------------------:| | Alittihad | 349,342 | | Almasryalyoum | 291,723 | | Almustaqbal | 446,873 | | Alqabas | 817,274 | | Echoroukonline | 139,732 | | Ryiadh | 858,188 | | Sabanews | 92,149 | | SaudiYoum | 888,068 | | Techreen | 314,597 | | Youm7 | 1,172,136 | ## Dataset Usage This dataset is valuable for natural language processing (NLP) tasks, including but not limited to sentiment analysis, topic modeling, and language modeling in the context of Arabic news articles. Researchers and practitioners in the field of NLP can benefit from the diverse linguistic content and the substantial size of the corpus.

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silk-road/Embedding-Adapter

silk-road

2023-12-17T07:33:14Z

8

0

[ "license:apache-2.0", "region:us" ]

null

2023-12-17T07:21:10Z

--- license: apache-2.0 ---

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justinian336/salvadoran-news-elmundo

justinian336

2023-12-18T01:04:55Z

7

0

[ "region:us" ]

null

2023-02-15T14:38:15Z

--- dataset_info: features: - name: image_src dtype: string - name: title dtype: string - name: content dtype: string - name: category dtype: class_label: names: '0': El Mundo '1': Tecnomundo '2': Nacionales '3': Guia Mundialista '4': Economia '5': Editorial '6': Confidencial '7': Politica - name: date dtype: string - name: link dtype: string splits: - name: train num_bytes: 89486722 num_examples: 45858 download_size: 49745848 dataset_size: 89486722 --- # Dataset Card for "salvadoran-news-elmundo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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projecte-aina/CoQCat

projecte-aina

2023-12-12T13:27:41Z

7

0

[ "task_categories:conversational", "task_categories:question-answering", "task_ids:dialogue-generation", "task_ids:extractive-qa", "task_ids:closed-domain-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:ca", "license:cc-by-nc-nd-4.0", "region:us" ]

[ "conversational", "question-answering" ]

2023-09-12T15:42:48Z

--- annotations_creators: - expert-generated language: - ca language_creators: - found license: - cc-by-nc-nd-4.0 multilinguality: - monolingual pretty_name: CoQCat - Conversational Question Answering in Catalan size_categories: - 10K<n<100K source_datasets: [] tags: [] task_categories: - conversational - question-answering task_ids: - dialogue-generation - extractive-qa - closed-domain-qa --- # Dataset Card for CoQCat ## 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:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** [email protected] ### Dataset Summary CoQCat is a dataset for Conversational Question Answering in Catalan. It is based on [CoQA dataset](https://stanfordnlp.github.io/coqa/). CoQCat comprises 89,364 question-answer pairs, sourced from conversations related to 6,000 text passages from six different domains. The questions and responses are designed to maintain a conversational tone. The answers are presented in a free-form text format, with evidence highlighted from the passage. For the development and test sets, an additional 2 responses to each question have been collected. This work is licensed under a [CC BY-NC-ND 4.0 International License](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en_GB). ### Supported Tasks and Leaderboards Conversational models, question answering. ### Languages The dataset is in Catalan (ca-ES). ## Dataset Structure ### Data Instances Three JSON files, one for each split. An example of 'test' looks as follows: ``` { "source": "petites_histories", "id": "petites_histories_100", "filename": "coqcat_batch1", "title": "Superant la crisi dels 40", "story": "“Què collons estic fent aquí” es preguntà, “…fotent bots i suant a raig als meus quaranta tacos en una classe de body-step. Si no he estat mai un amant del gimnàs! Tot això és culpa del club de ‘singles', els desaparellats desesperats per trobar algú que ens escolti una mica, mentre calculem les possibilitats de fotre un clau”. Pensant tot això es va descomptar i anava a contratemps, un altre cop.\nHo acaba de decidir: sortint de la classe es donarà de baixa. Sí, serà millor oblidar els ‘singles' i dedicar els caps de setmana que no tingui les nenes a fer sortides amb el seu germà, els nebots són més grans que les seves filles i ja fan bones excursions.\nUn cop dutxat s'apropà al mostrador de l'entrada. El noi el va fer passar a l'oficina per omplir el full de baixa. Dins, una veu el va cridar: “Txume?”. Es girà estranyat ja que li deien així només a l'institut, després va ser el Jaume. Va mirar la dona que li somreia.:\n-“Estàs igual que fa vint-i-cinc anys! Has fet un pacte amb el diable?”\n-“… Alba?? Ets l'Alba?”- la seva cara li recordava a la de la noia grassoneta de la classe, però ara no ho estava pas de grassa! -“Tu sí que hi has pactat! Estàs molt millor… bé, vull dir que…”\n-“Sí, em vaig posar les piles.”\n-“Segueixes escrivint relats? Recordo que a tots ens agradaven les històries de l'Alba.”\n-“Doncs encara ho faig, just ara en plegar vaig a una sessió literària musical. Si vols venir, podem recordar batalletes.”\nS'hi apuntà. Van xerrar, van riure, van escoltar relats i poemes, van reviure les aventures de l'institut amb música de jazz de fons, … Hi estava a gust, i s'adonà que, per primer cop en molt temps, no avaluava les possibilitats d'acabar prenent la darrera copa a casa seva.", "questions": [ {"input_text": "Està satisfet del que està fent?","turn_id": 1}, {"input_text": "Sua?","turn_id": 2}, {"input_text": "Quants anys té?","turn_id": 3}, {"input_text": "Li agrada anar al gimnàs?","turn_id": 4}, {"input_text": "Està solter?","turn_id": 5}, {"input_text": "Segueix bé el ritme de la música?","turn_id": 6}, {"input_text": "Continuarà apuntat al gimnàs?","turn_id": 7}, {"input_text": "Què farà els caps de setmana?","turn_id": 8}, {"input_text": "On li deien Txume?","turn_id": 9}, {"input_text": "Com l'anomenaven després?","turn_id": 10}, {"input_text": "En Jaume ha envellit bé?","turn_id": 11}, {"input_text": "L'Alba s'havia aprimat?","turn_id": 12}, {"input_text": "A què es dedica l'Alba?","turn_id": 13}, {"input_text": "On van anar plegats?","turn_id": 14}, {"input_text": "Volia que acabessin anant a la seva llar?","turn_id": 15} ], "answers": [ {"input_text": "No.", "span_text": "Què collons estic fent aquí", "span_start": 1, "span_end": 28,"turn_id": 1}, {"input_text": "Sí, a raig.", "span_text": "fotent bots i suant a raig", "span_start": 45, "span_end": 71,"turn_id": 2}, {"input_text": "Quaranta.", "span_text": "als meus quaranta tacos", "span_start": 72, "span_end": 95,"turn_id": 3}, {"input_text": "No.", "span_text": "Si no he estat mai un amant del gimnàs", "span_start": 124, "span_end": 162,"turn_id": 4}, {"input_text": "Sí.", "span_text": "Tot això és culpa del club de ‘singles'", "span_start": 164, "span_end": 203,"turn_id": 5}, {"input_text": "No.", "span_text": "es va descomptar i anava a contratemps, un altre cop", "span_start": 347, "span_end": 399,"turn_id": 6}, {"input_text": "No, quan surti de la classe es donarà de baixa.", "span_text": "sortint de la classe es donarà de baixa", "span_start": 422, "span_end": 461,"turn_id": 7}, {"input_text": "Farà sortides amb el seu germà i els seus nebots.", "span_text": "dedicar els caps de setmana que no tingui les nenes a fer sortides amb el seu germà, els nebots són més grans que les seves filles i ja fan bones excursions", "span_start": 503, "span_end": 659,"turn_id": 8}, {"input_text": "A l'institut.", "span_text": "Dins, una veu el va cridar: “Txume?”. Es girà estranyat ja que li deien així només a l'institut", "span_start": 776, "span_end": 871,"turn_id": 9}, {"input_text": "Jaume.", "span_text": "que li deien així només a l'institut, després va ser el Jaume", "span_start": 835, "span_end": 896,"turn_id": 10}, {"input_text": "Sí.", "span_text": "Estàs igual que fa vint-i-cinc anys", "span_start": 934, "span_end": 969,"turn_id": 11}, {"input_text": "Sí.", "span_text": "però ara no ho estava pas de grassa", "span_start": 1096, "span_end": 1131,"turn_id": 12}, {"input_text": "Escriu relats.", "span_text": "Segueixes escrivint relats? Recordo que a tots ens agradaven les històries de l'Alba.”\n-“Doncs encara ho faig", "span_start": 1232, "span_end": 1341,"turn_id": 13}, {"input_text": "A una sessió literària musical.", "span_text": "just ara en plegar vaig a una sessió literària musical. Si vols venir, podem recordar batalletes.”\nS'hi apuntà", "span_start": 1343, "span_end": 1453,"turn_id": 14}, {"input_text": "No.", "span_text": "no avaluava les possibilitats d'acabar prenent la darrera copa a casa seva", "span_start": 1641, "span_end": 1715,"turn_id": 15} ], "additional_answers": { "0": [ {"input_text": "No.", "span_text": "Què collons estic fent aquí", "span_start": 1, "span_end": 28, "turn_id": 1}, {"input_text": "Sí, molt.", "span_text": "fotent bots i suant a raig", "span_start": 45, "span_end": 71, "turn_id": 2}, {"input_text": "Quaranta.", "span_text": "als meus quaranta tacos", "span_start": 72, "span_end": 95, "turn_id": 3}, {"input_text": "No.", "span_text": "Si no he estat mai un amant del gimnàs", "span_start": 124, "span_end": 162, "turn_id": 4}, {"input_text": "Sí.", "span_text": "Tot això és culpa del club de ‘singles'", "span_start": 164, "span_end": 203, "turn_id": 5}, {"input_text": "No.", "span_text": "es va descomptar i anava a contratemps, un altre cop", "span_start": 347, "span_end": 399, "turn_id": 6}, {"input_text": "No.", "span_text": "sortint de la classe es donarà de baixa", "span_start": 422, "span_end": 461, "turn_id": 7}, {"input_text": "Farà sortides amb el seu germà i els seus nebots.", "span_text": "serà millor oblidar els ‘singles' i dedicar els caps de setmana que no tingui les nenes a fer sortides amb el seu germà, els nebots són més grans que les seves filles i ja fan bones excursions", "span_start": 467, "span_end": 659, "turn_id": 8}, {"input_text": "A l'institut.", "span_text": "“Txume?”. Es girà estranyat ja que li deien així només a l'institut", "span_start": 804, "span_end": 871, "turn_id": 9}, {"input_text": "Jaume.", "span_text": "després va ser el Jaume", "span_start": 873, "span_end": 896, "turn_id": 10}, {"input_text": "Sí.", "span_text": "Estàs igual que fa vint-i-cinc anys", "span_start": 934, "span_end": 969, "turn_id": 11}, {"input_text": "Sí.", "span_text": "però ara no ho estava pas de grassa", "span_start": 1096, "span_end": 1131, "turn_id": 12}, {"input_text": "Escriu relats.", "span_text": "Segueixes escrivint relats? Recordo que a tots ens agradaven les històries de l'Alba.”\n-“Doncs encara ho faig", "span_start": 1232, "span_end": 1341, "turn_id": 13}, {"input_text": "A una sessió literària musical.", "span_text": "just ara en plegar vaig a una sessió literària musical. Si vols venir, podem recordar batalletes.”\nS'hi apuntà", "span_start": 1343, "span_end": 1453, "turn_id": 14}, {"input_text": "No.", "span_text": "no avaluava les possibilitats d'acabar prenent la darrera copa a casa seva", "span_start": 1641, "span_end": 1715, "turn_id": 15} ], "1": [ {"input_text": "No.", "span_text": "Què collons estic fent aquí", "span_start": 1, "span_end": 28, "turn_id": 1}, {"input_text": "Sí.", "span_text": "fotent bots i suant", "span_start": 45, "span_end": 64, "turn_id": 2}, {"input_text": "Quaranta.", "span_text": "als meus quaranta tacos", "span_start": 72, "span_end": 95, "turn_id": 3}, {"input_text": "No.", "span_text": "Si no he estat mai un amant del gimnàs", "span_start": 124, "span_end": 162, "turn_id": 4}, {"input_text": "Sí.", "span_text": "Tot això és culpa del club de ‘singles', els desaparellats desesperats per trobar algú que ens escolti una mica", "span_start": 164, "span_end": 275, "turn_id": 5}, {"input_text": "No.", "span_text": "es va descomptar i anava a contratemps, un altre cop", "span_start": 347, "span_end": 399, "turn_id": 6}, {"input_text": "No.", "span_text": "sortint de la classe es donarà de baixa", "span_start": 422, "span_end": 461, "turn_id": 7}, {"input_text": "A fer sortides amb el seu germà.", "span_text": "serà millor oblidar els ‘singles' i dedicar els caps de setmana que no tingui les nenes a fer sortides amb el seu germà", "span_start": 467, "span_end": 586, "turn_id": 8}, {"input_text": "A l'institut.", "span_text": "una veu el va cridar: “Txume?”. Es girà estranyat ja que li deien així només a l'institut", "span_start": 782, "span_end": 871, "turn_id": 9}, {"input_text": "Jaume.", "span_text": "després va ser el Jaume", "span_start": 873, "span_end": 896, "turn_id": 10}, {"input_text": "Sí.", "span_text": "Estàs igual que fa vint-i-cinc anys", "span_start": 934, "span_end": 969, "turn_id": 11}, {"input_text": "Sí.", "span_text": "però ara no ho estava pas de grassa", "span_start": 1096, "span_end": 1131, "turn_id": 12}, {"input_text": "Escriu relats.", "span_text": "Segueixes escrivint relats? Recordo que a tots ens agradaven les històries de l'Alba.”\n-“Doncs encara ho faig", "span_start": 1232, "span_end": 1341, "turn_id": 13}, {"input_text": "A una sessió literària musical.", "span_text": "una sessió literària musical. Si vols venir, podem recordar batalletes.”\nS'hi apuntà", "span_start": 1369, "span_end": 1453, "turn_id": 14}, {"input_text": "No.", "span_text": "no avaluava les possibilitats d'acabar prenent la darrera copa a casa seva", "span_start": 1641, "span_end": 1715, "turn_id": 15} ] } } ``` ### Data Fields The data fields are the same among all splits. - `source`: a `string` feature. - `id`: a `string` feature. - `filename`: a `string` feature. - `story`: a `string` feature. - `questions`: a `list` of dictionaries containing: - `input_text`: a `string` feature. - `turn_id`: a `int32` feature. - `answers`: a `list` of dictionaries containing: - `input_text`: a `string` feature. - `span_text`: a `string` feature. - `answer_start`: a `int32` feature. - `answer_end`: a `int32` feature. - `turn_id`: a `int32` feature. - `additional_answers` (only in `dev`and `test`): a dictionary feature containing: - `0`: a `list` of dictionaries equal to `answers`. - `1`: a `list` of dictionaries equal to `answers`. ### Data Splits * dev.json: 8,909 question-answering examples. 600 text passages from 4 domains * test.json: 8,986 question-answering examples. 600 text passages from 6 domains * train.json: 71,489 question-answering examples. 4800 text passages from 4 domains ## Dataset Creation ### Curation Rationale We created this dataset to contribute to the development of language models in Catalan, a low-resource language. ### Source Data #### Initial Data Collection and Normalization We obtained the initial text passages from different sources, depending on the domain: | domain | source | train | dev | test | total | |:-|:-|-:|-:|-:|-:| | biographies | [Catalan wikipedia](ca.wikipedia.org) | 1200 | 150 | 100 | 1450| | literature | [Gutenberg Project](gutenberg.org/) | 1200 | 150 | 100 | 1450| | news | [VilaWeb](vilaweb.cat) | 1200 | 150 | 100 | 1450| | mitology | Catalan wikipedia | 1200 | 150 | 100 | 1450| | short histories | [Petites històries](https://petiteshistories.wordpress.com/) | 0 | 0 | 100 | 100| | movie plots | Catalan wikipedia | 0 | 0 | 100 | 100 | | TOTAL | | 4800 | 600 | 600 | 6000 | #### Who are the source language producers? The contents of [Catalan Wikipedia](ca.wikipedia.org) are developed by a team of volunteers, and are subject to review processes also carried out by volunteers. The texts from [VilaWeb](vilaweb.cat) are prepared by journalists and communication professionals, and edited by expert proofreaders. The paragraphes extracted from the [Gutenberg Project](gutenberg.org/) are written by diferent Catalan authors. The texts of the [Petites històries website](https://petiteshistories.wordpress.com/) are written by the writer M. Carme Marí and published on her website. The author has provided them for the preparation of this dataset. ### Annotations #### Annotation process The annotation process was entrusted to the company [M47 labs](https://www.m47labs.com/) through a public tender process. We asked to organize the annotators team in pairs: one of the members asking questions from the text passage and the other one answering them. For the elaboration of addicional answers for the dev and test splits, we asked the participation of a third person, not involved in the original conversation. Annotation guidelines can be found in [Zenodo](https://zenodo.org/records/10362295). #### Who are the annotators? Annotation was entrusted to the company [M47 labs](https://www.m47labs.com/) through a public tender process. We asked for a team of at least 4 annotators, students or graduates of universities, ideally in the field of Humanities or Social Sciences, with optimal demonstrable knowledge of the Catalan language (minimum level C1, or equivalent), and a senior coordinator with proven experience in management and coordination of text annotation. ### Personal and Sensitive Information No personal or sensitive information included. ## Considerations for Using the Data ### Social Impact of Dataset We hope that this dataset will help the development of virtual assistants in Catalan, a language that is often not taken into account. ### Discussion of Biases [N/A] ### Other Known Limitations [N/A] ## Additional Information ### Dataset Curators Language Technologies Unit at the Barcelona Supercomputing Center ([email protected]) 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/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina). ### Licensing Information This work is licensed under a [CC BY-NC-ND 4.0 International License](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en_GB). ### Citation Information [DOI](https://zenodo.org/records/10362295) ### Contributions The annotation was entrusted to the company [M47 labs](https://www.m47labs.com/) through a public tender process. Thanks to [M. Carme Marí](https://petiteshistories.wordpress.com/quant-a/) and the [VilaWeb](vilaweb.cat) team for allowing us to use their texts. And also to all the [Catalan Wikipedia](ca.wikipedia.org) and [Gutenberg Project](gutenberg.org/) volunteers all their work.

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optimum/llm-perf-dataset

optimum

2023-12-18T01:13:55Z

7

0

[ "license:apache-2.0", "region:us" ]

null

2023-11-14T12:16:55Z

--- license: apache-2.0 ---

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LLM360/CrystalCoderDatasets

LLM360

2023-12-12T16:29:38Z

7

12

[ "language:en", "pretrained", "arxiv:2207.14255", "region:us" ]

null

2023-11-30T23:57:32Z

--- language: - en tags: - pretrained --- # Description of the Dataset This release integrates the entire data sequence utilized in the CrystalCoder training. It encompasses data sequences from the three pre-training stages, combining information from two prior works: the [SlimPajama dataset](https://huggingface.co/datasets/cerebras/SlimPajama-627B) and [StarCoder](https://huggingface.co/datasets/bigcode/starcoderdata), totaling approximately 1300 billion tokens. These tokens are distributed across three stages, each with distinct weights. ## Stage 1 During this initial stage, half of the [SlimPajama data](https://huggingface.co/datasets/cerebras/SlimPajama-627B) is utilized, equivalent to approximately 345 billion tokens. ## Stage 2 In the second stage, the remaining half of the [SlimPajama data](https://huggingface.co/datasets/cerebras/SlimPajama-627B) is employed, along with two epochs of [StarCoder data](https://huggingface.co/datasets/bigcode/starcoderdata). For the StarCoder data, we apply [FIM augmentation](https://arxiv.org/abs/2207.14255) with an FIM rate of 0.9 and an SPM rate of 0.5. The total token count for this stage is calculated as 0.5 * 690 + 2 * 291, resulting in 927 billion tokens. ## Stage 3 The third stage involves reusing Python and web-related data from the [StarCoder data](https://huggingface.co/datasets/bigcode/starcoderdata), including HTML, CSS, and JavaScript. This data is utilized for training over three epochs, with the application of FIM at a rate of 0.3 alongside an SPM rate of 0.5. The total token count for this stage is 100 billion. Additionally, a small portion of the SlimPajama dataset, excluding the Github part, is also reused, contributing around 10 billion tokens. ### Instruction tuning (Stage 3a) To enhance the model's proficiency in real chat scenarios, we utilize a diverse set of instruction tuning datasets, totaling approximately 1 billion tokens. Specifically, our data include [OASST1-guanaco](https://huggingface.co/datasets/openaccess-ai-collective/oasst1-guanaco-extended-sharegpt), [SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca), [ShareGPT_V4.3](https://huggingface.co/datasets/Aeala/ShareGPT_Vicuna_unfiltered), [Evol-ShareGPT](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k), [CodeAlpaca](https://huggingface.co/datasets/lucasmccabe-lmi/CodeAlpaca-20k), [Rosetta Code](https://github.com/sahil280114/codealpaca/blob/master/data/rosetta_alpaca.json), [Evol-CodeAlpaca 1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1), [Evol-CodeAlpaca 2](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1), and a self-generated dataset centered on website creation through the [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) pipeline. We will release the full dataset soon. # Primary Usage This dataset serves as the foundation for training CrystalCoder and supports further reproduction. For training from scratch, please refer to our [training codes](https://github.com/LLM360/crystalcoder-train). For training from middle checkpoints, please load the dataloader states in checkpoints and follow [this tutorial](https://docs.cerebras.net/en/latest/wsc/tutorials/dataloader-checkpointing.html). # License Pretraining data in langauge model mostly comes from a collection of data sources with various licenses. Any use of all or part of the data here must abide by the terms of the original licenses, including attribution clauses when relevant. We refer users to [SlimPajama dataset](https://huggingface.co/datasets/cerebras/SlimPajama-627B) and [StarCoder](https://huggingface.co/datasets/bigcode/starcoderdata) for detailed license attribution.

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Mitsuki-Sakamoto/alpaca_farm-alpaca_gpt4_preference-re-preference_test

Mitsuki-Sakamoto

2023-12-13T13:41:18Z

7

0

[ "region:us" ]

null

2023-12-01T03:44:33Z

--- dataset_info: - config_name: opt-1.3b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 326835 num_examples: 194 download_size: 218330 dataset_size: 326835 - config_name: pythia-1.3b-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 340641 num_examples: 194 download_size: 229970 dataset_size: 340641 - config_name: pythia-1.3b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 splits: - name: preference num_bytes: 333120 num_examples: 194 download_size: 213247 dataset_size: 333120 - config_name: pythia-1.3b_alpaca_farm_instructions_sft_constant_slow-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 521801 num_examples: 194 download_size: 317469 dataset_size: 521801 - config_name: pythia-1.3b_alpaca_farm_instructions_sft_slow-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 363613 num_examples: 194 download_size: 229405 dataset_size: 363613 - config_name: pythia-1.3b_alpaca_farm_instructions_sft_wo_peft-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 381652 num_examples: 194 download_size: 241724 dataset_size: 381652 - config_name: pythia-1.4b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 447991 num_examples: 194 download_size: 271136 dataset_size: 447991 - config_name: pythia-1B-response-full-static-sft-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 162223 num_examples: 194 download_size: 110142 dataset_size: 162223 - config_name: pythia-1B-static-sft-reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 - name: old_output_1 dtype: string - name: old_output_2 dtype: string splits: - name: preference num_bytes: 120611 num_examples: 194 download_size: 83257 dataset_size: 120611 - config_name: reward-model-deberta-v3-large-v2 features: - name: instruction dtype: string - name: input dtype: string - name: output_1 dtype: string - name: output_2 dtype: string - name: preference dtype: int64 - name: old_preference dtype: int64 splits: - name: preference num_bytes: 113541 num_examples: 194 download_size: 76166 dataset_size: 113541 configs: - config_name: opt-1.3b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2 data_files: - split: preference path: opt-1.3b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2/preference-* - config_name: pythia-1.3b-reward-model-deberta-v3-large-v2 data_files: - split: preference path: pythia-1.3b-reward-model-deberta-v3-large-v2/preference-* - config_name: pythia-1.3b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2 data_files: - split: preference path: pythia-1.3b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2/preference-* - config_name: pythia-1.3b_alpaca_farm_instructions_sft_constant_slow-reward-model-deberta-v3-large-v2 data_files: - split: preference path: pythia-1.3b_alpaca_farm_instructions_sft_constant_slow-reward-model-deberta-v3-large-v2/preference-* - config_name: pythia-1.3b_alpaca_farm_instructions_sft_slow-reward-model-deberta-v3-large-v2 data_files: - split: preference path: pythia-1.3b_alpaca_farm_instructions_sft_slow-reward-model-deberta-v3-large-v2/preference-* - config_name: pythia-1.3b_alpaca_farm_instructions_sft_wo_peft-reward-model-deberta-v3-large-v2 data_files: - split: preference path: pythia-1.3b_alpaca_farm_instructions_sft_wo_peft-reward-model-deberta-v3-large-v2/preference-* - config_name: pythia-1.4b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2 data_files: - split: preference path: pythia-1.4b_alpaca_farm_instructions_sft-reward-model-deberta-v3-large-v2/preference-* - config_name: pythia-1B-response-full-static-sft-reward-model-deberta-v3-large-v2 data_files: - split: preference path: pythia-1B-response-full-static-sft-reward-model-deberta-v3-large-v2/preference-* - config_name: pythia-1B-static-sft-reward-model-deberta-v3-large-v2 data_files: - split: preference path: pythia-1B-static-sft-reward-model-deberta-v3-large-v2/preference-* - config_name: reward-model-deberta-v3-large-v2 data_files: - split: preference path: reward-model-deberta-v3-large-v2/preference-* ---

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ayymen/Pontoon-Translations

ayymen

2023-12-14T17:28:41Z

7

0

[ "task_categories:translation", "task_categories:text2text-generation", "annotations_creators:crowdsourced", "language:ab", "language:ace", "language:ach", "language:ady", "language:af", "language:am", "language:an", "language:ann", "language:anp", "language:ar", "language:arn", "language:as", "language:ast", "language:ay", "language:az", "language:azz", "language:ba", "language:ban", "language:bas", "language:be", "language:bg", "language:bm", "language:bn", "language:bo", "language:br", "language:brx", "language:bs", "language:bxr", "language:ca", "language:cak", "language:ceb", "language:ckb", "language:cnh", "language:co", "language:crh", "language:cs", "language:csb", "language:cv", "language:cy", "language:da", "language:dag", "language:de", "language:dsb", "language:dv", "language:dyu", "language:ee", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:ewo", "language:fa", "language:ff", "language:fi", "language:fo", "language:fr", "language:frp", "language:fuf", "language:fur", "language:fy", "language:ga", "language:gd", "language:gl", "language:gn", "language:gom", "language:gor", "language:gu", "language:guc", "language:gv", "language:ha", "language:he", "language:hi", "language:hil", "language:hr", "language:hsb", "language:ht", "language:hu", "language:hus", "language:hy", "language:hye", "language:hyw", "language:ia", "language:id", "language:ie", "language:ig", "language:ilo", "language:is", "language:it", "language:ixl", "language:ja", "language:jbo", "language:jiv", "language:jv", "language:ka", "language:kaa", "language:kab", "language:kbd", "language:kk", "language:km", "language:kmr", "language:kn", "language:ko", "language:kpv", "language:ks", "language:kw", "language:ky", "language:lb", "language:lg", "language:lij", "language:ln", "language:lo", "language:lt", "language:ltg", "language:lus", "language:lv", "language:lzz", "language:mai", "language:mdf", "language:meh", "language:mg", "language:mhr", "language:mix", "language:mk", "language:ml", "language:mn", "language:mni", "language:mr", "language:mrj", "language:ms", "language:mt", "language:my", "language:myv", "language:nan", "language:nb", "language:ne", "language:nhe", "language:nhi", "language:nia", "language:nl", "language:nn", "language:nr", "language:nso", "language:nv", "language:ny", "language:nyn", "language:oc", "language:om", "language:or", "language:os", "language:pa", "language:pai", "language:pap", "language:pl", "language:ppl", "language:ps", "language:pt", "language:quc", "language:quy", "language:qvi", "language:rm", "language:ro", "language:ru", "language:rw", "language:sah", "language:sat", "language:sc", "language:scn", "language:sco", "language:sd", "language:ses", "language:shi", "language:si", "language:sk", "language:skr", "language:sl", "language:sn", "language:snk", "language:son", "language:sq", "language:sr", "language:ss", "language:st", "language:su", "language:sv", "language:sw", "language:syr", "language:szl", "language:ta", "language:te", "language:tg", "language:th", "language:ti", "language:tig", "language:tk", "language:tl", "language:tn", "language:tok", "language:tr", "language:trs", "language:ts", "language:tsz", "language:tt", "language:tw", "language:tyv", "language:tzm", "language:udm", "language:ug", "language:uk", "language:ur", "language:uz", "language:ve", "language:vec", "language:vi", "language:vot", "language:wo", "language:xcl", "language:xh", "language:yi", "language:yo", "language:yua", "language:yue", "language:zam", "language:zgh", "language:zh", "language:zu", "language:zza", "license:mpl-2.0", "region:us" ]

[ "translation", "text2text-generation" ]

2023-12-03T21:44:52Z

--- configs: - config_name: ab-en data_files: "ab-en.tsv" - config_name: ace-en data_files: "ace-en.tsv" - config_name: ach-en data_files: "ach-en.tsv" - config_name: ady-en data_files: "ady-en.tsv" - config_name: af-en data_files: "af-en.tsv" - config_name: am-en data_files: "am-en.tsv" - config_name: an-en data_files: "an-en.tsv" - config_name: ann-en data_files: "ann-en.tsv" - config_name: anp-en data_files: "anp-en.tsv" - config_name: ar-en data_files: "ar-en.tsv" - config_name: arn-en data_files: "arn-en.tsv" - config_name: as-en data_files: "as-en.tsv" - config_name: ast-en data_files: "ast-en.tsv" - config_name: ay-en data_files: "ay-en.tsv" - config_name: az-en data_files: "az-en.tsv" - config_name: azz-en data_files: "azz-en.tsv" - config_name: ba-en data_files: "ba-en.tsv" - config_name: ban-en data_files: "ban-en.tsv" - config_name: bas-en data_files: "bas-en.tsv" - config_name: be-en data_files: "be-en.tsv" - config_name: bg-en data_files: "bg-en.tsv" - config_name: bm-en data_files: "bm-en.tsv" - config_name: bn-BD-en data_files: "bn-BD-en.tsv" - config_name: bn-IN-en data_files: "bn-IN-en.tsv" - config_name: bn-en data_files: "bn-en.tsv" - config_name: bo-en data_files: "bo-en.tsv" - config_name: br-en data_files: "br-en.tsv" - config_name: brx-en data_files: "brx-en.tsv" - config_name: bs-en data_files: "bs-en.tsv" - config_name: bxr-en data_files: "bxr-en.tsv" - config_name: ca-en data_files: "ca-en.tsv" - config_name: ca-valencia-en data_files: "ca-valencia-en.tsv" - config_name: cak-en data_files: "cak-en.tsv" - config_name: ceb-en data_files: "ceb-en.tsv" - config_name: ckb-en data_files: "ckb-en.tsv" - config_name: cnh-en data_files: "cnh-en.tsv" - config_name: co-en data_files: "co-en.tsv" - config_name: crh-en data_files: "crh-en.tsv" - config_name: cs-en data_files: "cs-en.tsv" - config_name: csb-en data_files: "csb-en.tsv" - config_name: cv-en data_files: "cv-en.tsv" - config_name: cy-en data_files: "cy-en.tsv" - config_name: da-en data_files: "da-en.tsv" - config_name: dag-en data_files: "dag-en.tsv" - config_name: de-en data_files: "de-en.tsv" - config_name: dsb-en data_files: "dsb-en.tsv" - config_name: dv-en data_files: "dv-en.tsv" - config_name: dyu-en data_files: "dyu-en.tsv" - config_name: ee-en data_files: "ee-en.tsv" - config_name: el-en data_files: "el-en.tsv" - config_name: en-CA-en data_files: "en-CA-en.tsv" - config_name: en-GB-en data_files: "en-GB-en.tsv" - config_name: en-ZA-en data_files: "en-ZA-en.tsv" - config_name: eo-en data_files: "eo-en.tsv" - config_name: es-AR-en data_files: "es-AR-en.tsv" - config_name: es-CL-en data_files: "es-CL-en.tsv" - config_name: es-ES-en data_files: "es-ES-en.tsv" - config_name: es-MX-en data_files: "es-MX-en.tsv" - config_name: es-en data_files: "es-en.tsv" - config_name: et-en data_files: "et-en.tsv" - config_name: eu-en data_files: "eu-en.tsv" - config_name: ewo-en data_files: "ewo-en.tsv" - config_name: fa-en data_files: "fa-en.tsv" - config_name: ff-en data_files: "ff-en.tsv" - config_name: fi-en data_files: "fi-en.tsv" - config_name: fo-en data_files: "fo-en.tsv" - config_name: fr-en data_files: "fr-en.tsv" - config_name: frp-en data_files: "frp-en.tsv" - config_name: fuf-en data_files: "fuf-en.tsv" - config_name: fur-en data_files: "fur-en.tsv" - config_name: fy-NL-en data_files: "fy-NL-en.tsv" - config_name: ga-IE-en data_files: "ga-IE-en.tsv" - config_name: gd-en data_files: "gd-en.tsv" - config_name: gl-en data_files: "gl-en.tsv" - config_name: gn-en data_files: "gn-en.tsv" - config_name: gom-en data_files: "gom-en.tsv" - config_name: gor-en data_files: "gor-en.tsv" - config_name: gu-IN-en data_files: "gu-IN-en.tsv" - config_name: guc-en data_files: "guc-en.tsv" - config_name: gv-en data_files: "gv-en.tsv" - config_name: ha-en data_files: "ha-en.tsv" - config_name: he-en data_files: "he-en.tsv" - config_name: hi-IN-en data_files: "hi-IN-en.tsv" - config_name: hi-en data_files: "hi-en.tsv" - config_name: hil-en data_files: "hil-en.tsv" - config_name: hr-en data_files: "hr-en.tsv" - config_name: hsb-en data_files: "hsb-en.tsv" - config_name: ht-en data_files: "ht-en.tsv" - config_name: hu-en data_files: "hu-en.tsv" - config_name: hus-en data_files: "hus-en.tsv" - config_name: hy-AM-en data_files: "hy-AM-en.tsv" - config_name: hye-en data_files: "hye-en.tsv" - config_name: hyw-en data_files: "hyw-en.tsv" - config_name: ia-en data_files: "ia-en.tsv" - config_name: id-en data_files: "id-en.tsv" - config_name: ie-en data_files: "ie-en.tsv" - config_name: ig-en data_files: "ig-en.tsv" - config_name: ilo-en data_files: "ilo-en.tsv" - config_name: is-en data_files: "is-en.tsv" - config_name: it-en data_files: "it-en.tsv" - config_name: ixl-en data_files: "ixl-en.tsv" - config_name: ja-en data_files: "ja-en.tsv" - config_name: jbo-en data_files: "jbo-en.tsv" - config_name: jiv-en data_files: "jiv-en.tsv" - config_name: jv-en data_files: "jv-en.tsv" - config_name: ka-en data_files: "ka-en.tsv" - config_name: kaa-en data_files: "kaa-en.tsv" - config_name: kab-en data_files: "kab-en.tsv" - config_name: kbd-en data_files: "kbd-en.tsv" - config_name: kk-en data_files: "kk-en.tsv" - config_name: km-en data_files: "km-en.tsv" - config_name: kmr-en data_files: "kmr-en.tsv" - config_name: kn-en data_files: "kn-en.tsv" - config_name: ko-en data_files: "ko-en.tsv" - config_name: kpv-en data_files: "kpv-en.tsv" - config_name: ks-en data_files: "ks-en.tsv" - config_name: kw-en data_files: "kw-en.tsv" - config_name: ky-en data_files: "ky-en.tsv" - config_name: lb-en data_files: "lb-en.tsv" - config_name: lg-en data_files: "lg-en.tsv" - config_name: lij-en data_files: "lij-en.tsv" - config_name: ln-en data_files: "ln-en.tsv" - config_name: lo-en data_files: "lo-en.tsv" - config_name: lt-en data_files: "lt-en.tsv" - config_name: ltg-en data_files: "ltg-en.tsv" - config_name: lus-en data_files: "lus-en.tsv" - config_name: lv-en data_files: "lv-en.tsv" - config_name: lzz-en data_files: "lzz-en.tsv" - config_name: mai-en data_files: "mai-en.tsv" - config_name: mdf-en data_files: "mdf-en.tsv" - config_name: meh-en data_files: "meh-en.tsv" - config_name: mg-en data_files: "mg-en.tsv" - config_name: mhr-en data_files: "mhr-en.tsv" - config_name: mix-en data_files: "mix-en.tsv" - config_name: mk-en data_files: "mk-en.tsv" - config_name: ml-en data_files: "ml-en.tsv" - config_name: mn-en data_files: "mn-en.tsv" - config_name: mni-en data_files: "mni-en.tsv" - config_name: mr-en data_files: "mr-en.tsv" - config_name: mrj-en data_files: "mrj-en.tsv" - config_name: ms-en data_files: "ms-en.tsv" - config_name: mt-en data_files: "mt-en.tsv" - config_name: my-en data_files: "my-en.tsv" - config_name: myv-en data_files: "myv-en.tsv" - config_name: nan-tw-en data_files: "nan-tw-en.tsv" - config_name: nb-NO-en data_files: "nb-NO-en.tsv" - config_name: ne-NP-en data_files: "ne-NP-en.tsv" - config_name: nhe-en data_files: "nhe-en.tsv" - config_name: nhi-en data_files: "nhi-en.tsv" - config_name: nia-en data_files: "nia-en.tsv" - config_name: nl-en data_files: "nl-en.tsv" - config_name: nn-NO-en data_files: "nn-NO-en.tsv" - config_name: nr-en data_files: "nr-en.tsv" - config_name: nso-en data_files: "nso-en.tsv" - config_name: nv-en data_files: "nv-en.tsv" - config_name: ny-en data_files: "ny-en.tsv" - config_name: nyn-en data_files: "nyn-en.tsv" - config_name: oc-en data_files: "oc-en.tsv" - config_name: om-en data_files: "om-en.tsv" - config_name: or-en data_files: "or-en.tsv" - config_name: os-en data_files: "os-en.tsv" - config_name: pa-IN-en data_files: "pa-IN-en.tsv" - config_name: pa-PK-en data_files: "pa-PK-en.tsv" - config_name: pai-en data_files: "pai-en.tsv" - config_name: pap-AW-en data_files: "pap-AW-en.tsv" - config_name: pl-en data_files: "pl-en.tsv" - config_name: ppl-en data_files: "ppl-en.tsv" - config_name: ps-en data_files: "ps-en.tsv" - config_name: pt-BR-en data_files: "pt-BR-en.tsv" - config_name: pt-PT-en data_files: "pt-PT-en.tsv" - config_name: pt-en data_files: "pt-en.tsv" - config_name: quc-en data_files: "quc-en.tsv" - config_name: quy-en data_files: "quy-en.tsv" - config_name: qvi-en data_files: "qvi-en.tsv" - config_name: rm-en data_files: "rm-en.tsv" - config_name: rm-sursilv-en data_files: "rm-sursilv-en.tsv" - config_name: rm-vallader-en data_files: "rm-vallader-en.tsv" - config_name: ro-en data_files: "ro-en.tsv" - config_name: ru-en data_files: "ru-en.tsv" - config_name: rw-en data_files: "rw-en.tsv" - config_name: sah-en data_files: "sah-en.tsv" - config_name: sat-en data_files: "sat-en.tsv" - config_name: sc-en data_files: "sc-en.tsv" - config_name: scn-en data_files: "scn-en.tsv" - config_name: sco-en data_files: "sco-en.tsv" - config_name: sd-en data_files: "sd-en.tsv" - config_name: ses-en data_files: "ses-en.tsv" - config_name: shi-en data_files: "shi-en.tsv" - config_name: si-en data_files: "si-en.tsv" - config_name: sk-en data_files: "sk-en.tsv" - config_name: skr-en data_files: "skr-en.tsv" - config_name: sl-en data_files: "sl-en.tsv" - config_name: sn-en data_files: "sn-en.tsv" - config_name: snk-en data_files: "snk-en.tsv" - config_name: son-en data_files: "son-en.tsv" - config_name: sq-en data_files: "sq-en.tsv" - config_name: sr-en data_files: "sr-en.tsv" - config_name: ss-en data_files: "ss-en.tsv" - config_name: st-en data_files: "st-en.tsv" - config_name: su-en data_files: "su-en.tsv" - config_name: sv-SE-en data_files: "sv-SE-en.tsv" - config_name: sw-en data_files: "sw-en.tsv" - config_name: syr-en data_files: "syr-en.tsv" - config_name: szl-en data_files: "szl-en.tsv" - config_name: ta-en data_files: "ta-en.tsv" - config_name: te-en data_files: "te-en.tsv" - config_name: tg-en data_files: "tg-en.tsv" - config_name: th-en data_files: "th-en.tsv" - config_name: ti-en data_files: "ti-en.tsv" - config_name: tig-en data_files: "tig-en.tsv" - config_name: tk-en data_files: "tk-en.tsv" - config_name: tl-en data_files: "tl-en.tsv" - config_name: tn-en data_files: "tn-en.tsv" - config_name: tok-en data_files: "tok-en.tsv" - config_name: tr-en data_files: "tr-en.tsv" - config_name: trs-en data_files: "trs-en.tsv" - config_name: ts-en data_files: "ts-en.tsv" - config_name: tsz-en data_files: "tsz-en.tsv" - config_name: tt-en data_files: "tt-en.tsv" - config_name: tw-en data_files: "tw-en.tsv" - config_name: tyv-en data_files: "tyv-en.tsv" - config_name: tzm-en data_files: "tzm-en.tsv" - config_name: udm-en data_files: "udm-en.tsv" - config_name: ug-en data_files: "ug-en.tsv" - config_name: uk-en data_files: "uk-en.tsv" - config_name: ur-en data_files: "ur-en.tsv" - config_name: uz-en data_files: "uz-en.tsv" - config_name: ve-en data_files: "ve-en.tsv" - config_name: vec-en data_files: "vec-en.tsv" - config_name: vi-en data_files: "vi-en.tsv" - config_name: vot-en data_files: "vot-en.tsv" - config_name: wo-en data_files: "wo-en.tsv" - config_name: xcl-en data_files: "xcl-en.tsv" - config_name: xh-en data_files: "xh-en.tsv" - config_name: yi-en data_files: "yi-en.tsv" - config_name: yo-en data_files: "yo-en.tsv" - config_name: yua-en data_files: "yua-en.tsv" - config_name: yue-en data_files: "yue-en.tsv" - config_name: zam-en data_files: "zam-en.tsv" - config_name: zgh-en data_files: "zgh-en.tsv" default: true - config_name: zh-CN-en data_files: "zh-CN-en.tsv" - config_name: zh-HK-en data_files: "zh-HK-en.tsv" - config_name: zh-TW-en data_files: "zh-TW-en.tsv" - config_name: zu-en data_files: "zu-en.tsv" - config_name: zza-en data_files: "zza-en.tsv" license: mpl-2.0 task_categories: - translation - text2text-generation pretty_name: Pontoon Translations annotations_creators: - crowdsourced language: - ab - ace - ach - ady - af - am - an - ann - anp - ar - arn - as - ast - ay - az - azz - ba - ban - bas - be - bg - bm - bn - bn - bn - bo - br - brx - bs - bxr - ca - ca - cak - ceb - ckb - cnh - co - crh - cs - csb - cv - cy - da - dag - de - dsb - dv - dyu - ee - el - en - en - en - en - eo - es - es - es - es - es - et - eu - ewo - fa - ff - fi - fo - fr - frp - fuf - fur - fy - ga - gd - gl - gn - gom - gor - gu - guc - gv - ha - he - hi - hi - hil - hr - hsb - ht - hu - hus - hy - hye - hyw - ia - id - ie - ig - ilo - is - it - ixl - ja - jbo - jiv - jv - ka - kaa - kab - kbd - kk - km - kmr - kn - ko - kpv - ks - kw - ky - lb - lg - lij - ln - lo - lt - ltg - lus - lv - lzz - mai - mdf - meh - mg - mhr - mix - mk - ml - mn - mni - mr - mrj - ms - mt - my - myv - nan - nb - ne - nhe - nhi - nia - nl - nn - nr - nso - nv - ny - nyn - oc - om - or - os - pa - pa - pai - pap - pl - ppl - ps - pt - pt - pt - quc - quy - qvi - rm - rm - rm - ro - ru - rw - sah - sat - sc - scn - sco - sd - ses - shi - si - sk - skr - sl - sn - snk - son - sq - sr - ss - st - su - sv - sw - syr - szl - ta - te - tg - th - ti - tig - tk - tl - tn - tok - tr - trs - ts - tsz - tt - tw - tyv - tzm - udm - ug - uk - ur - uz - ve - vec - vi - vot - wo - xcl - xh - yi - yo - yua - yue - zam - zgh - zh - zh - zh - zu - zza --- # Pontoon Translations This is a dataset containing translations from Mozilla's [Pontoon](https://pontoon.mozilla.org) localization platform for more than 200 languages. Source sentences are in English.

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SeaLLMs/Sea-bench

SeaLLMs

2023-12-12T13:34:52Z

7

8

[ "size_categories:n<1K", "language:en", "language:vi", "language:id", "language:km", "language:th", "language:lo", "language:my", "language:ms", "language:tl", "language:zh", "license:apache-2.0", "region:us" ]

null

2023-12-04T03:53:06Z

--- license: apache-2.0 language: - en - vi - id - km - th - lo - my - ms - tl - zh size_categories: - n<1K configs: - config_name: Question data_files: question.jsonl --- # Sea-bench - a benchmark for evaluating chat assistants in Southeast Asian languages. **See interactive benchmark view at [Spaces/SeaLLMs/Sea-bench](https://huggingface.co/spaces/SeaLLMs/Sea-bench)** While there are popular benchmarks to evaluate LLMs as helpful assistants, such as [MT-bench](https://huggingface.co/spaces/lmsys/mt-bench), they are only English-based and likely to be unsuitable to evaluate performances in low-resource languages. Due to this lack of multilingual benchmarks for assistant-style models, we engaged native linguists to build a multilingual test set with instructions that cover 9 Southeast Asian languages, called [Sea-bench](https://huggingface.co/datasets/SeaLLMs/Sea-bench). The linguists sourced such data by manually translating open-source English test sets, collecting real user questions from local forums and websites, collecting real math and reasoning questions from reputable sources, as well as writing test instructions and questions themselves. Our Sea-Bench consists of diverse categories of instructions to evaluate models, as described in the following: * Task-solving: This type of data comprises various text understanding and processing tasks that test the ability of the language model to perform certain NLP tasks such as summarization, translation, etc. * Math-reasoning: This includes math problems and logical reasoning tasks. * General-instruction data: This type of data consists of general user-centric instructions, which evaluate the model's ability in general knowledge and writing. Examples for this type can be requests for recommendations, such as "Suggest three popular books," or instructions that require the model to generate creative outputs, like "Write a short story about a dragon." * NaturalQA: This consists of queries posted by real users, often in popular local forums, involving local contexts or scenarios. The aim is to test the model's capacity to understand and respond coherently to colloquial language, idioms, and locally contextual references. * Safety: This includes both general safety and local context-related safety instructions. The instructions could involve testing the model's understanding of safe practices, its ability to advise on safety rules, or its capacity to respond to safety-related queries. While most general safety questions are translated from open sources, other local context-related safety instructions are written by linguists of each language. Safety data only covers Vietnamese, Indonesian, and Thai. The released Sea-bench test set is a **small subset** that contains **20 questions per task type per language**. Sea-bench can be evaluated similar to MT-bench, using GPT-4 as a judge in a score-based grading metrics and a peer comparison (or pairwise comparison) manner. Instructions to evaluate models on Sea-bench (score-based grading): ```bash # Clone and install LLM judge: https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge # Download SeaLLMs/Sea-bench files to folder `Sea-bench` # Copy `Sea-bench` folder to llm `FastChat/fastchat/llm_judge` # Run generation, similar to MT-bench, e.g: python gen_model_answer.py --model-path lmsys/vicuna-7b-v1.5 --model-id vicuna-7b-v1.5 --bench-name Sea-bench # Run LLM judgement python gen_judgment.py \ --parallel 6 \ --bench-name Sea-bench \ --model-list ${YOUR_MODEL_NAME} ``` ## Evaluation results: ![fig_sea_bench_side_by_side.png](fig_sea_bench_side_by_side.png) Sea-bench is used to evaluate [SeaLLMs](https://huggingface.co/SeaLLMs/SeaLLM-Chat-13b), a group of language models built with focus in Southeast Asian languages. ## Contribution If you have Sea-bench evaluations on your model that you would like into the aggregated results. Please kindly submit a pull request with an updated [model_judgment/gpt-4_single.jsonl ](https://huggingface.co/datasets/SeaLLMs/Sea-bench/tree/main/model_judgment/gpt-4_single.jsonl) file. Please use a different model name than the ones indicated in the file. ## Citation If you find our project useful, hope you can star our repo and cite our work as follows. Corresponding Author: [[email protected]](mailto:[email protected]) ``` @article{damonlpsg2023seallm, author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing}, title = {SeaLLMs - Large Language Models for Southeast Asia}, year = 2023, Eprint = {arXiv:2312.00738}, } ```

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open-llm-leaderboard/details_mncai__yi-34B-v3

open-llm-leaderboard

2023-12-11T01:54:36Z

7

0

[ "region:us" ]

null

2023-12-11T01:53:57Z

--- pretty_name: Evaluation run of mncai/yi-34B-v3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mncai/yi-34B-v3](https://huggingface.co/mncai/yi-34B-v3) 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_mncai__yi-34B-v3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-11T01:51:08.694143](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__yi-34B-v3/blob/main/results_2023-12-11T01-51-08.694143.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\": 0.7536948044621744,\n\ \ \"acc_stderr\": 0.028378789321173548,\n \"acc_norm\": 0.7581198984934292,\n\ \ \"acc_norm_stderr\": 0.02891498378900509,\n \"mc1\": 0.4186046511627907,\n\ \ \"mc1_stderr\": 0.017270015284476855,\n \"mc2\": 0.5753679426280454,\n\ \ \"mc2_stderr\": 0.014962842073717312\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6390784982935154,\n \"acc_stderr\": 0.014034761386175452,\n\ \ \"acc_norm\": 0.6706484641638225,\n \"acc_norm_stderr\": 0.013734057652635476\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6487751443935471,\n\ \ \"acc_stderr\": 0.004763774981834676,\n \"acc_norm\": 0.8511252738498307,\n\ \ \"acc_norm_stderr\": 0.0035523745313052004\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6962962962962963,\n\ \ \"acc_stderr\": 0.03972552884785137,\n \"acc_norm\": 0.6962962962962963,\n\ \ \"acc_norm_stderr\": 0.03972552884785137\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.881578947368421,\n \"acc_stderr\": 0.026293995855474935,\n\ \ \"acc_norm\": 0.881578947368421,\n \"acc_norm_stderr\": 0.026293995855474935\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.79,\n\ \ \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n \ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.8188679245283019,\n \"acc_stderr\": 0.023702963526757798,\n\ \ \"acc_norm\": 0.8188679245283019,\n \"acc_norm_stderr\": 0.023702963526757798\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9097222222222222,\n\ \ \"acc_stderr\": 0.023964965777906935,\n \"acc_norm\": 0.9097222222222222,\n\ \ \"acc_norm_stderr\": 0.023964965777906935\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|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-college_mathematics|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness|hendrycksTest-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\": 0.5392156862745098,\n \"acc_stderr\": 0.04959859966384181,\n\ \ \"acc_norm\": 0.5392156862745098,\n \"acc_norm_stderr\": 0.04959859966384181\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n\ \ \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7787234042553192,\n \"acc_stderr\": 0.027136349602424056,\n\ \ \"acc_norm\": 0.7787234042553192,\n \"acc_norm_stderr\": 0.027136349602424056\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5701754385964912,\n\ \ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.5701754385964912,\n\ \ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7379310344827587,\n \"acc_stderr\": 0.036646663372252565,\n\ \ \"acc_norm\": 0.7379310344827587,\n \"acc_norm_stderr\": 0.036646663372252565\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.6957671957671958,\n \"acc_stderr\": 0.02369541500946309,\n \"\ acc_norm\": 0.6957671957671958,\n \"acc_norm_stderr\": 0.02369541500946309\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5396825396825397,\n\ \ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.5396825396825397,\n\ \ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562427,\n \ \ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562427\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.9,\n\ \ \"acc_stderr\": 0.017066403719657255,\n \"acc_norm\": 0.9,\n \ \ \"acc_norm_stderr\": 0.017066403719657255\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.6699507389162561,\n \"acc_stderr\": 0.033085304262282574,\n\ \ \"acc_norm\": 0.6699507389162561,\n \"acc_norm_stderr\": 0.033085304262282574\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\"\ : 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8545454545454545,\n \"acc_stderr\": 0.027530196355066584,\n\ \ \"acc_norm\": 0.8545454545454545,\n \"acc_norm_stderr\": 0.027530196355066584\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9343434343434344,\n \"acc_stderr\": 0.01764652667723333,\n \"\ acc_norm\": 0.9343434343434344,\n \"acc_norm_stderr\": 0.01764652667723333\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9740932642487047,\n \"acc_stderr\": 0.01146452335695318,\n\ \ \"acc_norm\": 0.9740932642487047,\n \"acc_norm_stderr\": 0.01146452335695318\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.8205128205128205,\n \"acc_stderr\": 0.019457390787681803,\n\ \ \"acc_norm\": 0.8205128205128205,\n \"acc_norm_stderr\": 0.019457390787681803\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.4111111111111111,\n \"acc_stderr\": 0.02999992350870669,\n \ \ \"acc_norm\": 0.4111111111111111,\n \"acc_norm_stderr\": 0.02999992350870669\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8529411764705882,\n \"acc_stderr\": 0.023005459446673964,\n\ \ \"acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.023005459446673964\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4966887417218543,\n \"acc_stderr\": 0.04082393379449654,\n \"\ acc_norm\": 0.4966887417218543,\n \"acc_norm_stderr\": 0.04082393379449654\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9284403669724771,\n \"acc_stderr\": 0.01105125524781546,\n \"\ acc_norm\": 0.9284403669724771,\n \"acc_norm_stderr\": 0.01105125524781546\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6620370370370371,\n \"acc_stderr\": 0.03225941352631295,\n \"\ acc_norm\": 0.6620370370370371,\n \"acc_norm_stderr\": 0.03225941352631295\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9068627450980392,\n \"acc_stderr\": 0.020397853969426994,\n \"\ acc_norm\": 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969426994\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.9156118143459916,\n \"acc_stderr\": 0.01809424711647332,\n \ \ \"acc_norm\": 0.9156118143459916,\n \"acc_norm_stderr\": 0.01809424711647332\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8026905829596412,\n\ \ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\ \ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n\ \ \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.9008264462809917,\n \"acc_stderr\": 0.02728524631275896,\n \"\ acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.02728524631275896\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n\ \ \"acc_stderr\": 0.029239272675632748,\n \"acc_norm\": 0.8981481481481481,\n\ \ \"acc_norm_stderr\": 0.029239272675632748\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8650306748466258,\n \"acc_stderr\": 0.026845765054553855,\n\ \ \"acc_norm\": 0.8650306748466258,\n \"acc_norm_stderr\": 0.026845765054553855\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5714285714285714,\n\ \ \"acc_stderr\": 0.04697113923010213,\n \"acc_norm\": 0.5714285714285714,\n\ \ \"acc_norm_stderr\": 0.04697113923010213\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n\ \ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9401709401709402,\n\ \ \"acc_stderr\": 0.015537514263253862,\n \"acc_norm\": 0.9401709401709402,\n\ \ \"acc_norm_stderr\": 0.015537514263253862\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \ \ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9029374201787995,\n\ \ \"acc_stderr\": 0.010586474712018283,\n \"acc_norm\": 0.9029374201787995,\n\ \ \"acc_norm_stderr\": 0.010586474712018283\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8121387283236994,\n \"acc_stderr\": 0.02102926975242323,\n\ \ \"acc_norm\": 0.8121387283236994,\n \"acc_norm_stderr\": 0.02102926975242323\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.7039106145251397,\n\ \ \"acc_stderr\": 0.015268677317602274,\n \"acc_norm\": 0.7039106145251397,\n\ \ \"acc_norm_stderr\": 0.015268677317602274\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8398692810457516,\n \"acc_stderr\": 0.020998740930362303,\n\ \ \"acc_norm\": 0.8398692810457516,\n \"acc_norm_stderr\": 0.020998740930362303\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8167202572347267,\n\ \ \"acc_stderr\": 0.021974198848265812,\n \"acc_norm\": 0.8167202572347267,\n\ \ \"acc_norm_stderr\": 0.021974198848265812\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8641975308641975,\n \"acc_stderr\": 0.0190615881815054,\n\ \ \"acc_norm\": 0.8641975308641975,\n \"acc_norm_stderr\": 0.0190615881815054\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6276595744680851,\n \"acc_stderr\": 0.02883892147125145,\n \ \ \"acc_norm\": 0.6276595744680851,\n \"acc_norm_stderr\": 0.02883892147125145\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5971316818774446,\n\ \ \"acc_stderr\": 0.01252695557711801,\n \"acc_norm\": 0.5971316818774446,\n\ \ \"acc_norm_stderr\": 0.01252695557711801\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8345588235294118,\n \"acc_stderr\": 0.02257177102549474,\n\ \ \"acc_norm\": 0.8345588235294118,\n \"acc_norm_stderr\": 0.02257177102549474\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8169934640522876,\n \"acc_stderr\": 0.015643069911273344,\n \ \ \"acc_norm\": 0.8169934640522876,\n \"acc_norm_stderr\": 0.015643069911273344\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8489795918367347,\n \"acc_stderr\": 0.022923004094736844,\n\ \ \"acc_norm\": 0.8489795918367347,\n \"acc_norm_stderr\": 0.022923004094736844\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8855721393034826,\n\ \ \"acc_stderr\": 0.022509345325101706,\n \"acc_norm\": 0.8855721393034826,\n\ \ \"acc_norm_stderr\": 0.022509345325101706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \ \ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5903614457831325,\n\ \ \"acc_stderr\": 0.038284011150790206,\n \"acc_norm\": 0.5903614457831325,\n\ \ \"acc_norm_stderr\": 0.038284011150790206\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.024648068961366152,\n\ \ \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.024648068961366152\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4186046511627907,\n\ \ \"mc1_stderr\": 0.017270015284476855,\n \"mc2\": 0.5753679426280454,\n\ \ \"mc2_stderr\": 0.014962842073717312\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.835043409629045,\n \"acc_stderr\": 0.010430917468237419\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.645185746777862,\n \ \ \"acc_stderr\": 0.013179083387979214\n }\n}\n```" repo_url: https://huggingface.co/mncai/yi-34B-v3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|arc:challenge|25_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-11T01-51-08.694143.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|gsm8k|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hellaswag|10_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-51-08.694143.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T01-51-08.694143.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T01-51-08.694143.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T01_51_08.694143 path: - '**/details_harness|winogrande|5_2023-12-11T01-51-08.694143.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-11T01-51-08.694143.parquet' - config_name: results data_files: - split: 2023_12_11T01_51_08.694143 path: - results_2023-12-11T01-51-08.694143.parquet - split: latest path: - results_2023-12-11T01-51-08.694143.parquet --- # Dataset Card for Evaluation run of mncai/yi-34B-v3 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/mncai/yi-34B-v3 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [mncai/yi-34B-v3](https://huggingface.co/mncai/yi-34B-v3) 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_mncai__yi-34B-v3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-11T01:51:08.694143](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__yi-34B-v3/blob/main/results_2023-12-11T01-51-08.694143.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": 0.7536948044621744, "acc_stderr": 0.028378789321173548, "acc_norm": 0.7581198984934292, "acc_norm_stderr": 0.02891498378900509, "mc1": 0.4186046511627907, "mc1_stderr": 0.017270015284476855, "mc2": 0.5753679426280454, "mc2_stderr": 0.014962842073717312 }, "harness|arc:challenge|25": { "acc": 0.6390784982935154, "acc_stderr": 0.014034761386175452, "acc_norm": 0.6706484641638225, "acc_norm_stderr": 0.013734057652635476 }, "harness|hellaswag|10": { "acc": 0.6487751443935471, "acc_stderr": 0.004763774981834676, "acc_norm": 0.8511252738498307, "acc_norm_stderr": 0.0035523745313052004 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6962962962962963, "acc_stderr": 0.03972552884785137, "acc_norm": 0.6962962962962963, "acc_norm_stderr": 0.03972552884785137 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.881578947368421, "acc_stderr": 0.026293995855474935, "acc_norm": 0.881578947368421, "acc_norm_stderr": 0.026293995855474935 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8188679245283019, "acc_stderr": 0.023702963526757798, "acc_norm": 0.8188679245283019, "acc_norm_stderr": 0.023702963526757798 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.9097222222222222, "acc_stderr": 0.023964965777906935, "acc_norm": 0.9097222222222222, "acc_norm_stderr": 0.023964965777906935 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.58, "acc_stderr": 0.049604496374885836, "acc_norm": 0.58, 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"acc_stderr": 0.04309118709946458, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.04309118709946458 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8489795918367347, "acc_stderr": 0.022923004094736844, "acc_norm": 0.8489795918367347, "acc_norm_stderr": 0.022923004094736844 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101706, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101706 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.92, "acc_stderr": 0.0272659924344291, "acc_norm": 0.92, "acc_norm_stderr": 0.0272659924344291 }, "harness|hendrycksTest-virology|5": { "acc": 0.5903614457831325, "acc_stderr": 0.038284011150790206, "acc_norm": 0.5903614457831325, "acc_norm_stderr": 0.038284011150790206 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8830409356725146, "acc_stderr": 0.024648068961366152, "acc_norm": 0.8830409356725146, "acc_norm_stderr": 0.024648068961366152 }, "harness|truthfulqa:mc|0": { "mc1": 0.4186046511627907, "mc1_stderr": 0.017270015284476855, "mc2": 0.5753679426280454, "mc2_stderr": 0.014962842073717312 }, "harness|winogrande|5": { "acc": 0.835043409629045, "acc_stderr": 0.010430917468237419 }, "harness|gsm8k|5": { "acc": 0.645185746777862, "acc_stderr": 0.013179083387979214 } } ``` ### 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]

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kpriyanshu256/semeval-task-8-b-v2-test-paraphrase-2

kpriyanshu256

2023-12-11T03:26:21Z

7

0

[ "region:us" ]

null

2023-12-11T03:26:20Z

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: text dtype: string - name: model dtype: string - name: source dtype: string - name: label dtype: int64 - name: id dtype: int64 - name: paraphrase dtype: string - name: paraphrase2 dtype: string splits: - name: test num_bytes: 11109023 num_examples: 3000 download_size: 5184022 dataset_size: 11109023 --- # Dataset Card for "semeval-task-8-b-v2-test-paraphrase-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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Lkhagvasurenam/STTmn

Lkhagvasurenam

2023-12-11T04:10:17Z

7

0

[ "region:us" ]

null

2023-12-11T04:10:02Z

--- dataset_info: features: - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string splits: - name: train num_bytes: 7998858.1 num_examples: 9 - name: test num_bytes: 1145369.0 num_examples: 1 download_size: 9066089 dataset_size: 9144227.1 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

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open-llm-leaderboard/details_mistralai__Mixtral-8x7B-v0.1

open-llm-leaderboard

2023-12-15T14:38:08Z

7

0

[ "region:us" ]

null

2023-12-11T18:07:04Z

--- pretty_name: Evaluation run of mistralai/Mixtral-8x7B-v0.1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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_mistralai__Mixtral-8x7B-v0.1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-15T14:35:04.630519](https://huggingface.co/datasets/open-llm-leaderboard/details_mistralai__Mixtral-8x7B-v0.1/blob/main/results_2023-12-15T14-35-04.630519.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\": 0.6972963513811701,\n\ \ \"acc_stderr\": 0.03075228309763406,\n \"acc_norm\": 0.7033766203346898,\n\ \ \"acc_norm_stderr\": 0.03134647583855853,\n \"mc1\": 0.3268053855569155,\n\ \ \"mc1_stderr\": 0.01641987473113503,\n \"mc2\": 0.47623983056821834,\n\ \ \"mc2_stderr\": 0.01423216788461584\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6109215017064846,\n \"acc_stderr\": 0.014247309976045607,\n\ \ \"acc_norm\": 0.643344709897611,\n \"acc_norm_stderr\": 0.01399805690262019\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6548496315475005,\n\ \ \"acc_stderr\": 0.004744456628455121,\n \"acc_norm\": 0.8570005974905397,\n\ \ \"acc_norm_stderr\": 0.00349356791409329\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.674074074074074,\n\ \ \"acc_stderr\": 0.040491220417025055,\n \"acc_norm\": 0.674074074074074,\n\ \ \"acc_norm_stderr\": 0.040491220417025055\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8026315789473685,\n \"acc_stderr\": 0.03238981601699397,\n\ \ \"acc_norm\": 0.8026315789473685,\n \"acc_norm_stderr\": 0.03238981601699397\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.67,\n\ \ \"acc_stderr\": 0.04725815626252609,\n \"acc_norm\": 0.67,\n \ \ \"acc_norm_stderr\": 0.04725815626252609\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.769811320754717,\n \"acc_stderr\": 0.025907897122408173,\n\ \ \"acc_norm\": 0.769811320754717,\n \"acc_norm_stderr\": 0.025907897122408173\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8472222222222222,\n\ \ \"acc_stderr\": 0.030085743248565666,\n \"acc_norm\": 0.8472222222222222,\n\ \ \"acc_norm_stderr\": 0.030085743248565666\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n\ \ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.6878612716763006,\n\ \ \"acc_stderr\": 0.03533133389323657,\n \"acc_norm\": 0.6878612716763006,\n\ \ \"acc_norm_stderr\": 0.03533133389323657\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.47058823529411764,\n \"acc_stderr\": 0.049665709039785295,\n\ \ \"acc_norm\": 0.47058823529411764,\n \"acc_norm_stderr\": 0.049665709039785295\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\ \ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6680851063829787,\n \"acc_stderr\": 0.030783736757745647,\n\ \ \"acc_norm\": 0.6680851063829787,\n \"acc_norm_stderr\": 0.030783736757745647\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6228070175438597,\n\ \ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.6228070175438597,\n\ \ \"acc_norm_stderr\": 0.04559522141958216\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\"\ : 0.4417989417989418,\n \"acc_stderr\": 0.025576257061253833,\n \"\ acc_norm\": 0.4417989417989418,\n \"acc_norm_stderr\": 0.025576257061253833\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \ \ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.8290322580645161,\n \"acc_stderr\": 0.021417242936321582,\n \"\ acc_norm\": 0.8290322580645161,\n \"acc_norm_stderr\": 0.021417242936321582\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.6157635467980296,\n \"acc_stderr\": 0.034223985656575515,\n \"\ acc_norm\": 0.6157635467980296,\n \"acc_norm_stderr\": 0.034223985656575515\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|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-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\"\ : 0.8484848484848485,\n \"acc_stderr\": 0.025545650426603613,\n \"\ acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.025545650426603613\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n\ \ \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7051282051282052,\n \"acc_stderr\": 0.023119362758232304,\n\ \ \"acc_norm\": 0.7051282051282052,\n \"acc_norm_stderr\": 0.023119362758232304\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.35185185185185186,\n \"acc_stderr\": 0.02911661760608301,\n \ \ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.02911661760608301\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7605042016806722,\n \"acc_stderr\": 0.027722065493361252,\n\ \ \"acc_norm\": 0.7605042016806722,\n \"acc_norm_stderr\": 0.027722065493361252\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.5165562913907285,\n \"acc_stderr\": 0.04080244185628972,\n \"\ acc_norm\": 0.5165562913907285,\n \"acc_norm_stderr\": 0.04080244185628972\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8678899082568807,\n \"acc_stderr\": 0.014517801914598238,\n \"\ acc_norm\": 0.8678899082568807,\n \"acc_norm_stderr\": 0.014517801914598238\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6203703703703703,\n \"acc_stderr\": 0.03309682581119035,\n \"\ acc_norm\": 0.6203703703703703,\n \"acc_norm_stderr\": 0.03309682581119035\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.024509803921568606,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.024509803921568606\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8734177215189873,\n \"acc_stderr\": 0.021644195727955173,\n \ \ \"acc_norm\": 0.8734177215189873,\n \"acc_norm_stderr\": 0.021644195727955173\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7443946188340808,\n\ \ \"acc_stderr\": 0.029275891003969927,\n \"acc_norm\": 0.7443946188340808,\n\ \ \"acc_norm_stderr\": 0.029275891003969927\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8244274809160306,\n \"acc_stderr\": 0.03336820338476076,\n\ \ \"acc_norm\": 0.8244274809160306,\n \"acc_norm_stderr\": 0.03336820338476076\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035202,\n \"\ acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035202\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n\ \ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\ \ \"acc_norm_stderr\": 0.036809181416738807\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\": 0.5089285714285714,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026622,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026622\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9273504273504274,\n\ \ \"acc_stderr\": 0.017004368568132366,\n \"acc_norm\": 0.9273504273504274,\n\ \ \"acc_norm_stderr\": 0.017004368568132366\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \ \ \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932263\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8773946360153256,\n\ \ \"acc_stderr\": 0.011728672144131563,\n \"acc_norm\": 0.8773946360153256,\n\ \ \"acc_norm_stderr\": 0.011728672144131563\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.791907514450867,\n \"acc_stderr\": 0.021855255263421795,\n\ \ \"acc_norm\": 0.791907514450867,\n \"acc_norm_stderr\": 0.021855255263421795\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.37318435754189944,\n\ \ \"acc_stderr\": 0.01617569201338196,\n \"acc_norm\": 0.37318435754189944,\n\ \ \"acc_norm_stderr\": 0.01617569201338196\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.0242886194660461,\n\ \ \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.0242886194660461\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7620578778135049,\n\ \ \"acc_stderr\": 0.024185150647818707,\n \"acc_norm\": 0.7620578778135049,\n\ \ \"acc_norm_stderr\": 0.024185150647818707\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8364197530864198,\n \"acc_stderr\": 0.02058146613825715,\n\ \ \"acc_norm\": 0.8364197530864198,\n \"acc_norm_stderr\": 0.02058146613825715\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.549645390070922,\n \"acc_stderr\": 0.02968010556502904,\n \ \ \"acc_norm\": 0.549645390070922,\n \"acc_norm_stderr\": 0.02968010556502904\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5215123859191656,\n\ \ \"acc_stderr\": 0.012758410941038925,\n \"acc_norm\": 0.5215123859191656,\n\ \ \"acc_norm_stderr\": 0.012758410941038925\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7904411764705882,\n \"acc_stderr\": 0.024723110407677055,\n\ \ \"acc_norm\": 0.7904411764705882,\n \"acc_norm_stderr\": 0.024723110407677055\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.761437908496732,\n \"acc_stderr\": 0.017242385828779613,\n \ \ \"acc_norm\": 0.761437908496732,\n \"acc_norm_stderr\": 0.017242385828779613\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7795918367346939,\n \"acc_stderr\": 0.02653704531214529,\n\ \ \"acc_norm\": 0.7795918367346939,\n \"acc_norm_stderr\": 0.02653704531214529\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\ \ \"acc_stderr\": 0.024845753212306046,\n \"acc_norm\": 0.8557213930348259,\n\ \ \"acc_norm_stderr\": 0.024845753212306046\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \ \ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\ \ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\ \ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061452,\n\ \ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061452\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3268053855569155,\n\ \ \"mc1_stderr\": 0.01641987473113503,\n \"mc2\": 0.47623983056821834,\n\ \ \"mc2_stderr\": 0.01423216788461584\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8018942383583267,\n \"acc_stderr\": 0.011201862744487047\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.46853677028051555,\n \ \ \"acc_stderr\": 0.01374518994845042\n }\n}\n```" repo_url: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|arc:challenge|25_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|arc:challenge|25_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-15T14-35-04.630519.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|gsm8k|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|gsm8k|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hellaswag|10_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hellaswag|10_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T18-04-02.035270.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-15T14-35-04.630519.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-management|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-15T14-35-04.630519.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|truthfulqa:mc|0_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-15T14-35-04.630519.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T18_04_02.035270 path: - '**/details_harness|winogrande|5_2023-12-11T18-04-02.035270.parquet' - split: 2023_12_15T14_35_04.630519 path: - '**/details_harness|winogrande|5_2023-12-15T14-35-04.630519.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-15T14-35-04.630519.parquet' - config_name: results data_files: - split: 2023_12_11T18_04_02.035270 path: - results_2023-12-11T18-04-02.035270.parquet - split: 2023_12_15T14_35_04.630519 path: - results_2023-12-15T14-35-04.630519.parquet - split: latest path: - results_2023-12-15T14-35-04.630519.parquet --- # Dataset Card for Evaluation run of mistralai/Mixtral-8x7B-v0.1  Dataset automatically created during the evaluation run of model [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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_mistralai__Mixtral-8x7B-v0.1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-15T14:35:04.630519](https://huggingface.co/datasets/open-llm-leaderboard/details_mistralai__Mixtral-8x7B-v0.1/blob/main/results_2023-12-15T14-35-04.630519.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": 0.6972963513811701, "acc_stderr": 0.03075228309763406, "acc_norm": 0.7033766203346898, "acc_norm_stderr": 0.03134647583855853, "mc1": 0.3268053855569155, "mc1_stderr": 0.01641987473113503, "mc2": 0.47623983056821834, "mc2_stderr": 0.01423216788461584 }, "harness|arc:challenge|25": { "acc": 0.6109215017064846, "acc_stderr": 0.014247309976045607, "acc_norm": 0.643344709897611, "acc_norm_stderr": 0.01399805690262019 }, "harness|hellaswag|10": { "acc": 0.6548496315475005, "acc_stderr": 0.004744456628455121, "acc_norm": 0.8570005974905397, "acc_norm_stderr": 0.00349356791409329 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.674074074074074, "acc_stderr": 0.040491220417025055, "acc_norm": 0.674074074074074, "acc_norm_stderr": 0.040491220417025055 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8026315789473685, "acc_stderr": 0.03238981601699397, "acc_norm": 0.8026315789473685, "acc_norm_stderr": 0.03238981601699397 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.769811320754717, "acc_stderr": 0.025907897122408173, "acc_norm": 0.769811320754717, "acc_norm_stderr": 0.025907897122408173 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8472222222222222, "acc_stderr": 0.030085743248565666, "acc_norm": 0.8472222222222222, "acc_norm_stderr": 0.030085743248565666 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.049999999999999996, "acc_norm": 0.55, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6878612716763006, "acc_stderr": 0.03533133389323657, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.03533133389323657 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.47058823529411764, "acc_stderr": 0.049665709039785295, "acc_norm": 0.47058823529411764, "acc_norm_stderr": 0.049665709039785295 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6680851063829787, "acc_stderr": 0.030783736757745647, "acc_norm": 0.6680851063829787, "acc_norm_stderr": 0.030783736757745647 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.6228070175438597, "acc_stderr": 0.04559522141958216, 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"harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6157635467980296, "acc_stderr": 0.034223985656575515, "acc_norm": 0.6157635467980296, "acc_norm_stderr": 0.034223985656575515 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "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": 0.8484848484848485, "acc_stderr": 0.025545650426603613, "acc_norm": 0.8484848484848485, "acc_norm_stderr": 0.025545650426603613 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.917098445595855, "acc_stderr": 0.01989934131572178, "acc_norm": 0.917098445595855, "acc_norm_stderr": 0.01989934131572178 }, "harness|hendrycksTest-high_school_macroeconomics|5": { 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"harness|truthfulqa:mc|0": { "mc1": 0.3268053855569155, "mc1_stderr": 0.01641987473113503, "mc2": 0.47623983056821834, "mc2_stderr": 0.01423216788461584 }, "harness|winogrande|5": { "acc": 0.8018942383583267, "acc_stderr": 0.011201862744487047 }, "harness|gsm8k|5": { "acc": 0.46853677028051555, "acc_stderr": 0.01374518994845042 } } ``` ## 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 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[More Information Needed] ### Annotations [optional]  #### Annotation process  [More Information Needed] #### Who are the annotators?  [More Information Needed] #### Personal and Sensitive Information  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

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GroupSix/common-voice-en-sv

GroupSix

2023-12-11T20:29:46Z

7

0

[ "region:us" ]

null

2023-12-11T19:34:59Z

--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 12090945008 num_examples: 12588 - name: test num_bytes: 4937998648 num_examples: 5141 download_size: 2508578885 dataset_size: 17028943656 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

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faizalnf1800/Writing_Prompt_FAIR

faizalnf1800

2023-12-15T15:31:59Z

7

0

[ "arxiv:1805.04833", "region:us" ]

null

2023-12-12T12:04:38Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 406584542 num_examples: 3504584 - name: test num_bytes: 22558960 num_examples: 193806 - name: valid num_bytes: 12839086.633194694 num_examples: 111038 download_size: 311122593 dataset_size: 441982588.6331947 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* --- This is Dataset for this paper [Hierarchical Neural Story Generation](https://arxiv.org/pdf/1805.04833v1.pdf)

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DragonLine/ksponspeech

DragonLine

2023-12-12T16:51:10Z

7

0

[ "region:us" ]

null

2023-12-12T14:05:24Z

--- dataset_info: features: - name: audio dtype: audio: sampling_rate: 16000 - name: transcripts dtype: string splits: - name: train num_bytes: 53133867240.215996 num_examples: 299636 - name: test num_bytes: 6736967357.531417 num_examples: 37455 - name: valid num_bytes: 6484620568.886582 num_examples: 37454 download_size: 62734833313 dataset_size: 66355455166.633995 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---

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tli489/LongAlpaca-100

tli489

2023-12-12T19:50:48Z

7

0

[ "region:us" ]

null

2023-12-12T19:47:58Z

--- dataset_info: features: - name: instruction dtype: string - name: file dtype: string - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 6804562 num_examples: 100 download_size: 3626767 dataset_size: 6804562 configs: - config_name: default data_files: - split: train path: data/train-* ---

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cointegrated/dral_lite

cointegrated

2023-12-13T16:01:28Z

7

0

[ "region:us" ]

null

2023-12-13T15:57:18Z

--- dataset_info: features: - name: id dtype: string - name: conv_id dtype: string - name: original_or_reenacted dtype: string - name: time_start dtype: string - name: time_end dtype: string - name: duration dtype: string - name: trans_id dtype: string - name: wav_orig sequence: float32 - name: wav_trans sequence: float32 splits: - name: v2_short num_bytes: 347182958 num_examples: 1173 download_size: 349676470 dataset_size: 347182958 configs: - config_name: default data_files: - split: v2_short path: data/v2_short-* --- This is a copy of DRAL-2.0 dataset (https://www.cs.utep.edu/nigel/dral/), with only short fragments and only those where the audio on the left is original.

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madhaviit/NHSFT10k

madhaviit

2023-12-13T21:51:35Z

7

0

[ "region:us" ]

null

2023-12-13T21:51:32Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 14141408 num_examples: 10000 download_size: 5006544 dataset_size: 14141408 configs: - config_name: default data_files: - split: train path: data/train-* ---

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Erynan/eval_deon_100

Erynan

2023-12-13T22:03:21Z

7

0

[ "region:us" ]

null

2023-12-13T22:03:21Z

--- dataset_info: features: - name: prompt dtype: string - name: response_a dtype: string - name: response_b dtype: string - name: more_reasonable dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 27529 num_examples: 100 download_size: 16292 dataset_size: 27529 configs: - config_name: default data_files: - split: train path: data/train-* ---

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aisuko/vqa

aisuko

2023-12-14T03:46:32Z

7

0

[ "license:apache-2.0", "region:us" ]

null

2023-12-14T03:37:03Z

--- license: apache-2.0 --- # Overview The original code is from https://huggingface.co/datasets/Graphcore/vqa/tree/main Adaptered by: Aisuko # How to use it ```python from datasets import load_dataset dataset = load_dataset("aisuko/vqa", split="validation[:200]") dataset ``` ``` Dataset({ features: ['question', 'question_type', 'question_id', 'image_id', 'answer_type', 'label'], num_rows: 200 }) ``` ## Remove the label column ```python dataset = dataset.remove_columns(['question_type', 'question_id', 'answer_type']) ``` ## Check the image ```python from PIL import Image image = Image.open(dataset[0]['image_id']) image ```

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khanzaid/sentiment_review

khanzaid

2023-12-14T06:31:35Z

7

0

[ "region:us" ]

null

2023-12-14T06:31:33Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 792620 num_examples: 4846 download_size: 412816 dataset_size: 792620 configs: - config_name: default data_files: - split: train path: data/train-* ---

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Seongill/NQ_5_missing_adv_test

Seongill

2023-12-14T06:36:56Z

7

0

[ "region:us" ]

null

2023-12-14T06:36:52Z

--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: has_answer dtype: bool - name: similar_sub dtype: string - name: ctxs list: - name: answer_sent sequence: string - name: hasanswer dtype: bool - name: id dtype: string - name: is_adv dtype: bool - name: new_answer_sent dtype: string - name: original_text dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: status dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1993029 num_examples: 450 download_size: 1138217 dataset_size: 1993029 configs: - config_name: default data_files: - split: train path: data/train-* ---

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HowMannyMore/romanurdu-sentiment-dataset

HowMannyMore

2023-12-14T11:51:44Z

7

0

[ "region:us" ]

null

2023-12-14T11:51:41Z

--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 4797399 num_examples: 53869 - name: test num_bytes: 990904 num_examples: 11185 - name: valid num_bytes: 849266 num_examples: 9507 download_size: 4449642 dataset_size: 6637569 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* ---

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open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-v3

open-llm-leaderboard

2023-12-14T12:17:46Z

7

0

[ "region:us" ]

null

2023-12-14T12:17:00Z

--- pretty_name: Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-v3 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [kyujinpy/PlatYi-34B-Llama-Q-v3](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-v3)\ \ 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_kyujinpy__PlatYi-34B-Llama-Q-v3\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-14T12:14:12.357754](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-v3/blob/main/results_2023-12-14T12-14-12.357754.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\": 0.7362231043895405,\n\ \ \"acc_stderr\": 0.028525669907782977,\n \"acc_norm\": 0.7496962088802717,\n\ \ \"acc_norm_stderr\": 0.02917819886610155,\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5180149372938835,\n\ \ \"mc2_stderr\": 0.014666631083011332\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6117747440273038,\n \"acc_stderr\": 0.014241614207414046,\n\ \ \"acc_norm\": 0.643344709897611,\n \"acc_norm_stderr\": 0.013998056902620192\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6490738896634136,\n\ \ \"acc_stderr\": 0.004762844770909867,\n \"acc_norm\": 0.8488348934475204,\n\ \ \"acc_norm_stderr\": 0.0035747765941085046\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.7407407407407407,\n\ \ \"acc_stderr\": 0.03785714465066652,\n \"acc_norm\": 0.7407407407407407,\n\ \ \"acc_norm_stderr\": 0.03785714465066652\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8618421052631579,\n \"acc_stderr\": 0.028081042939576552,\n\ \ \"acc_norm\": 0.8618421052631579,\n \"acc_norm_stderr\": 0.028081042939576552\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.83,\n\ \ \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.83,\n \ \ \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7849056603773585,\n \"acc_stderr\": 0.025288394502891363,\n\ \ \"acc_norm\": 0.7849056603773585,\n \"acc_norm_stderr\": 0.025288394502891363\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8611111111111112,\n\ \ \"acc_stderr\": 0.028919802956134926,\n \"acc_norm\": 0.8611111111111112,\n\ \ \"acc_norm_stderr\": 0.028919802956134926\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \ \ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\ acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6994219653179191,\n\ \ \"acc_stderr\": 0.03496101481191179,\n \"acc_norm\": 0.6994219653179191,\n\ \ \"acc_norm_stderr\": 0.03496101481191179\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4803921568627451,\n \"acc_stderr\": 0.04971358884367406,\n\ \ \"acc_norm\": 0.4803921568627451,\n \"acc_norm_stderr\": 0.04971358884367406\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\ \ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7446808510638298,\n \"acc_stderr\": 0.028504856470514255,\n\ \ \"acc_norm\": 0.7446808510638298,\n \"acc_norm_stderr\": 0.028504856470514255\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6052631578947368,\n\ \ \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.6052631578947368,\n\ \ \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7517241379310344,\n \"acc_stderr\": 0.036001056927277716,\n\ \ \"acc_norm\": 0.7517241379310344,\n \"acc_norm_stderr\": 0.036001056927277716\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.6481481481481481,\n \"acc_stderr\": 0.024594975128920938,\n \"\ acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.024594975128920938\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.6031746031746031,\n\ \ \"acc_stderr\": 0.04375888492727059,\n \"acc_norm\": 0.6031746031746031,\n\ \ \"acc_norm_stderr\": 0.04375888492727059\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.8903225806451613,\n\ \ \"acc_stderr\": 0.01777677870048519,\n \"acc_norm\": 0.8903225806451613,\n\ \ \"acc_norm_stderr\": 0.01777677870048519\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.6108374384236454,\n \"acc_stderr\": 0.034304624161038716,\n\ \ \"acc_norm\": 0.6108374384236454,\n \"acc_norm_stderr\": 0.034304624161038716\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\"\ : 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8606060606060606,\n \"acc_stderr\": 0.027045948825865394,\n\ \ \"acc_norm\": 0.8606060606060606,\n \"acc_norm_stderr\": 0.027045948825865394\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.9040404040404041,\n \"acc_stderr\": 0.020984808610047933,\n \"\ acc_norm\": 0.9040404040404041,\n \"acc_norm_stderr\": 0.020984808610047933\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527026,\n\ \ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527026\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7794871794871795,\n \"acc_stderr\": 0.0210206726808279,\n \ \ \"acc_norm\": 0.7794871794871795,\n \"acc_norm_stderr\": 0.0210206726808279\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.4111111111111111,\n \"acc_stderr\": 0.02999992350870668,\n \ \ \"acc_norm\": 0.4111111111111111,\n \"acc_norm_stderr\": 0.02999992350870668\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.8403361344537815,\n \"acc_stderr\": 0.0237933539975288,\n \ \ \"acc_norm\": 0.8403361344537815,\n \"acc_norm_stderr\": 0.0237933539975288\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4768211920529801,\n \"acc_stderr\": 0.04078093859163083,\n \"\ acc_norm\": 0.4768211920529801,\n \"acc_norm_stderr\": 0.04078093859163083\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.9174311926605505,\n \"acc_stderr\": 0.011800361363016569,\n \"\ acc_norm\": 0.9174311926605505,\n \"acc_norm_stderr\": 0.011800361363016569\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6342592592592593,\n \"acc_stderr\": 0.032847388576472056,\n \"\ acc_norm\": 0.6342592592592593,\n \"acc_norm_stderr\": 0.032847388576472056\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"\ acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.9240506329113924,\n \"acc_stderr\": 0.017244633251065702,\n \ \ \"acc_norm\": 0.9240506329113924,\n \"acc_norm_stderr\": 0.017244633251065702\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8026905829596412,\n\ \ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\ \ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8473282442748091,\n \"acc_stderr\": 0.03154521672005471,\n\ \ \"acc_norm\": 0.8473282442748091,\n \"acc_norm_stderr\": 0.03154521672005471\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.9090909090909091,\n \"acc_stderr\": 0.02624319405407388,\n \"\ acc_norm\": 0.9090909090909091,\n \"acc_norm_stderr\": 0.02624319405407388\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8888888888888888,\n\ \ \"acc_stderr\": 0.030381596756651655,\n \"acc_norm\": 0.8888888888888888,\n\ \ \"acc_norm_stderr\": 0.030381596756651655\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8895705521472392,\n \"acc_stderr\": 0.024624937788941318,\n\ \ \"acc_norm\": 0.8895705521472392,\n \"acc_norm_stderr\": 0.024624937788941318\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6607142857142857,\n\ \ \"acc_stderr\": 0.04493949068613541,\n \"acc_norm\": 0.6607142857142857,\n\ \ \"acc_norm_stderr\": 0.04493949068613541\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8737864077669902,\n \"acc_stderr\": 0.03288180278808628,\n\ \ \"acc_norm\": 0.8737864077669902,\n \"acc_norm_stderr\": 0.03288180278808628\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9273504273504274,\n\ \ \"acc_stderr\": 0.017004368568132342,\n \"acc_norm\": 0.9273504273504274,\n\ \ \"acc_norm_stderr\": 0.017004368568132342\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.84,\n \"acc_stderr\": 0.036845294917747094,\n \ \ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.036845294917747094\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9067688378033205,\n\ \ \"acc_stderr\": 0.010397417087292847,\n \"acc_norm\": 0.9067688378033205,\n\ \ \"acc_norm_stderr\": 0.010397417087292847\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8179190751445087,\n \"acc_stderr\": 0.020776761102512982,\n\ \ \"acc_norm\": 0.8179190751445087,\n \"acc_norm_stderr\": 0.020776761102512982\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.5687150837988827,\n\ \ \"acc_stderr\": 0.016563829399047714,\n \"acc_norm\": 0.5687150837988827,\n\ \ \"acc_norm_stderr\": 0.016563829399047714\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8202614379084967,\n \"acc_stderr\": 0.02198603218206415,\n\ \ \"acc_norm\": 0.8202614379084967,\n \"acc_norm_stderr\": 0.02198603218206415\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.819935691318328,\n\ \ \"acc_stderr\": 0.02182342285774494,\n \"acc_norm\": 0.819935691318328,\n\ \ \"acc_norm_stderr\": 0.02182342285774494\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8487654320987654,\n \"acc_stderr\": 0.019935086092149886,\n\ \ \"acc_norm\": 0.8487654320987654,\n \"acc_norm_stderr\": 0.019935086092149886\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.6134751773049646,\n \"acc_stderr\": 0.029049190342543465,\n \ \ \"acc_norm\": 0.6134751773049646,\n \"acc_norm_stderr\": 0.029049190342543465\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5821382007822686,\n\ \ \"acc_stderr\": 0.012596744108998565,\n \"acc_norm\": 0.5821382007822686,\n\ \ \"acc_norm_stderr\": 0.012596744108998565\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8051470588235294,\n \"acc_stderr\": 0.02406059942348742,\n\ \ \"acc_norm\": 0.8051470588235294,\n \"acc_norm_stderr\": 0.02406059942348742\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.8071895424836601,\n \"acc_stderr\": 0.015959983971206744,\n \ \ \"acc_norm\": 0.8071895424836601,\n \"acc_norm_stderr\": 0.015959983971206744\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.8285714285714286,\n \"acc_stderr\": 0.024127463462650163,\n\ \ \"acc_norm\": 0.8285714285714286,\n \"acc_norm_stderr\": 0.024127463462650163\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8855721393034826,\n\ \ \"acc_stderr\": 0.022509345325101716,\n \"acc_norm\": 0.8855721393034826,\n\ \ \"acc_norm_stderr\": 0.022509345325101716\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\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8654970760233918,\n \"acc_stderr\": 0.026168221344662297,\n\ \ \"acc_norm\": 0.8654970760233918,\n \"acc_norm_stderr\": 0.026168221344662297\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3671970624235006,\n\ \ \"mc1_stderr\": 0.01687480500145318,\n \"mc2\": 0.5180149372938835,\n\ \ \"mc2_stderr\": 0.014666631083011332\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8421468034727704,\n \"acc_stderr\": 0.010247165248719764\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0667172100075815,\n \ \ \"acc_stderr\": 0.006873340544455133\n }\n}\n```" repo_url: https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-v3 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: [email protected] configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|arc:challenge|25_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-14T12-14-12.357754.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|gsm8k|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hellaswag|10_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-14T12-14-12.357754.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-management|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-14T12-14-12.357754.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|truthfulqa:mc|0_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-14T12-14-12.357754.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_14T12_14_12.357754 path: - '**/details_harness|winogrande|5_2023-12-14T12-14-12.357754.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-14T12-14-12.357754.parquet' - config_name: results data_files: - split: 2023_12_14T12_14_12.357754 path: - results_2023-12-14T12-14-12.357754.parquet - split: latest path: - results_2023-12-14T12-14-12.357754.parquet --- # Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-v3  Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-Llama-Q-v3](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-v3) 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_kyujinpy__PlatYi-34B-Llama-Q-v3", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-14T12:14:12.357754](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-v3/blob/main/results_2023-12-14T12-14-12.357754.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": 0.7362231043895405, "acc_stderr": 0.028525669907782977, "acc_norm": 0.7496962088802717, "acc_norm_stderr": 0.02917819886610155, "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5180149372938835, "mc2_stderr": 0.014666631083011332 }, "harness|arc:challenge|25": { "acc": 0.6117747440273038, "acc_stderr": 0.014241614207414046, "acc_norm": 0.643344709897611, "acc_norm_stderr": 0.013998056902620192 }, "harness|hellaswag|10": { "acc": 0.6490738896634136, "acc_stderr": 0.004762844770909867, "acc_norm": 0.8488348934475204, "acc_norm_stderr": 0.0035747765941085046 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7407407407407407, "acc_stderr": 0.03785714465066652, "acc_norm": 0.7407407407407407, "acc_norm_stderr": 0.03785714465066652 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8618421052631579, "acc_stderr": 0.028081042939576552, "acc_norm": 0.8618421052631579, "acc_norm_stderr": 0.028081042939576552 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.83, "acc_stderr": 0.0377525168068637, "acc_norm": 0.83, "acc_norm_stderr": 0.0377525168068637 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7849056603773585, "acc_stderr": 0.025288394502891363, "acc_norm": 0.7849056603773585, "acc_norm_stderr": 0.025288394502891363 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8611111111111112, "acc_stderr": 0.028919802956134926, "acc_norm": 0.8611111111111112, "acc_norm_stderr": 0.028919802956134926 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6994219653179191, "acc_stderr": 0.03496101481191179, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.03496101481191179 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4803921568627451, "acc_stderr": 0.04971358884367406, "acc_norm": 0.4803921568627451, "acc_norm_stderr": 0.04971358884367406 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.81, "acc_stderr": 0.039427724440366234, "acc_norm": 0.81, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7446808510638298, "acc_stderr": 0.028504856470514255, "acc_norm": 0.7446808510638298, "acc_norm_stderr": 0.028504856470514255 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.6052631578947368, "acc_stderr": 0.045981880578165414, "acc_norm": 0.6052631578947368, "acc_norm_stderr": 0.045981880578165414 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7517241379310344, "acc_stderr": 0.036001056927277716, "acc_norm": 0.7517241379310344, "acc_norm_stderr": 0.036001056927277716 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.6481481481481481, "acc_stderr": 0.024594975128920938, "acc_norm": 0.6481481481481481, "acc_norm_stderr": 0.024594975128920938 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.6031746031746031, "acc_stderr": 0.04375888492727059, "acc_norm": 0.6031746031746031, "acc_norm_stderr": 0.04375888492727059 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8903225806451613, "acc_stderr": 0.01777677870048519, "acc_norm": 0.8903225806451613, "acc_norm_stderr": 0.01777677870048519 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6108374384236454, "acc_stderr": 0.034304624161038716, "acc_norm": 0.6108374384236454, "acc_norm_stderr": 0.034304624161038716 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8606060606060606, "acc_stderr": 0.027045948825865394, "acc_norm": 0.8606060606060606, "acc_norm_stderr": 0.027045948825865394 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9040404040404041, "acc_stderr": 0.020984808610047933, "acc_norm": 0.9040404040404041, "acc_norm_stderr": 0.020984808610047933 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9689119170984456, "acc_stderr": 0.012525310625527026, "acc_norm": 0.9689119170984456, "acc_norm_stderr": 0.012525310625527026 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7794871794871795, "acc_stderr": 0.0210206726808279, "acc_norm": 0.7794871794871795, "acc_norm_stderr": 0.0210206726808279 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.4111111111111111, "acc_stderr": 0.02999992350870668, "acc_norm": 0.4111111111111111, "acc_norm_stderr": 0.02999992350870668 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8403361344537815, "acc_stderr": 0.0237933539975288, "acc_norm": 0.8403361344537815, "acc_norm_stderr": 0.0237933539975288 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4768211920529801, "acc_stderr": 0.04078093859163083, "acc_norm": 0.4768211920529801, "acc_norm_stderr": 0.04078093859163083 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.9174311926605505, "acc_stderr": 0.011800361363016569, "acc_norm": 0.9174311926605505, "acc_norm_stderr": 0.011800361363016569 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6342592592592593, "acc_stderr": 0.032847388576472056, "acc_norm": 0.6342592592592593, "acc_norm_stderr": 0.032847388576472056 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9215686274509803, "acc_stderr": 0.018869514646658928, "acc_norm": 0.9215686274509803, "acc_norm_stderr": 0.018869514646658928 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9240506329113924, "acc_stderr": 0.017244633251065702, "acc_norm": 0.9240506329113924, "acc_norm_stderr": 0.017244633251065702 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.8026905829596412, "acc_stderr": 0.02670985334496796, "acc_norm": 0.8026905829596412, "acc_norm_stderr": 0.02670985334496796 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8473282442748091, "acc_stderr": 0.03154521672005471, "acc_norm": 0.8473282442748091, "acc_norm_stderr": 0.03154521672005471 }, "harness|hendrycksTest-international_law|5": { "acc": 0.9090909090909091, "acc_stderr": 0.02624319405407388, "acc_norm": 0.9090909090909091, "acc_norm_stderr": 0.02624319405407388 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8888888888888888, "acc_stderr": 0.030381596756651655, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.030381596756651655 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8895705521472392, "acc_stderr": 0.024624937788941318, "acc_norm": 0.8895705521472392, "acc_norm_stderr": 0.024624937788941318 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6607142857142857, "acc_stderr": 0.04493949068613541, "acc_norm": 0.6607142857142857, "acc_norm_stderr": 0.04493949068613541 }, "harness|hendrycksTest-management|5": { "acc": 0.8737864077669902, "acc_stderr": 0.03288180278808628, "acc_norm": 0.8737864077669902, "acc_norm_stderr": 0.03288180278808628 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9273504273504274, "acc_stderr": 0.017004368568132342, "acc_norm": 0.9273504273504274, "acc_norm_stderr": 0.017004368568132342 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.84, "acc_stderr": 0.036845294917747094, "acc_norm": 0.84, "acc_norm_stderr": 0.036845294917747094 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9067688378033205, "acc_stderr": 0.010397417087292847, "acc_norm": 0.9067688378033205, "acc_norm_stderr": 0.010397417087292847 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8179190751445087, "acc_stderr": 0.020776761102512982, "acc_norm": 0.8179190751445087, "acc_norm_stderr": 0.020776761102512982 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.5687150837988827, "acc_stderr": 0.016563829399047714, "acc_norm": 0.5687150837988827, "acc_norm_stderr": 0.016563829399047714 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8202614379084967, "acc_stderr": 0.02198603218206415, "acc_norm": 0.8202614379084967, "acc_norm_stderr": 0.02198603218206415 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.819935691318328, "acc_stderr": 0.02182342285774494, "acc_norm": 0.819935691318328, "acc_norm_stderr": 0.02182342285774494 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8487654320987654, "acc_stderr": 0.019935086092149886, "acc_norm": 0.8487654320987654, "acc_norm_stderr": 0.019935086092149886 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.6134751773049646, "acc_stderr": 0.029049190342543465, "acc_norm": 0.6134751773049646, "acc_norm_stderr": 0.029049190342543465 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5821382007822686, "acc_stderr": 0.012596744108998565, "acc_norm": 0.5821382007822686, "acc_norm_stderr": 0.012596744108998565 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8051470588235294, "acc_stderr": 0.02406059942348742, "acc_norm": 0.8051470588235294, "acc_norm_stderr": 0.02406059942348742 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8071895424836601, "acc_stderr": 0.015959983971206744, "acc_norm": 0.8071895424836601, "acc_norm_stderr": 0.015959983971206744 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8285714285714286, "acc_stderr": 0.024127463462650163, "acc_norm": 0.8285714285714286, "acc_norm_stderr": 0.024127463462650163 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8855721393034826, "acc_stderr": 0.022509345325101716, "acc_norm": 0.8855721393034826, "acc_norm_stderr": 0.022509345325101716 }, "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": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8654970760233918, "acc_stderr": 0.026168221344662297, "acc_norm": 0.8654970760233918, "acc_norm_stderr": 0.026168221344662297 }, "harness|truthfulqa:mc|0": { "mc1": 0.3671970624235006, "mc1_stderr": 0.01687480500145318, "mc2": 0.5180149372938835, "mc2_stderr": 0.014666631083011332 }, "harness|winogrande|5": { "acc": 0.8421468034727704, "acc_stderr": 0.010247165248719764 }, "harness|gsm8k|5": { "acc": 0.0667172100075815, "acc_stderr": 0.006873340544455133 } } ``` ## 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]

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BlahBlah314/AcolyteExP

BlahBlah314

2023-12-14T16:22:23Z

7

0

[ "region:us" ]

null

2023-12-14T16:22:22Z

--- dataset_info: features: - name: instruction dtype: string - name: enhanced dtype: string - name: prescription dtype: string splits: - name: train num_bytes: 419197 num_examples: 191 download_size: 47308 dataset_size: 419197 configs: - config_name: default data_files: - split: train path: data/train-* ---

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beratcmn/turkish-poems-cleaned

beratcmn

2023-12-14T20:13:06Z

7

0

[ "license:apache-2.0", "region:us" ]

null

2023-12-14T19:05:44Z

--- license: apache-2.0 --- HTML tags removed and overall cleaned version of okg/turkish-poems. Original: https://huggingface.co/datasets/okg/turkish-poems Data için teşekkür ederim okg <3

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nlplabtdtu/edu_child_v2

nlplabtdtu

2023-12-15T08:01:29Z

7

0

[ "region:us" ]

null

2023-12-15T08:01:25Z

--- dataset_info: features: - name: content dtype: string - name: metadata struct: - name: metadata struct: - name: id dtype: string - name: seq_num dtype: int64 - name: source dtype: string - name: vector sequence: float64 splits: - name: train num_bytes: 3914344 num_examples: 302 download_size: 3072056 dataset_size: 3914344 configs: - config_name: default data_files: - split: train path: data/train-* ---

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LabHC/Bias_in_Bios_stratify

LabHC

2023-12-15T09:37:49Z

7

0

[ "region:us" ]

null

2023-12-15T09:30:43Z

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: gender dtype: int64 splits: - name: train num_bytes: 41703222 num_examples: 100000 - name: test num_bytes: 848564 num_examples: 2000 - name: dev num_bytes: 836655 num_examples: 2000 download_size: 26189142 dataset_size: 43388441 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: dev path: data/dev-* ---

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jlbaker361/wikiart-balanced25

jlbaker361

2023-12-15T17:06:52Z

7

0

[ "region:us" ]

null

2023-12-15T17:06:50Z

--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: style dtype: string - name: name dtype: string - name: gen_style dtype: string splits: - name: train num_bytes: 23078706.68 num_examples: 607 - name: test num_bytes: 2987451.32 num_examples: 68 download_size: 26031378 dataset_size: 26066158.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

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syp1229/Y_total_freq

syp1229

2023-12-15T18:34:31Z

7

0

[ "region:us" ]

null

2023-12-15T18:22:41Z

--- dataset_info: features: - name: audio struct: - name: array sequence: float64 - name: path dtype: string - name: sample_rate dtype: int64 - name: text dtype: string - name: scriptId dtype: int64 - name: fileNm dtype: string - name: recrdTime dtype: float64 - name: recrdQuality dtype: int64 - name: recrdDt dtype: string - name: scriptSetNo dtype: string - name: recrdEnvrn dtype: string - name: colctUnitCode dtype: string - name: cityCode dtype: string - name: recrdUnit dtype: string - name: convrsThema dtype: string - name: gender dtype: string - name: recorderId dtype: string - name: age dtype: int64 splits: - name: train num_bytes: 9454815261 num_examples: 12401 - name: test num_bytes: 504186814 num_examples: 605 download_size: 6285334123 dataset_size: 9959002075 --- # Dataset Card for "Y_total_freq" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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vivekdugale/llama2_mental_health_dataset_172

vivekdugale

2023-12-16T06:04:32Z

7

0

[ "region:us" ]

null

2023-12-16T06:03:27Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 189265 num_examples: 172 download_size: 102246 dataset_size: 189265 configs: - config_name: default data_files: - split: train path: data/train-* ---

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nam194/codesum_java_512_128

nam194

2023-12-16T09:05:51Z

7

0

[ "region:us" ]

null

2023-12-16T08:42:37Z

--- dataset_info: features: - name: hexsha dtype: string - name: repo dtype: string - name: path dtype: string - name: license sequence: string - name: language dtype: string - name: identifier dtype: string - name: return_type dtype: string - name: original_string dtype: string - name: original_docstring dtype: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: code dtype: string - name: code_tokens sequence: string - name: short_docstring dtype: string - name: short_docstring_tokens sequence: string - name: comment sequence: string - name: parameters list: - name: param dtype: string - name: type dtype: string - name: docstring_params struct: - name: returns list: - name: docstring dtype: string - name: docstring_tokens sequence: string - name: type dtype: string - name: raises list: - name: docstring dtype: string - name: docstring_tokens sequence: string - name: type dtype: string - name: params list: - name: identifier dtype: string - name: type dtype: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: default dtype: string - name: is_optional dtype: bool - name: outlier_params list: - name: identifier dtype: string - name: type dtype: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: default dtype: string - name: is_optional dtype: bool - name: others list: - name: identifier dtype: string - name: docstring dtype: string - name: docstring_tokens sequence: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 49797821738 num_examples: 6629193 download_size: 9683784722 dataset_size: 49797821738 configs: - config_name: default data_files: - split: train path: data/train-* ---

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ticoAg/cotinus-poem

ticoAg

2023-12-16T13:57:41Z

7

0

[ "license:apache-2.0", "region:us" ]

null

2023-12-16T13:47:43Z

--- license: apache-2.0 ---

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anurag2op/pcam-augmented-train-test-val-50k

anurag2op

2023-12-16T13:55:24Z

7

0

[ "region:us" ]

null

2023-12-16T13:51:24Z

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': non_tumor '1': tumor splits: - name: train num_bytes: 630998760.0 num_examples: 30000 - name: validation num_bytes: 210044410.0 num_examples: 10000 - name: test num_bytes: 211024140.0 num_examples: 10000 download_size: 1050350091 dataset_size: 1052067310.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

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RikoteMaster/emotion_recog_sample

RikoteMaster

2023-12-17T18:18:18Z

7

0

[ "region:us" ]

null

2023-12-17T18:18:16Z

--- dataset_info: features: - name: Text_processed dtype: string - name: Emotion dtype: string - name: Augmented dtype: bool - name: text dtype: string splits: - name: train num_bytes: 35573 num_examples: 63 download_size: 18616 dataset_size: 35573 configs: - config_name: default data_files: - split: train path: data/train-* ---

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Rootreck/so-vits-svc-4.0-ru-The_Witcher_3_Wild_Hunt

Rootreck

2023-12-15T22:40:36Z

6

0

[ "language:ru", "region:us" ]

null

2023-08-01T07:17:28Z

--- language: - ru --- Это тренировочные данные моделей голосов персонажей из "Ведьмак 3: Дикая охота" для so-vits-svc-4.1.1

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TinyPixel/lima

TinyPixel

2023-12-15T05:20:55Z

6

0

[ "region:us" ]

null

2023-10-02T06:24:35Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2904960 num_examples: 1030 download_size: 1695740 dataset_size: 2904960 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "lima" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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laion/tripple-ocr

laion

2023-12-15T16:54:09Z

6

8

[ "source_datasets:wendlerc/RenderedText", "source_datasets:wendlerc/CaptionedSynthText", "source_datasets:AtlasUnified/atlas-pdf-img-cluster", "license:cc0-1.0", "region:us" ]

null

2023-10-24T12:16:51Z

--- license: cc0-1.0 dataset_info: features: - name: key dtype: string - name: image dtype: image - name: ocr_annotation_text sequence: string - name: ocr_annotation_bb_relative sequence: sequence: sequence: float64 - name: caption dtype: string - name: source_tar dtype: string splits: - name: train num_examples: 20 source_datasets: - wendlerc/RenderedText - wendlerc/CaptionedSynthText - AtlasUnified/atlas-pdf-img-cluster configs: - config_name: default data_files: - split: train path: data/*.parquet ---

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MohamedRashad/multilingual-tts

MohamedRashad

2023-12-12T21:04:06Z

6

1

[ "task_categories:text-to-speech", "size_categories:10K<n<100K", "language:ar", "language:en", "language:zh", "language:es", "language:fr", "language:hi", "language:ru", "language:pt", "language:ja", "language:de", "language:tr", "language:bn", "language:id", "language:ur", "language:vi", "license:gpl-3.0", "region:us" ]

[ "text-to-speech" ]

2023-12-01T23:46:06Z

--- license: gpl-3.0 dataset_info: features: - name: text dtype: string - name: speaker dtype: string - name: languages dtype: string - name: audio dtype: audio splits: - name: train num_bytes: 1561588634.72 num_examples: 25540 download_size: 1548036818 dataset_size: 1561588634.72 task_categories: - text-to-speech language: - ar - en - zh - es - fr - hi - ru - pt - ja - de - tr - bn - id - ur - vi pretty_name: Multilingual TTS size_categories: - 10K<n<100K --- # Before Anything and Everything ⚱ _In the time of writing this Dataset Card, ~**17,490**~ **18,412** civilian has been killed in Palestine (~**7,870**~ **8,000** are children and ~**6,121**~ **6,200** are women)._ **Se**ek **a**ny **n**on-**pro**fit **organi**zation **t**o **he**lp **th**em **wi**th **wh**at **y**ou **c**an (For myself, [I use Mersal](https://www.every.org/mersal/f/support-humanitarian)) 🇵🇸 ## Dataset Description The Multilingual TTS dataset is an exceptional compilation of text-to-speech (TTS) samples, meticulously crafted to showcase the richness and diversity of human languages. This dataset encompasses a variety of real-world sentences in fifteen prominent languages, carefully chosen to reflect global linguistic diversity. Each sample is accompanied by its corresponding high-quality audio output. <style> .image-container { display: flex; justify-content: center; align-items: center; height: 65vh; margin: 0; } .image-container img { max-width: 48%; /* Adjust the width as needed */ height: auto; } </style> <div class="image-container"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6116d0584ef9fdfbf45dc4d9/UX0s8S2yWSJ3NbbvmOJOi.png"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6116d0584ef9fdfbf45dc4d9/zIyPCWH7Y58gLVCeIfq4n.png"> </div> ## Key Features: 1. **Language Diversity**: The dataset covers a spectrum of languages, including **Beng**ali, **Mand**arin **Chin**ese, **Turk**ish, **Hin**di, **Fre**nch, **Vietn**amese, **Portu**guese, **Span**ish, **Japa**nese, **Ger**man, **Russ**ian, **Indon**esian, **Stan**dard **Ara**bic, **Engl**ish, **a**nd **Ur**du. This wide linguistic representation ensures inclusivity and applicability to a global audience. 3. **Real-World Sentences**: Comprising 25,000 samples, the dataset mirrors authentic communication scenarios. Sentences span diverse topics, ranging from everyday conversations to informative texts and news snippets, providing a comprehensive linguistic landscape. 4. **Multilingual Sentences**: A distinctive feature of this dataset is its inclusion of sentences that seamlessly integrate multiple languages. Each sample combines at least two languages, capturing the intricate dynamics of multilingual communication and rendering the dataset particularly valuable for training and evaluating multilingual TTS systems. 5. **Audio Quality**: Special attention has been given to the audio quality of each sample. The audio outputs are meticulously designed to be clear, natural-sounding, and faithful representations of the corresponding text, ensuring a rich auditory experience. 6. **Generated by GPT-4 and elevenlabs**: The dataset is the result of a collaboration between GPT-4 and elevenlabs, combining cutting-edge language generation capabilities with domain expertise. This collaboration guarantees a high level of accuracy, coherence, and linguistic nuance in both the text and audio components. ## Potential Use Cases: 1. **Multilingual TTS Model Training**: Researchers and developers can leverage this dataset to train and refine multilingual TTS models, enhancing their proficiency across a diverse array of languages. 2. **Cross-Language Evaluation**: The dataset serves as a valuable resource for evaluating TTS systems in handling multilingual scenarios, offering a benchmark for assessing model capabilities across different languages. 3. **Language Integration Testing**: Developers working on applications requiring multilingual TTS functionality can utilize this dataset to test and optimize language integration, ensuring a seamless user experience across various linguistic contexts. ## Acknowledgments: The creation of the Multilingual TTS dataset was made possible through the collaborative efforts of **OpenAI's GPT-4** and the expertise of **Elevenlabs Multilingual V2**. We extend our gratitude to the AI and language processing communities for their continuous support in advancing the field of multilingual TTS. This dataset stands as a significant contribution, fostering innovation and progress in language technologies.

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jlbaker361/wikiart

jlbaker361

2023-12-15T14:29:19Z

6

0

[ "region:us" ]

null

2023-12-08T02:32:30Z

--- dataset_info: features: - name: image dtype: image - name: text dtype: string - name: style dtype: string - name: name dtype: string - name: gen_style dtype: string splits: - name: train num_bytes: 2702920462.8247585 num_examples: 73304 - name: test num_bytes: 295758759.14224136 num_examples: 8145 download_size: 2970796741 dataset_size: 2998679221.967 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

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kanishka/counterfactual_babylm_aann_indef_naan

kanishka

2023-12-13T01:24:56Z

6

0

[ "region:us" ]

null

2023-12-11T22:28:37Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 581833803 num_examples: 11632617 - name: validation num_bytes: 56120230 num_examples: 1026747 download_size: 0 dataset_size: 637954033 --- # Dataset Card for "counterfactual_babylm_aann_indef_naan" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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pankajemplay/mistral-intent-data-1816

pankajemplay

2023-12-12T02:26:38Z

6

0

[ "region:us" ]

null

2023-12-12T02:26:35Z

--- dataset_info: features: - name: User Query dtype: string - name: Intent dtype: string - name: id type dtype: string - name: id value dtype: string - name: id slot filled dtype: bool - name: Task dtype: string - name: task slot filled dtype: bool - name: Bot Response dtype: string - name: text dtype: string - name: instruction dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1155635 num_examples: 1816 download_size: 210883 dataset_size: 1155635 configs: - config_name: default data_files: - split: train path: data/train-* ---

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ouvic215/Soldering-Data-pix2pix-1209-white-2

ouvic215

2023-12-12T07:44:45Z

6

0

[ "region:us" ]

null

2023-12-12T07:40:53Z

--- dataset_info: features: - name: mask_image dtype: image - name: text dtype: string - name: image dtype: image splits: - name: train num_bytes: 2087059454.5 num_examples: 25950 download_size: 1724200255 dataset_size: 2087059454.5 --- # Dataset Card for "Soldering-Data-pix2pix-1209-white-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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ArasAyen/pc512

ArasAyen

2023-12-12T09:27:56Z

6

0

[ "region:us" ]

null

2023-12-12T09:27:40Z

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 124752550.0 num_examples: 302 download_size: 124617017 dataset_size: 124752550.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "pc512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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kristmh/high_vs_random

kristmh

2023-12-12T10:50:24Z

6

0

[ "region:us" ]

null

2023-12-12T10:49:18Z

--- configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validate path: data/validate-* dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: text_clean dtype: string - name: labels dtype: int64 - name: __index_level_0__ dtype: int64 splits: - name: test num_bytes: 35090618 num_examples: 42053 - name: train num_bytes: 284101969 num_examples: 336420 - name: validate num_bytes: 36222999 num_examples: 42052 download_size: 175883924 dataset_size: 355415586 --- # Dataset Card for "high_vs_random" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

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shyzii/exrcise-llama2-converted

shyzii

2023-12-12T11:19:26Z

6

0

[ "region:us" ]

null

2023-12-12T11:07:12Z

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 7877 num_examples: 48 download_size: 3063 dataset_size: 7877 configs: - config_name: default data_files: - split: train path: data/train-* ---

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mlsa-iai-msu-lab/ru_sci_bench

mlsa-iai-msu-lab

2023-12-17T23:23:47Z

6

0

[ "size_categories:100K<n<1M", "language:ru", "language:en", "license:mit", "region:us" ]

null

2023-12-12T15:43:37Z

--- license: mit dataset_info: features: - name: paper_id dtype: int64 - name: title dtype: string - name: abstract dtype: string splits: - name: en num_bytes: 170801402 num_examples: 182436 - name: ru num_bytes: 312579538 num_examples: 194071 download_size: 246316956 dataset_size: 483380940 configs: - config_name: default data_files: - split: en path: data/en-* - split: ru path: data/ru-* language: - ru - en pretty_name: ruSciBench size_categories: - 100K<n<1M --- # ruSciBench text data Titles and abstracts of research papers in english and russian for ruSciBench benchmark. [Habr post](https://habr.com/ru/articles/781032/) [Benchmark code](https://github.com/mlsa-iai-msu-lab/ru-sci-bench) [Google Colab example](https://colab.research.google.com/drive/1S04UOLRYuI2J5qM2QkoWqSbv6GO94YrP?usp=sharing) ### Authors Benchmark developed by MLSA Lab of Institute for AI, MSU. ### Acknowledgement The research is part of the project #23-Ш05-21 SES MSU "Development of mathematical methods of machine learning for processing large-volume textual scientific information". We would like to thank [eLibrary](https://elibrary.ru/) for provided datasets. ### Contacts Nikolai Gerasimenko ([email protected]), Alexey Vatolin ([email protected])

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Seongill/NQ_5_adversary_v2

Seongill

2023-12-13T06:34:34Z

6

0

[ "region:us" ]

null

2023-12-13T06:34:29Z

--- dataset_info: features: - name: question dtype: string - name: answers sequence: string - name: ctxs list: - name: answer_sent sequence: string - name: hasanswer dtype: bool - name: id dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: has_answer dtype: bool - name: random_sub dtype: string - name: similar_sub dtype: string - name: ent_type dtype: string - name: new_ctxs list: - name: answer_sent sequence: string - name: hasanswer dtype: bool - name: id dtype: string - name: is_adv dtype: bool - name: new_answer_sent dtype: string - name: original_text dtype: string - name: score dtype: float64 - name: text dtype: string - name: title dtype: string - name: num_advs dtype: int64 - name: num_ctxs dtype: int64 splits: - name: train num_bytes: 27255069 num_examples: 3610 download_size: 15221336 dataset_size: 27255069 configs: - config_name: default data_files: - split: train path: data/train-* ---

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MMoin/mini-platypus

MMoin

2023-12-14T11:27:27Z

6

0

[ "region:us" ]

null

2023-12-13T11:25:14Z

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 4186564 num_examples: 1000 download_size: 2245925 dataset_size: 4186564 configs: - config_name: default data_files: - split: train path: data/train-* ---

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