datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
open-llm-leaderboard/details_cookinai__CatMacaroni-Slerp | ---
pretty_name: Evaluation run of cookinai/CatMacaroni-Slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [cookinai/CatMacaroni-Slerp](https://huggingface.co/cookinai/CatMacaroni-Slerp)\
\ 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_cookinai__CatMacaroni-Slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-20T21:17:23.139479](https://huggingface.co/datasets/open-llm-leaderboard/details_cookinai__CatMacaroni-Slerp/blob/main/results_2023-12-20T21-17-23.139479.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\": 0.6549634202662564,\n\
\ \"acc_stderr\": 0.032063571652802186,\n \"acc_norm\": 0.6546746443892243,\n\
\ \"acc_norm_stderr\": 0.032730217428552345,\n \"mc1\": 0.4418604651162791,\n\
\ \"mc1_stderr\": 0.017384767478986218,\n \"mc2\": 0.6102215759974746,\n\
\ \"mc2_stderr\": 0.015132806306597834\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6663822525597269,\n \"acc_stderr\": 0.013778687054176541,\n\
\ \"acc_norm\": 0.6928327645051194,\n \"acc_norm_stderr\": 0.013481034054980941\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6799442342162916,\n\
\ \"acc_stderr\": 0.00465544276659947,\n \"acc_norm\": 0.8687512447719578,\n\
\ \"acc_norm_stderr\": 0.0033698210047622503\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\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.041539484047423976,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.041539484047423976\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\
\ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n\
\ \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \
\ \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7245283018867924,\n \"acc_stderr\": 0.027495663683724057,\n\
\ \"acc_norm\": 0.7245283018867924,\n \"acc_norm_stderr\": 0.027495663683724057\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\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\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\
\ \"acc_stderr\": 0.035676037996391706,\n \"acc_norm\": 0.6763005780346821,\n\
\ \"acc_norm_stderr\": 0.035676037996391706\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\
\ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n\
\ \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146267,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146267\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4312169312169312,\n \"acc_stderr\": 0.025506481698138215,\n \"\
acc_norm\": 0.4312169312169312,\n \"acc_norm_stderr\": 0.025506481698138215\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7741935483870968,\n\
\ \"acc_stderr\": 0.023785577884181015,\n \"acc_norm\": 0.7741935483870968,\n\
\ \"acc_norm_stderr\": 0.023785577884181015\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\
\ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267045,\n \"\
acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267045\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \
\ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37777777777777777,\n \"acc_stderr\": 0.029560707392465725,\n \
\ \"acc_norm\": 0.37777777777777777,\n \"acc_norm_stderr\": 0.029560707392465725\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886793,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886793\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374303,\n \"\
acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374303\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5462962962962963,\n \"acc_stderr\": 0.03395322726375797,\n \"\
acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.03395322726375797\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944863,\n \
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944863\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.03749492448709695,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.03749492448709695\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.03957835471980981,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.03957835471980981\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\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\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\
\ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.021586494001281376,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.021586494001281376\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8339719029374202,\n\
\ \"acc_stderr\": 0.0133064782430663,\n \"acc_norm\": 0.8339719029374202,\n\
\ \"acc_norm_stderr\": 0.0133064782430663\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069363,\n\
\ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069363\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.40782122905027934,\n\
\ \"acc_stderr\": 0.016435865260914746,\n \"acc_norm\": 0.40782122905027934,\n\
\ \"acc_norm_stderr\": 0.016435865260914746\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7091503267973857,\n \"acc_stderr\": 0.02600480036395213,\n\
\ \"acc_norm\": 0.7091503267973857,\n \"acc_norm_stderr\": 0.02600480036395213\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\
\ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\
\ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\
: 0.4716312056737589,\n \"acc_stderr\": 0.029779450957303062,\n \"\
acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.029779450957303062\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4680573663624511,\n\
\ \"acc_stderr\": 0.012744149704869649,\n \"acc_norm\": 0.4680573663624511,\n\
\ \"acc_norm_stderr\": 0.012744149704869649\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.027778298701545443,\n\
\ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.027778298701545443\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6862745098039216,\n \"acc_stderr\": 0.018771683893528183,\n \
\ \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.018771683893528183\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n\
\ \"acc_stderr\": 0.024484487162913973,\n \"acc_norm\": 0.8606965174129353,\n\
\ \"acc_norm_stderr\": 0.024484487162913973\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4418604651162791,\n\
\ \"mc1_stderr\": 0.017384767478986218,\n \"mc2\": 0.6102215759974746,\n\
\ \"mc2_stderr\": 0.015132806306597834\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8113654301499605,\n \"acc_stderr\": 0.01099517231801981\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7308567096285065,\n \
\ \"acc_stderr\": 0.01221659545729273\n }\n}\n```"
repo_url: https://huggingface.co/cookinai/CatMacaroni-Slerp
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|arc:challenge|25_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|gsm8k|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hellaswag|10_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-20T21-17-23.139479.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-20T21-17-23.139479.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- '**/details_harness|winogrande|5_2023-12-20T21-17-23.139479.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-20T21-17-23.139479.parquet'
- config_name: results
data_files:
- split: 2023_12_20T21_17_23.139479
path:
- results_2023-12-20T21-17-23.139479.parquet
- split: latest
path:
- results_2023-12-20T21-17-23.139479.parquet
---
# Dataset Card for Evaluation run of cookinai/CatMacaroni-Slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [cookinai/CatMacaroni-Slerp](https://huggingface.co/cookinai/CatMacaroni-Slerp) 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_cookinai__CatMacaroni-Slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-20T21:17:23.139479](https://huggingface.co/datasets/open-llm-leaderboard/details_cookinai__CatMacaroni-Slerp/blob/main/results_2023-12-20T21-17-23.139479.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": 0.6549634202662564,
"acc_stderr": 0.032063571652802186,
"acc_norm": 0.6546746443892243,
"acc_norm_stderr": 0.032730217428552345,
"mc1": 0.4418604651162791,
"mc1_stderr": 0.017384767478986218,
"mc2": 0.6102215759974746,
"mc2_stderr": 0.015132806306597834
},
"harness|arc:challenge|25": {
"acc": 0.6663822525597269,
"acc_stderr": 0.013778687054176541,
"acc_norm": 0.6928327645051194,
"acc_norm_stderr": 0.013481034054980941
},
"harness|hellaswag|10": {
"acc": 0.6799442342162916,
"acc_stderr": 0.00465544276659947,
"acc_norm": 0.8687512447719578,
"acc_norm_stderr": 0.0033698210047622503
},
"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": 0.6370370370370371,
"acc_stderr": 0.041539484047423976,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.041539484047423976
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6907894736842105,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.6907894736842105,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7245283018867924,
"acc_stderr": 0.027495663683724057,
"acc_norm": 0.7245283018867924,
"acc_norm_stderr": 0.027495663683724057
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"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": 0.33,
"acc_stderr": 0.04725815626252604,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252604
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.035676037996391706,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.035676037996391706
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.049406356306056595,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.049406356306056595
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5787234042553191,
"acc_stderr": 0.03227834510146267,
"acc_norm": 0.5787234042553191,
"acc_norm_stderr": 0.03227834510146267
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878152,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878152
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4312169312169312,
"acc_stderr": 0.025506481698138215,
"acc_norm": 0.4312169312169312,
"acc_norm_stderr": 0.025506481698138215
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.04463112720677172,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677172
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7741935483870968,
"acc_stderr": 0.023785577884181015,
"acc_norm": 0.7741935483870968,
"acc_norm_stderr": 0.023785577884181015
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4827586206896552,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.4827586206896552,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7818181818181819,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.7818181818181819,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7929292929292929,
"acc_stderr": 0.028869778460267045,
"acc_norm": 0.7929292929292929,
"acc_norm_stderr": 0.028869778460267045
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.021500249576033456,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.021500249576033456
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6743589743589744,
"acc_stderr": 0.02375966576741229,
"acc_norm": 0.6743589743589744,
"acc_norm_stderr": 0.02375966576741229
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.37777777777777777,
"acc_stderr": 0.029560707392465725,
"acc_norm": 0.37777777777777777,
"acc_norm_stderr": 0.029560707392465725
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.030388353551886793,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.030388353551886793
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
"acc_stderr": 0.03861557546255169,
"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.03861557546255169
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8458715596330275,
"acc_stderr": 0.015480826865374303,
"acc_norm": 0.8458715596330275,
"acc_norm_stderr": 0.015480826865374303
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5462962962962963,
"acc_stderr": 0.03395322726375797,
"acc_norm": 0.5462962962962963,
"acc_norm_stderr": 0.03395322726375797
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931045,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931045
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8143459915611815,
"acc_stderr": 0.025310495376944863,
"acc_norm": 0.8143459915611815,
"acc_norm_stderr": 0.025310495376944863
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7786259541984732,
"acc_stderr": 0.03641297081313729,
"acc_norm": 0.7786259541984732,
"acc_norm_stderr": 0.03641297081313729
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.03749492448709695,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.03749492448709695
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7870370370370371,
"acc_stderr": 0.03957835471980981,
"acc_norm": 0.7870370370370371,
"acc_norm_stderr": 0.03957835471980981
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
"acc_stderr": 0.03291099578615769,
"acc_norm": 0.7730061349693251,
"acc_norm_stderr": 0.03291099578615769
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.7961165048543689,
"acc_stderr": 0.039891398595317706,
"acc_norm": 0.7961165048543689,
"acc_norm_stderr": 0.039891398595317706
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8760683760683761,
"acc_stderr": 0.021586494001281376,
"acc_norm": 0.8760683760683761,
"acc_norm_stderr": 0.021586494001281376
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8339719029374202,
"acc_stderr": 0.0133064782430663,
"acc_norm": 0.8339719029374202,
"acc_norm_stderr": 0.0133064782430663
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7398843930635838,
"acc_stderr": 0.023618678310069363,
"acc_norm": 0.7398843930635838,
"acc_norm_stderr": 0.023618678310069363
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.40782122905027934,
"acc_stderr": 0.016435865260914746,
"acc_norm": 0.40782122905027934,
"acc_norm_stderr": 0.016435865260914746
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7091503267973857,
"acc_stderr": 0.02600480036395213,
"acc_norm": 0.7091503267973857,
"acc_norm_stderr": 0.02600480036395213
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7106109324758842,
"acc_stderr": 0.025755865922632945,
"acc_norm": 0.7106109324758842,
"acc_norm_stderr": 0.025755865922632945
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.75,
"acc_stderr": 0.02409347123262133,
"acc_norm": 0.75,
"acc_norm_stderr": 0.02409347123262133
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4716312056737589,
"acc_stderr": 0.029779450957303062,
"acc_norm": 0.4716312056737589,
"acc_norm_stderr": 0.029779450957303062
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4680573663624511,
"acc_stderr": 0.012744149704869649,
"acc_norm": 0.4680573663624511,
"acc_norm_stderr": 0.012744149704869649
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7022058823529411,
"acc_stderr": 0.027778298701545443,
"acc_norm": 0.7022058823529411,
"acc_norm_stderr": 0.027778298701545443
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6862745098039216,
"acc_stderr": 0.018771683893528183,
"acc_norm": 0.6862745098039216,
"acc_norm_stderr": 0.018771683893528183
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
"acc_stderr": 0.04461272175910509,
"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.04461272175910509
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7224489795918367,
"acc_stderr": 0.028666857790274648,
"acc_norm": 0.7224489795918367,
"acc_norm_stderr": 0.028666857790274648
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8606965174129353,
"acc_stderr": 0.024484487162913973,
"acc_norm": 0.8606965174129353,
"acc_norm_stderr": 0.024484487162913973
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.0358870281282637,
"acc_norm": 0.85,
"acc_norm_stderr": 0.0358870281282637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
"acc_stderr": 0.03869543323472101,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4418604651162791,
"mc1_stderr": 0.017384767478986218,
"mc2": 0.6102215759974746,
"mc2_stderr": 0.015132806306597834
},
"harness|winogrande|5": {
"acc": 0.8113654301499605,
"acc_stderr": 0.01099517231801981
},
"harness|gsm8k|5": {
"acc": 0.7308567096285065,
"acc_stderr": 0.01221659545729273
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
dannyvas23/notas_suicidios | ---
license: afl-3.0
---
|
autoevaluate/autoeval-staging-eval-project-squad_v2-94d8b010-11595542 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: autoevaluate/extractive-question-answering
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: autoevaluate/extractive-question-answering
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
Francesco/chess-pieces-mjzgj | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': chess-pieces
'1': bishop
'2': black-bishop
'3': black-king
'4': black-knight
'5': black-pawn
'6': black-queen
'7': black-rook
'8': white-bishop
'9': white-king
'10': white-knight
'11': white-pawn
'12': white-queen
'13': white-rook
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: chess-pieces-mjzgj
tags:
- rf100
---
# Dataset Card for chess-pieces-mjzgj
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/chess-pieces-mjzgj
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
chess-pieces-mjzgj
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/chess-pieces-mjzgj
### Citation Information
```
@misc{ chess-pieces-mjzgj,
title = { chess pieces mjzgj Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/chess-pieces-mjzgj } },
url = { https://universe.roboflow.com/object-detection/chess-pieces-mjzgj },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
zoohun/medical-data-one | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5526229
num_examples: 7037
download_size: 1296786
dataset_size: 5526229
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Multimodal-Fatima/VQAv2_test_1 | ---
dataset_info:
features:
- name: question_type
dtype: string
- name: multiple_choice_answer
dtype: string
- name: answers
sequence: string
- name: answers_original
list:
- name: answer
dtype: string
- name: answer_confidence
dtype: string
- name: answer_id
dtype: int64
- name: id_image
dtype: int64
- name: answer_type
dtype: string
- name: question_id
dtype: int64
- name: question
dtype: string
- name: image
dtype: image
- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: blip_caption
dtype: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14
sequence: string
- name: DETA_detections_deta_swin_large_o365_coco_classes
list:
- name: attribute
dtype: string
- name: box
sequence: float32
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float32
- name: size
dtype: string
- name: tag
dtype: string
- name: Attributes_ViT_L_14_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_ViT_L_14_wo_openai
sequence: string
- name: clip_tags_ViT_L_14_with_openai
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_wo_openai
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_with_openai
sequence: string
- name: clip_tags_LAION_ViT_bigG_14_2B_wo_openai
sequence: string
- name: clip_tags_LAION_ViT_bigG_14_2B_with_openai
sequence: string
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full
sequence: string
splits:
- name: test
num_bytes: 14281937464.0
num_examples: 89559
download_size: 2695014507
dataset_size: 14281937464.0
---
# Dataset Card for "VQAv2_test_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AhmedBou/NCSS_2023_Data_Analysis | ---
license: apache-2.0
task_categories:
- token-classification
- text-generation
language:
- en
size_categories:
- n<1K
--- |
macarious/sv_corpora_parliament_processed | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 292351437
num_examples: 1892723
download_size: 0
dataset_size: 292351437
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "sv_corpora_parliament_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Nekochu__Luminia-13B-v3 | ---
pretty_name: Evaluation run of Nekochu/Luminia-13B-v3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Nekochu/Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-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_Nekochu__Luminia-13B-v3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-22T01:43:27.205787](https://huggingface.co/datasets/open-llm-leaderboard/details_Nekochu__Luminia-13B-v3/blob/main/results_2024-03-22T01-43-27.205787.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\": 0.5305067520715956,\n\
\ \"acc_stderr\": 0.0339720901123489,\n \"acc_norm\": 0.5396395753713893,\n\
\ \"acc_norm_stderr\": 0.03480536249301555,\n \"mc1\": 0.2802937576499388,\n\
\ \"mc1_stderr\": 0.015723139524608767,\n \"mc2\": 0.4373610781301898,\n\
\ \"mc2_stderr\": 0.014893320137130312\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4761092150170648,\n \"acc_stderr\": 0.014594701798071654,\n\
\ \"acc_norm\": 0.5247440273037542,\n \"acc_norm_stderr\": 0.014593487694937736\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5630352519418442,\n\
\ \"acc_stderr\": 0.004949969363017663,\n \"acc_norm\": 0.7608046205935073,\n\
\ \"acc_norm_stderr\": 0.00425720418339642\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411022,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411022\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5185185185185185,\n\
\ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.5185185185185185,\n\
\ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5460526315789473,\n \"acc_stderr\": 0.04051646342874142,\n\
\ \"acc_norm\": 0.5460526315789473,\n \"acc_norm_stderr\": 0.04051646342874142\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.53,\n\
\ \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5509433962264151,\n \"acc_stderr\": 0.0306127307136411,\n\
\ \"acc_norm\": 0.5509433962264151,\n \"acc_norm_stderr\": 0.0306127307136411\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5347222222222222,\n\
\ \"acc_stderr\": 0.04171115858181618,\n \"acc_norm\": 0.5347222222222222,\n\
\ \"acc_norm_stderr\": 0.04171115858181618\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\"\
: 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4682080924855491,\n\
\ \"acc_stderr\": 0.03804749744364763,\n \"acc_norm\": 0.4682080924855491,\n\
\ \"acc_norm_stderr\": 0.03804749744364763\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n\
\ \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.4297872340425532,\n \"acc_stderr\": 0.03236214467715564,\n\
\ \"acc_norm\": 0.4297872340425532,\n \"acc_norm_stderr\": 0.03236214467715564\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n\
\ \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n\
\ \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.4482758620689655,\n \"acc_stderr\": 0.04144311810878151,\n\
\ \"acc_norm\": 0.4482758620689655,\n \"acc_norm_stderr\": 0.04144311810878151\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3306878306878307,\n \"acc_stderr\": 0.02422996529842508,\n \"\
acc_norm\": 0.3306878306878307,\n \"acc_norm_stderr\": 0.02422996529842508\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.35714285714285715,\n\
\ \"acc_stderr\": 0.04285714285714281,\n \"acc_norm\": 0.35714285714285715,\n\
\ \"acc_norm_stderr\": 0.04285714285714281\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5451612903225806,\n\
\ \"acc_stderr\": 0.02832774309156107,\n \"acc_norm\": 0.5451612903225806,\n\
\ \"acc_norm_stderr\": 0.02832774309156107\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3842364532019704,\n \"acc_stderr\": 0.0342239856565755,\n\
\ \"acc_norm\": 0.3842364532019704,\n \"acc_norm_stderr\": 0.0342239856565755\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\
: 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n\
\ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.03501438706296781\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6666666666666666,\n \"acc_stderr\": 0.033586181457325226,\n \"\
acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.033586181457325226\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7823834196891192,\n \"acc_stderr\": 0.02977866303775296,\n\
\ \"acc_norm\": 0.7823834196891192,\n \"acc_norm_stderr\": 0.02977866303775296\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5307692307692308,\n \"acc_stderr\": 0.025302958890850154,\n\
\ \"acc_norm\": 0.5307692307692308,\n \"acc_norm_stderr\": 0.025302958890850154\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815635,\n \
\ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815635\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5336134453781513,\n \"acc_stderr\": 0.03240501447690071,\n \
\ \"acc_norm\": 0.5336134453781513,\n \"acc_norm_stderr\": 0.03240501447690071\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"\
acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7321100917431193,\n \"acc_stderr\": 0.01898746225797865,\n \"\
acc_norm\": 0.7321100917431193,\n \"acc_norm_stderr\": 0.01898746225797865\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4537037037037037,\n \"acc_stderr\": 0.03395322726375797,\n \"\
acc_norm\": 0.4537037037037037,\n \"acc_norm_stderr\": 0.03395322726375797\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7794117647058824,\n \"acc_stderr\": 0.029102254389674082,\n \"\
acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.029102254389674082\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.759493670886076,\n \"acc_stderr\": 0.027820781981149685,\n \
\ \"acc_norm\": 0.759493670886076,\n \"acc_norm_stderr\": 0.027820781981149685\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6143497757847534,\n\
\ \"acc_stderr\": 0.03266842214289202,\n \"acc_norm\": 0.6143497757847534,\n\
\ \"acc_norm_stderr\": 0.03266842214289202\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5877862595419847,\n \"acc_stderr\": 0.04317171194870254,\n\
\ \"acc_norm\": 0.5877862595419847,\n \"acc_norm_stderr\": 0.04317171194870254\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7107438016528925,\n \"acc_stderr\": 0.041391127276354626,\n \"\
acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.041391127276354626\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.04557239513497752,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.04557239513497752\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6319018404907976,\n \"acc_stderr\": 0.03789213935838396,\n\
\ \"acc_norm\": 0.6319018404907976,\n \"acc_norm_stderr\": 0.03789213935838396\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3392857142857143,\n\
\ \"acc_stderr\": 0.04493949068613539,\n \"acc_norm\": 0.3392857142857143,\n\
\ \"acc_norm_stderr\": 0.04493949068613539\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.0465614711001235,\n\
\ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.0465614711001235\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7905982905982906,\n\
\ \"acc_stderr\": 0.026655699653922737,\n \"acc_norm\": 0.7905982905982906,\n\
\ \"acc_norm_stderr\": 0.026655699653922737\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.6998722860791826,\n\
\ \"acc_stderr\": 0.01638924969131744,\n \"acc_norm\": 0.6998722860791826,\n\
\ \"acc_norm_stderr\": 0.01638924969131744\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6329479768786127,\n \"acc_stderr\": 0.025950054337654075,\n\
\ \"acc_norm\": 0.6329479768786127,\n \"acc_norm_stderr\": 0.025950054337654075\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.38324022346368714,\n\
\ \"acc_stderr\": 0.016260159604429125,\n \"acc_norm\": 0.38324022346368714,\n\
\ \"acc_norm_stderr\": 0.016260159604429125\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5392156862745098,\n \"acc_stderr\": 0.028541722692618874,\n\
\ \"acc_norm\": 0.5392156862745098,\n \"acc_norm_stderr\": 0.028541722692618874\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5884244372990354,\n\
\ \"acc_stderr\": 0.027950481494401273,\n \"acc_norm\": 0.5884244372990354,\n\
\ \"acc_norm_stderr\": 0.027950481494401273\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5895061728395061,\n \"acc_stderr\": 0.027371350925124764,\n\
\ \"acc_norm\": 0.5895061728395061,\n \"acc_norm_stderr\": 0.027371350925124764\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666907,\n \
\ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666907\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4276401564537158,\n\
\ \"acc_stderr\": 0.012635799922765844,\n \"acc_norm\": 0.4276401564537158,\n\
\ \"acc_norm_stderr\": 0.012635799922765844\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4375,\n \"acc_stderr\": 0.030134614954403924,\n \
\ \"acc_norm\": 0.4375,\n \"acc_norm_stderr\": 0.030134614954403924\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5326797385620915,\n \"acc_stderr\": 0.020184583359102202,\n \
\ \"acc_norm\": 0.5326797385620915,\n \"acc_norm_stderr\": 0.020184583359102202\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6090909090909091,\n\
\ \"acc_stderr\": 0.04673752333670239,\n \"acc_norm\": 0.6090909090909091,\n\
\ \"acc_norm_stderr\": 0.04673752333670239\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6081632653061224,\n \"acc_stderr\": 0.031251275910891656,\n\
\ \"acc_norm\": 0.6081632653061224,\n \"acc_norm_stderr\": 0.031251275910891656\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7064676616915423,\n\
\ \"acc_stderr\": 0.03220024104534204,\n \"acc_norm\": 0.7064676616915423,\n\
\ \"acc_norm_stderr\": 0.03220024104534204\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.040201512610368466,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.040201512610368466\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3855421686746988,\n\
\ \"acc_stderr\": 0.03789134424611551,\n \"acc_norm\": 0.3855421686746988,\n\
\ \"acc_norm_stderr\": 0.03789134424611551\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7192982456140351,\n \"acc_stderr\": 0.034462962170884265,\n\
\ \"acc_norm\": 0.7192982456140351,\n \"acc_norm_stderr\": 0.034462962170884265\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2802937576499388,\n\
\ \"mc1_stderr\": 0.015723139524608767,\n \"mc2\": 0.4373610781301898,\n\
\ \"mc2_stderr\": 0.014893320137130312\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7261247040252565,\n \"acc_stderr\": 0.012533292732620297\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04245640636846096,\n \
\ \"acc_stderr\": 0.005553837749990045\n }\n}\n```"
repo_url: https://huggingface.co/Nekochu/Luminia-13B-v3
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|arc:challenge|25_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|gsm8k|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hellaswag|10_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-22T01-43-27.205787.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-22T01-43-27.205787.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- '**/details_harness|winogrande|5_2024-03-22T01-43-27.205787.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-22T01-43-27.205787.parquet'
- config_name: results
data_files:
- split: 2024_03_22T01_43_27.205787
path:
- results_2024-03-22T01-43-27.205787.parquet
- split: latest
path:
- results_2024-03-22T01-43-27.205787.parquet
---
# Dataset Card for Evaluation run of Nekochu/Luminia-13B-v3
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Nekochu/Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-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_Nekochu__Luminia-13B-v3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-22T01:43:27.205787](https://huggingface.co/datasets/open-llm-leaderboard/details_Nekochu__Luminia-13B-v3/blob/main/results_2024-03-22T01-43-27.205787.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": 0.5305067520715956,
"acc_stderr": 0.0339720901123489,
"acc_norm": 0.5396395753713893,
"acc_norm_stderr": 0.03480536249301555,
"mc1": 0.2802937576499388,
"mc1_stderr": 0.015723139524608767,
"mc2": 0.4373610781301898,
"mc2_stderr": 0.014893320137130312
},
"harness|arc:challenge|25": {
"acc": 0.4761092150170648,
"acc_stderr": 0.014594701798071654,
"acc_norm": 0.5247440273037542,
"acc_norm_stderr": 0.014593487694937736
},
"harness|hellaswag|10": {
"acc": 0.5630352519418442,
"acc_stderr": 0.004949969363017663,
"acc_norm": 0.7608046205935073,
"acc_norm_stderr": 0.00425720418339642
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.04793724854411022,
"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411022
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.043163785995113245,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.043163785995113245
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5460526315789473,
"acc_stderr": 0.04051646342874142,
"acc_norm": 0.5460526315789473,
"acc_norm_stderr": 0.04051646342874142
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5509433962264151,
"acc_stderr": 0.0306127307136411,
"acc_norm": 0.5509433962264151,
"acc_norm_stderr": 0.0306127307136411
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5347222222222222,
"acc_stderr": 0.04171115858181618,
"acc_norm": 0.5347222222222222,
"acc_norm_stderr": 0.04171115858181618
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.4682080924855491,
"acc_stderr": 0.03804749744364763,
"acc_norm": 0.4682080924855491,
"acc_norm_stderr": 0.03804749744364763
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.28431372549019607,
"acc_stderr": 0.04488482852329017,
"acc_norm": 0.28431372549019607,
"acc_norm_stderr": 0.04488482852329017
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252609,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252609
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4297872340425532,
"acc_stderr": 0.03236214467715564,
"acc_norm": 0.4297872340425532,
"acc_norm_stderr": 0.03236214467715564
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2894736842105263,
"acc_stderr": 0.04266339443159394,
"acc_norm": 0.2894736842105263,
"acc_norm_stderr": 0.04266339443159394
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4482758620689655,
"acc_stderr": 0.04144311810878151,
"acc_norm": 0.4482758620689655,
"acc_norm_stderr": 0.04144311810878151
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3306878306878307,
"acc_stderr": 0.02422996529842508,
"acc_norm": 0.3306878306878307,
"acc_norm_stderr": 0.02422996529842508
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.35714285714285715,
"acc_stderr": 0.04285714285714281,
"acc_norm": 0.35714285714285715,
"acc_norm_stderr": 0.04285714285714281
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5451612903225806,
"acc_stderr": 0.02832774309156107,
"acc_norm": 0.5451612903225806,
"acc_norm_stderr": 0.02832774309156107
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3842364532019704,
"acc_stderr": 0.0342239856565755,
"acc_norm": 0.3842364532019704,
"acc_norm_stderr": 0.0342239856565755
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7212121212121212,
"acc_stderr": 0.03501438706296781,
"acc_norm": 0.7212121212121212,
"acc_norm_stderr": 0.03501438706296781
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.033586181457325226,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.033586181457325226
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7823834196891192,
"acc_stderr": 0.02977866303775296,
"acc_norm": 0.7823834196891192,
"acc_norm_stderr": 0.02977866303775296
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5307692307692308,
"acc_stderr": 0.025302958890850154,
"acc_norm": 0.5307692307692308,
"acc_norm_stderr": 0.025302958890850154
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3074074074074074,
"acc_stderr": 0.028133252578815635,
"acc_norm": 0.3074074074074074,
"acc_norm_stderr": 0.028133252578815635
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5336134453781513,
"acc_stderr": 0.03240501447690071,
"acc_norm": 0.5336134453781513,
"acc_norm_stderr": 0.03240501447690071
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31125827814569534,
"acc_stderr": 0.03780445850526733,
"acc_norm": 0.31125827814569534,
"acc_norm_stderr": 0.03780445850526733
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7321100917431193,
"acc_stderr": 0.01898746225797865,
"acc_norm": 0.7321100917431193,
"acc_norm_stderr": 0.01898746225797865
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4537037037037037,
"acc_stderr": 0.03395322726375797,
"acc_norm": 0.4537037037037037,
"acc_norm_stderr": 0.03395322726375797
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7794117647058824,
"acc_stderr": 0.029102254389674082,
"acc_norm": 0.7794117647058824,
"acc_norm_stderr": 0.029102254389674082
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.759493670886076,
"acc_stderr": 0.027820781981149685,
"acc_norm": 0.759493670886076,
"acc_norm_stderr": 0.027820781981149685
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6143497757847534,
"acc_stderr": 0.03266842214289202,
"acc_norm": 0.6143497757847534,
"acc_norm_stderr": 0.03266842214289202
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.5877862595419847,
"acc_stderr": 0.04317171194870254,
"acc_norm": 0.5877862595419847,
"acc_norm_stderr": 0.04317171194870254
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7107438016528925,
"acc_stderr": 0.041391127276354626,
"acc_norm": 0.7107438016528925,
"acc_norm_stderr": 0.041391127276354626
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.04557239513497752,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.04557239513497752
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6319018404907976,
"acc_stderr": 0.03789213935838396,
"acc_norm": 0.6319018404907976,
"acc_norm_stderr": 0.03789213935838396
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.3392857142857143,
"acc_stderr": 0.04493949068613539,
"acc_norm": 0.3392857142857143,
"acc_norm_stderr": 0.04493949068613539
},
"harness|hendrycksTest-management|5": {
"acc": 0.6699029126213593,
"acc_stderr": 0.0465614711001235,
"acc_norm": 0.6699029126213593,
"acc_norm_stderr": 0.0465614711001235
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7905982905982906,
"acc_stderr": 0.026655699653922737,
"acc_norm": 0.7905982905982906,
"acc_norm_stderr": 0.026655699653922737
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6998722860791826,
"acc_stderr": 0.01638924969131744,
"acc_norm": 0.6998722860791826,
"acc_norm_stderr": 0.01638924969131744
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6329479768786127,
"acc_stderr": 0.025950054337654075,
"acc_norm": 0.6329479768786127,
"acc_norm_stderr": 0.025950054337654075
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.38324022346368714,
"acc_stderr": 0.016260159604429125,
"acc_norm": 0.38324022346368714,
"acc_norm_stderr": 0.016260159604429125
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.5392156862745098,
"acc_stderr": 0.028541722692618874,
"acc_norm": 0.5392156862745098,
"acc_norm_stderr": 0.028541722692618874
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5884244372990354,
"acc_stderr": 0.027950481494401273,
"acc_norm": 0.5884244372990354,
"acc_norm_stderr": 0.027950481494401273
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5895061728395061,
"acc_stderr": 0.027371350925124764,
"acc_norm": 0.5895061728395061,
"acc_norm_stderr": 0.027371350925124764
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.44680851063829785,
"acc_stderr": 0.029658235097666907,
"acc_norm": 0.44680851063829785,
"acc_norm_stderr": 0.029658235097666907
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4276401564537158,
"acc_stderr": 0.012635799922765844,
"acc_norm": 0.4276401564537158,
"acc_norm_stderr": 0.012635799922765844
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4375,
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"acc_norm": 0.4375,
"acc_norm_stderr": 0.030134614954403924
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5326797385620915,
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"acc_norm": 0.5326797385620915,
"acc_norm_stderr": 0.020184583359102202
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6090909090909091,
"acc_stderr": 0.04673752333670239,
"acc_norm": 0.6090909090909091,
"acc_norm_stderr": 0.04673752333670239
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6081632653061224,
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"acc_norm": 0.6081632653061224,
"acc_norm_stderr": 0.031251275910891656
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7064676616915423,
"acc_stderr": 0.03220024104534204,
"acc_norm": 0.7064676616915423,
"acc_norm_stderr": 0.03220024104534204
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.8,
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"acc_norm": 0.8,
"acc_norm_stderr": 0.040201512610368466
},
"harness|hendrycksTest-virology|5": {
"acc": 0.3855421686746988,
"acc_stderr": 0.03789134424611551,
"acc_norm": 0.3855421686746988,
"acc_norm_stderr": 0.03789134424611551
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7192982456140351,
"acc_stderr": 0.034462962170884265,
"acc_norm": 0.7192982456140351,
"acc_norm_stderr": 0.034462962170884265
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2802937576499388,
"mc1_stderr": 0.015723139524608767,
"mc2": 0.4373610781301898,
"mc2_stderr": 0.014893320137130312
},
"harness|winogrande|5": {
"acc": 0.7261247040252565,
"acc_stderr": 0.012533292732620297
},
"harness|gsm8k|5": {
"acc": 0.04245640636846096,
"acc_stderr": 0.005553837749990045
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
RahulRaman/counting-object-sd-dataset3-clean3 | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 53928990.0
num_examples: 570
download_size: 11139357
dataset_size: 53928990.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
316usman/thematic4d-rr | ---
dataset_info:
features:
- name: text
dtype: string
- name: country
dtype: string
- name: document_url
dtype: string
- name: source_url
dtype: string
- name: num_tokens
dtype: int64
splits:
- name: train
num_bytes: 305997651.49274594
num_examples: 471168
download_size: 112361778
dataset_size: 305997651.49274594
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
yzhuang/metatree_pollen | ---
dataset_info:
features:
- name: id
dtype: int64
- name: X
sequence: float64
- name: y
dtype: int64
splits:
- name: train
num_bytes: 161340
num_examples: 2689
- name: validation
num_bytes: 69540
num_examples: 1159
download_size: 177984
dataset_size: 230880
---
# Dataset Card for "metatree_pollen"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TahmidH/annotated_news_summary | ---
license: cc0-1.0
task_categories:
- summarization
language:
- bn
size_categories:
- 10K<n<100K
---
This dataset is created for instruction tuning purpose.It is based on the [News Summarization](https://huggingface.co/datasets/sustcsenlp/bn_news_summarization) dataset.
The instructions are given in the `inputs` column and their completions/answers are provided in the `targets` column. The `template_id` tracks each input_template-target_template pair. There are 15 template ids (from 1 to 15).
The ID and their respective templates are given below. `no_template` indicates that no template was used and only the summary or direct answer was provided for that input.
| ID | inputs_template | targets_template |
| ----- | ----- | ----- |
| 1 | এই সংবাদের জন্য একটি সংবাদ শিরোনাম লেখ: | প্রদত্ত সংবাদের সংবাদ শিরোনাম হলো, |
| 2 | একটি বাক্যে লেখাটির মূল বক্তব্য তুলে ধর: | প্রদত্ত অনুচ্ছেদের সংক্ষিপ্ত মূলভাব হলো, |
| 3 | নিচের অনুচ্ছেদে কী বলা হয়েছে তা সংক্ষেপে বর্ণনা কর। | প্রদত্ত অনুচ্ছেদের সংক্ষিপ্ত মূলভাব হলো, |
| 4 | নিচের অনুচ্ছেদে কী বলা হয়েছে তা সংক্ষেপে বর্ণনা কর। | no_template |
| 5 | এক বাক্যে নিচের অনুচ্ছেদের সারাংশ লেখ। | প্রদত্ত অনুচ্ছেদের সংক্ষিপ্ত মূলভাব হলো, |
| 6 | সংক্ষেপে বাক্যটির মূলভাব তুলে ধরো: | no_template |
| 7 | সংবাদ শিরোনাম লিখুন: | সংবাদটির শিরোনাম হলো, |
| 8 | সংক্ষেপে বাক্যটির মূলভাব তুলে ধরো: | বাক্যটির সংক্ষিপ্ত মূলভাব হলো, |
| 9 | নিন্মলিখিত সংবাদের শিরোনাম কী হতে পারে? | প্রদত্ত সংবাদের সংবাদ শিরোনাম হলো, |
| 10 | এক বাক্যে নিচের অনুচ্ছেদের সারাংশ লেখ। | no_template |
| 11 | আরো কম শব্দে বাক্যটির মূলভাব বর্ণনা কর: | no_template |
| 12 | প্রদত্ত তথ্য ব্যবহার করে একটি সংবাদ শিরোনাম লিখুন: | সংবাদটির শিরোনাম হলো,
| 13 | আরো কম শব্দে বাক্যটির মূলভাব বর্ণনা কর: | বাক্যটির সংক্ষিপ্ত মূলভাব হলো, |
| 14 | একটি বাক্যে লেখাটির মূল বক্তব্য তুলে ধর: | no_template |
| 15 | নিম্নলিখিত সংবাদের ভিত্তিতে একটি সংবাদ শিরোনাম লিখুন | প্রদত্ত সংবাদের সংবাদ শিরোনাম হলো, | |
filipecosta90/dbpedia-openai-1M-text-embedding-3-large-3072d | ---
language:
- en
dataset_info:
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: embedding
sequence: float64
splits:
- name: train
num_bytes: 24967850773
num_examples: 1000000
download_size: 24854966022
dataset_size: 24967850773
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
xixixi/test_db_sd | ---
license: openrail
---
|
open-llm-leaderboard/details_PulsarAI__MetaMath-Chupacabra-7B-v2.01-Slerp | ---
pretty_name: Evaluation run of PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp](https://huggingface.co/PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp)\
\ 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_PulsarAI__MetaMath-Chupacabra-7B-v2.01-Slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-09T17:58:17.272756](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__MetaMath-Chupacabra-7B-v2.01-Slerp/blob/main/results_2023-12-09T17-58-17.272756.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\": 0.6430394737674227,\n\
\ \"acc_stderr\": 0.03225098588955544,\n \"acc_norm\": 0.643238473261251,\n\
\ \"acc_norm_stderr\": 0.03291299264153459,\n \"mc1\": 0.39412484700122397,\n\
\ \"mc1_stderr\": 0.017106588140700322,\n \"mc2\": 0.5614591813728808,\n\
\ \"mc2_stderr\": 0.015408154626799953\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6271331058020477,\n \"acc_stderr\": 0.014131176760131169,\n\
\ \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.013830568927974332\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6669986058554073,\n\
\ \"acc_stderr\": 0.004703238534045804,\n \"acc_norm\": 0.8546106353316073,\n\
\ \"acc_norm_stderr\": 0.0035177257870177433\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322663,\n\
\ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322663\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\"\
: 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n\
\ \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.036430371689585475,\n\
\ \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.036430371689585475\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n\
\ \"acc_stderr\": 0.04897104952726366,\n \"acc_norm\": 0.4117647058823529,\n\
\ \"acc_norm_stderr\": 0.04897104952726366\n },\n \"harness|hendrycksTest-computer_security|5\"\
: {\n \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \
\ \"acc_norm\": 0.78,\n \"acc_norm_stderr\": 0.04163331998932261\n \
\ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5829787234042553,\n\
\ \"acc_stderr\": 0.03223276266711712,\n \"acc_norm\": 0.5829787234042553,\n\
\ \"acc_norm_stderr\": 0.03223276266711712\n },\n \"harness|hendrycksTest-econometrics|5\"\
: {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n\
\ \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n\
\ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\
: 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"\
acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778408,\n \"\
acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778408\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\
\ \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.40476190476190477,\n\
\ \"acc_norm_stderr\": 0.04390259265377562\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7677419354838709,\n\
\ \"acc_stderr\": 0.024022256130308235,\n \"acc_norm\": 0.7677419354838709,\n\
\ \"acc_norm_stderr\": 0.024022256130308235\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n\
\ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586818,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586818\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919446,\n\
\ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919446\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635477,\n\
\ \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635477\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3333333333333333,\n \"acc_stderr\": 0.02874204090394848,\n \
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.02874204090394848\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.02983796238829194,\n \
\ \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.02983796238829194\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8440366972477065,\n \"acc_stderr\": 0.01555580271359017,\n \"\
acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.01555580271359017\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5509259259259259,\n \"acc_stderr\": 0.03392238405321617,\n \"\
acc_norm\": 0.5509259259259259,\n \"acc_norm_stderr\": 0.03392238405321617\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.803921568627451,\n \"acc_stderr\": 0.027865942286639325,\n \"\
acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639325\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601446,\n \
\ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601446\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\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\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n\
\ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.03755265865037182,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.03755265865037182\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\": 0.5,\n\
\ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.02158649400128138,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.02158649400128138\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\": 0.8199233716475096,\n\
\ \"acc_stderr\": 0.013740797258579828,\n \"acc_norm\": 0.8199233716475096,\n\
\ \"acc_norm_stderr\": 0.013740797258579828\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.023948512905468365,\n\
\ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.023948512905468365\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.41787709497206704,\n\
\ \"acc_stderr\": 0.016495400635820084,\n \"acc_norm\": 0.41787709497206704,\n\
\ \"acc_norm_stderr\": 0.016495400635820084\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.025829163272757485,\n\
\ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.025829163272757485\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n\
\ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45045632333767927,\n\
\ \"acc_stderr\": 0.012707390438502346,\n \"acc_norm\": 0.45045632333767927,\n\
\ \"acc_norm_stderr\": 0.012707390438502346\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6544117647058824,\n \"acc_stderr\": 0.028888193103988633,\n\
\ \"acc_norm\": 0.6544117647058824,\n \"acc_norm_stderr\": 0.028888193103988633\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \
\ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784603,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784603\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.025870646766169143,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.025870646766169143\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.39412484700122397,\n\
\ \"mc1_stderr\": 0.017106588140700322,\n \"mc2\": 0.5614591813728808,\n\
\ \"mc2_stderr\": 0.015408154626799953\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7947908445146015,\n \"acc_stderr\": 0.01135031570746206\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7012888551933283,\n \
\ \"acc_stderr\": 0.012607137125693625\n }\n}\n```"
repo_url: https://huggingface.co/PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|arc:challenge|25_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|gsm8k|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hellaswag|10_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T17-58-17.272756.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-09T17-58-17.272756.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- '**/details_harness|winogrande|5_2023-12-09T17-58-17.272756.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-09T17-58-17.272756.parquet'
- config_name: results
data_files:
- split: 2023_12_09T17_58_17.272756
path:
- results_2023-12-09T17-58-17.272756.parquet
- split: latest
path:
- results_2023-12-09T17-58-17.272756.parquet
---
# Dataset Card for Evaluation run of PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp](https://huggingface.co/PulsarAI/MetaMath-Chupacabra-7B-v2.01-Slerp) 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_PulsarAI__MetaMath-Chupacabra-7B-v2.01-Slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-09T17:58:17.272756](https://huggingface.co/datasets/open-llm-leaderboard/details_PulsarAI__MetaMath-Chupacabra-7B-v2.01-Slerp/blob/main/results_2023-12-09T17-58-17.272756.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": 0.6430394737674227,
"acc_stderr": 0.03225098588955544,
"acc_norm": 0.643238473261251,
"acc_norm_stderr": 0.03291299264153459,
"mc1": 0.39412484700122397,
"mc1_stderr": 0.017106588140700322,
"mc2": 0.5614591813728808,
"mc2_stderr": 0.015408154626799953
},
"harness|arc:challenge|25": {
"acc": 0.6271331058020477,
"acc_stderr": 0.014131176760131169,
"acc_norm": 0.6612627986348123,
"acc_norm_stderr": 0.013830568927974332
},
"harness|hellaswag|10": {
"acc": 0.6669986058554073,
"acc_stderr": 0.004703238534045804,
"acc_norm": 0.8546106353316073,
"acc_norm_stderr": 0.0035177257870177433
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.26,
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"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"harness|hendrycksTest-astronomy|5": {
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},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.6,
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},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_norm": 0.6943396226415094,
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},
"harness|hendrycksTest-college_biology|5": {
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},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.45,
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},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
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},
"harness|hendrycksTest-college_medicine|5": {
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},
"harness|hendrycksTest-computer_security|5": {
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"acc_norm": 0.78,
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},
"harness|hendrycksTest-conceptual_physics|5": {
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},
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},
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"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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},
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6974789915966386,
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},
"harness|hendrycksTest-high_school_physics|5": {
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},
"harness|hendrycksTest-high_school_psychology|5": {
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"acc_norm_stderr": 0.01555580271359017
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5509259259259259,
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"harness|hendrycksTest-high_school_us_history|5": {
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},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7974683544303798,
"acc_stderr": 0.026160568246601446,
"acc_norm": 0.7974683544303798,
"acc_norm_stderr": 0.026160568246601446
},
"harness|hendrycksTest-human_aging|5": {
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"harness|hendrycksTest-human_sexuality|5": {
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"harness|hendrycksTest-international_law|5": {
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},
"harness|hendrycksTest-logical_fallacies|5": {
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},
"harness|hendrycksTest-machine_learning|5": {
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},
"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-marketing|5": {
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},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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"harness|hendrycksTest-nutrition|5": {
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},
"harness|hendrycksTest-prehistory|5": {
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},
"harness|hendrycksTest-security_studies|5": {
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},
"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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},
"harness|hendrycksTest-world_religions|5": {
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},
"harness|truthfulqa:mc|0": {
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"mc2": 0.5614591813728808,
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},
"harness|winogrande|5": {
"acc": 0.7947908445146015,
"acc_stderr": 0.01135031570746206
},
"harness|gsm8k|5": {
"acc": 0.7012888551933283,
"acc_stderr": 0.012607137125693625
}
}
```
### 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] |
sankovic/shirimxz | ---
license: openrail
---
|
Finnish-NLP/distilabel-intel-orca-dpo-pairs-fi-deepl-translated-sft-dpo | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response_accepted
dtype: string
- name: response_rejected
dtype: string
- name: instruction_orig
dtype: string
- name: response_accepted_orig
dtype: string
- name: response_rejected_orig
dtype: string
- name: instruction_len
dtype: int64
- name: response_acc_len
dtype: int64
- name: response_rej_len
dtype: int64
- name: response_orig_grade
dtype: string
- name: response_judgelm
dtype: string
splits:
- name: train
num_bytes: 31842478
num_examples: 6680
download_size: 18427432
dataset_size: 31842478
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
README TO DO BUT RELEASED NEVERTHELESS |
Piyush2512/CREMA-mel-spectrogram-images-preprocessed | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Anger
'1': Happy
'2': Fear
'3': Sad
'4': Disgust
'5': Neutral
- name: pixel_values
sequence:
sequence:
sequence: float32
splits:
- name: train
num_bytes: 4875082092.75
num_examples: 7442
download_size: 993636094
dataset_size: 4875082092.75
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Mykil/mfm | ---
task_categories:
- automatic-speech-recognition
dataset_info:
features:
- name: CHANNEL_NAME
dtype: string
- name: URL
dtype: string
- name: TITLE
dtype: string
- name: DESCRIPTION
dtype: string
- name: TRANSCRIPTION
dtype: string
- name: SEGMENTS
dtype: string
splits:
- name: train
num_bytes: 229019
num_examples: 1
download_size: 135260
dataset_size: 229019
tags:
- whisper
- whispering
- base
---
# Dataset Card for "mfm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nexdata/English-Russian_Parallel_Corpus_Data | ---
task_categories:
- translation
language:
- ru
- en
---
# Dataset Card for Nexdata/English-Russian_Parallel_Corpus_Data
## Description
English and Russian parallel corpus, 1,080,000 groups in total; excluded political, porn, personal information and other sensitive vocabulary; it can be a base corpus for text-based data analysis, used in machine translation and other fields.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1161?source=Huggingface
# Specifications
## Storage format
TXT
## Data content
English-Russian Parallel Corpus Data
## Data size
1.08 million pairs of English-Russian Parallel Corpus Data
## Language
English,Russian
## Application scenario
machine translation
# Licensing Information
Commercial License |
arpitsh018/ecf7a5069fc2c571562bffc574d45c82 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 536820664.7423957
num_examples: 11829
download_size: 105940966
dataset_size: 536820664.7423957
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
enoahjr/twitter_dataset_1713199583 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 972604
num_examples: 2969
download_size: 488904
dataset_size: 972604
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/hinata_bluearchive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of hinata/若葉ヒナタ/日向 (Blue Archive)
This is the dataset of hinata/若葉ヒナタ/日向 (Blue Archive), containing 476 images and their tags.
The core tags of this character are `long_hair, black_hair, breasts, halo, hair_over_one_eye, large_breasts, red_eyes, earrings, cross_earrings, very_long_hair, hat, sun_hat`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 476 | 804.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hinata_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 476 | 671.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hinata_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1259 | 1.36 GiB | [Download](https://huggingface.co/datasets/CyberHarem/hinata_bluearchive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hinata_bluearchive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, asymmetrical_bangs, blush, cleavage_cutout, cross, habit, jewelry, nun, simple_background, solo, white_background, eyes_visible_through_hair, looking_at_viewer, long_sleeves, upper_body, closed_mouth, necktie |
| 1 | 6 |  |  |  |  |  | 1girl, asymmetrical_bangs, cleavage_cutout, cross, habit, jewelry, long_sleeves, looking_at_viewer, nun, pelvic_curtain, simple_background, solo, white_background, white_thighhighs, blush, closed_mouth, sitting, smile, necktie |
| 2 | 8 |  |  |  |  |  | 1girl, black_choker, casual_one-piece_swimsuit, cleavage, closed_mouth, collarbone, looking_at_viewer, official_alternate_costume, solo, white_one-piece_swimsuit, blush, covered_navel, smile, bare_shoulders, cowboy_shot, hat_flower, braid, one_eye_covered, yellow_headwear, criss-cross_halter, jewelry, simple_background, white_background, beach, yellow_halo |
| 3 | 7 |  |  |  |  |  | 1girl, bare_shoulders, casual_one-piece_swimsuit, hat_flower, looking_at_viewer, official_alternate_costume, solo, white_one-piece_swimsuit, black_choker, cleavage, collarbone, simple_background, blush, jewelry, white_background, braided_ponytail, cowboy_shot, one_eye_covered, thighs, black_bikini, criss-cross_halter, huge_breasts, yellow_halo |
| 4 | 16 |  |  |  |  |  | 1girl, blush, casual_one-piece_swimsuit, cleavage, looking_at_viewer, official_alternate_costume, outdoors, solo, white_one-piece_swimsuit, hat_flower, black_choker, braid, collarbone, criss-cross_halter, day, jewelry, ocean, open_mouth, beach, blue_sky, bare_shoulders, bikini, one_eye_covered, smile, yellow_halo, covered_navel, cowboy_shot, cloud, yellow_headwear |
| 5 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, solo_focus, completely_nude, huge_breasts, mosaic_censoring, paizuri, sweat, collarbone, nipples, open_mouth, penis, braid, eyes_visible_through_hair, jewelry, looking_at_viewer, pov, wet |
| 6 | 5 |  |  |  |  |  | 1boy, blush, hetero, nipples, nun, solo_focus, 1girl, nude, open_mouth, sex_from_behind, white_thighhighs, eyes_visible_through_hair, garter_belt, habit, jewelry, penis, sweat, vaginal, bar_censor, collarbone, doggystyle, huge_breasts, motion_lines, navel, pussy, standing_sex, tongue_out |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | asymmetrical_bangs | blush | cleavage_cutout | cross | habit | jewelry | nun | simple_background | solo | white_background | eyes_visible_through_hair | looking_at_viewer | long_sleeves | upper_body | closed_mouth | necktie | pelvic_curtain | white_thighhighs | sitting | smile | black_choker | casual_one-piece_swimsuit | cleavage | collarbone | official_alternate_costume | white_one-piece_swimsuit | covered_navel | bare_shoulders | cowboy_shot | hat_flower | braid | one_eye_covered | yellow_headwear | criss-cross_halter | beach | yellow_halo | braided_ponytail | thighs | black_bikini | huge_breasts | outdoors | day | ocean | open_mouth | blue_sky | bikini | cloud | 1boy | hetero | solo_focus | completely_nude | mosaic_censoring | paizuri | sweat | nipples | penis | pov | wet | nude | sex_from_behind | garter_belt | vaginal | bar_censor | doggystyle | motion_lines | navel | pussy | standing_sex | tongue_out |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------------|:--------|:------------------|:--------|:--------|:----------|:------|:--------------------|:-------|:-------------------|:----------------------------|:--------------------|:---------------|:-------------|:---------------|:----------|:-----------------|:-------------------|:----------|:--------|:---------------|:----------------------------|:-----------|:-------------|:-----------------------------|:---------------------------|:----------------|:-----------------|:--------------|:-------------|:--------|:------------------|:------------------|:---------------------|:--------|:--------------|:-------------------|:---------|:---------------|:---------------|:-----------|:------|:--------|:-------------|:-----------|:---------|:--------|:-------|:---------|:-------------|:------------------|:-------------------|:----------|:--------|:----------|:--------|:------|:------|:-------|:------------------|:--------------|:----------|:-------------|:-------------|:---------------|:--------|:--------|:---------------|:-------------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | | X | | | | X | | X | X | X | | X | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | | X | | | | X | | X | X | X | | X | | | | | | | | | X | X | X | X | X | X | | X | X | X | | X | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 16 |  |  |  |  |  | X | | X | | | | X | | | X | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | X | | | | X | | | | | X | X | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | X | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | X | | | X | X | X | | | | X | | | | | | | X | | | | | | X | | | | | | | | | | | | | | | | X | | | | X | | | | X | X | X | | | | X | X | X | | | X | X | X | X | X | X | X | X | X | X | X |
|
mhenrichsen/creator | ---
dataset_info:
features:
- name: id
dtype: int64
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 184006
num_examples: 1000
download_size: 10392
dataset_size: 184006
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "creator"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
id_puisi | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
- text-generation
- fill-mask
task_ids: []
paperswithcode_id: null
pretty_name: Indonesian Puisi
tags:
- poem-generation
dataset_info:
features:
- name: title
dtype: string
- name: author
dtype: string
- name: puisi
dtype: string
- name: puisi_with_header
dtype: string
splits:
- name: train
num_bytes: 10613475
num_examples: 7223
download_size: 10558108
dataset_size: 10613475
---
# Dataset Card for id_puisi
## 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:** [puisi-pantun-generator](https://github.com/ilhamfp/puisi-pantun-generator)
- **Repository:** [puisi-pantun-generator](https://github.com/ilhamfp/puisi-pantun-generator)
- **Paper:** [N/A]
- **Leaderboard:** [N/A]
- **Point of Contact:** [Ilham Firdausi Putra](ilhamfputra31@gmail.com)
### Dataset Summary
Puisi (poem) is an Indonesian poetic form. The dataset contains 7223 Indonesian puisi with its title and author.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Indonesian
## Dataset Structure
### Data Instances
```
{
'puisi_with_header': 'TEPERANGKAP
Oleh Mangku Langit Jingga
Mungkin kau membiarkan aku
Membiarkan perasaan ini larut
Memberi ruang jiwaku hampa
Agar tetap terbiasa nikmati
Perangkap yang kau buat
Perisai yang kau banggakan
Takkan jadi tameng bagimu
Aku mengerti betapa hebatnya
Perangkap mu hei sang dewi
Ku akan terus merasa terbiasa
Dengan pesona indahmu
Ku masih akan nikmati hadirmu
Berjalanlah pada hati yang sama
Satu hati denganku
Walau ku terperangkap
Namunku nikmati dan jalani',
'title': 'TEPERANGKAP',
'author': 'Oleh Mangku Langit Jingga',
'puisi': 'Mungkin kau membiarkan aku
Membiarkan perasaan ini larut
Memberi ruang jiwaku hampa
Agar tetap terbiasa nikmati
Perangkap yang kau buat
Perisai yang kau banggakan
Takkan jadi tameng bagimu
Aku mengerti betapa hebatnya
Perangkap mu hei sang dewi
Ku akan terus merasa terbiasa
Dengan pesona indahmu
Ku masih akan nikmati hadirmu
Berjalanlah pada hati yang sama
Satu hati denganku
Walau ku terperangkap
Namunku nikmati dan jalani',
}
```
### Data Fields
- `puisi_with_header`: the raw text from scraping
- `title`: the title extracted from the raw text using regex
- `author`: the author extracted from the raw text using regex
- `puisi`: the poem with title and author extracted out using regex
### Data Splits
The dataset contains only a train set.
## Dataset Creation
### Curation Rationale
The dataset was initially collected as an experiment to generate an Indonesian poem using GPT-2.
### Source Data
#### Initial Data Collection and Normalization
The dataset was scraped using BeautifulSoup from lokerpuisi.web.id (the data no longer exist on the original blog). The title and author column was produced using regex match from puisi_with_header column.
#### Who are the source language producers?
The poems were generated by humans. The users of the original blog voluntarily submit their original poems to get published on the blog.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### 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
The regex match used to extract the title & author from the raw text is not perfect. Some title & text is still failed to get extracted.
## Additional Information
### Dataset Curators
Ilham Firdausi Putra
### Licensing Information
MIT License
### Citation Information
[N/A]
### Contributions
Thanks to [@ilhamfp](https://github.com/ilhamfp) for adding this dataset. |
sinhala-nlp/NSINA-Headlines | ---
license: cc-by-sa-4.0
task_categories:
- text-generation
language:
- si
--- |
Mrstoh/Wlsjsj | ---
license: afl-3.0
---
|
CyberHarem/suou_momoko_theidolmstermillionlive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of suou_momoko/周防桃子 (THE iDOLM@STER: Million Live!)
This is the dataset of suou_momoko/周防桃子 (THE iDOLM@STER: Million Live!), containing 500 images and their tags.
The core tags of this character are `blue_eyes, brown_hair, short_hair, bangs, ahoge, hair_ornament, hair_flower`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 646.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suou_momoko_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 358.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suou_momoko_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1209 | 794.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suou_momoko_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 569.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suou_momoko_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1209 | 1.14 GiB | [Download](https://huggingface.co/datasets/CyberHarem/suou_momoko_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/suou_momoko_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, yellow_dress, floral_print, simple_background, white_background, long_sleeves, open_mouth, upper_body, short_sleeves, smile, v-shaped_eyebrows, white_flower |
| 1 | 9 |  |  |  |  |  | 1girl, blush, flower, looking_at_viewer, solo, yellow_dress, simple_background, white_background, floral_print, upper_body, smile, collarbone, wavy_hair |
| 2 | 9 |  |  |  |  |  | 1girl, blue_bow, bracelet, hair_bow, solo, blush, looking_at_viewer, open_mouth, puffy_short_sleeves, orange_bow, bowtie, sailor_collar, star_hair_ornament, white_dress, frilled_dress, :d, collarbone, holding |
| 3 | 7 |  |  |  |  |  | 1girl, blush, looking_at_viewer, puffy_short_sleeves, solo, wrist_cuffs, apron, blue_dress, short_twintails, :d, alice_(alice_in_wonderland)_(cosplay), blue_ribbon, open_mouth, frilled_dress, hair_ribbon, simple_background, blue_bowtie, card, hair_bow, heart, low_twintails, white_background |
| 4 | 10 |  |  |  |  |  | 1girl, beret, blush, red_headwear, solo, white_shirt, pinafore_dress, long_sleeves, looking_at_viewer, bowtie, simple_background, upper_body, white_background, light_brown_hair, blunt_bangs, open_mouth, striped, wavy_hair |
| 5 | 7 |  |  |  |  |  | 1girl, blush, looking_at_viewer, maid_headdress, pink_bowtie, puffy_short_sleeves, solo, wrist_cuffs, enmaided, frilled_apron, simple_background, white_apron, pink_dress, white_background, :o, frilled_sleeves, heart_hands, open_mouth, skirt, upper_body, waist_apron, white_shirt |
| 6 | 6 |  |  |  |  |  | 1girl, black_gloves, blush, cat_ears, jingle_bell, mini_crown, solo, animal_ear_fluff, fur_trim, looking_at_viewer, puffy_short_sleeves, blue_bow, dress, epaulettes, neck_bell, open_mouth, :d, blurry, cat_tail, frilled_sleeves, gold_trim, holding, simple_background, striped_bowtie, upper_body |
| 7 | 6 |  |  |  |  |  | 1girl, blush, looking_at_viewer, navel, solo, small_breasts, bow_bikini, simple_background, blue_bikini, sailor_bikini, smile, white_background, white_bikini |
| 8 | 15 |  |  |  |  |  | 1boy, blush, hetero, 1girl, nipples, small_breasts, solo_focus, open_mouth, penis, loli, navel, spread_legs, vaginal, sweat, bar_censor, completely_nude, cum_in_pussy, flower, saliva, tears, sex_from_behind, straddling |
| 9 | 9 |  |  |  |  |  | 1girl, solo, day, looking_at_viewer, outdoors, blue_sky, beach, cloud, blush, ocean, smile, barefoot, black_bikini, frilled_bikini, navel, open_mouth, small_breasts |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | solo | yellow_dress | floral_print | simple_background | white_background | long_sleeves | open_mouth | upper_body | short_sleeves | smile | v-shaped_eyebrows | white_flower | flower | collarbone | wavy_hair | blue_bow | bracelet | hair_bow | puffy_short_sleeves | orange_bow | bowtie | sailor_collar | star_hair_ornament | white_dress | frilled_dress | :d | holding | wrist_cuffs | apron | blue_dress | short_twintails | alice_(alice_in_wonderland)_(cosplay) | blue_ribbon | hair_ribbon | blue_bowtie | card | heart | low_twintails | beret | red_headwear | white_shirt | pinafore_dress | light_brown_hair | blunt_bangs | striped | maid_headdress | pink_bowtie | enmaided | frilled_apron | white_apron | pink_dress | :o | frilled_sleeves | heart_hands | skirt | waist_apron | black_gloves | cat_ears | jingle_bell | mini_crown | animal_ear_fluff | fur_trim | dress | epaulettes | neck_bell | blurry | cat_tail | gold_trim | striped_bowtie | navel | small_breasts | bow_bikini | blue_bikini | sailor_bikini | white_bikini | 1boy | hetero | nipples | solo_focus | penis | loli | spread_legs | vaginal | sweat | bar_censor | completely_nude | cum_in_pussy | saliva | tears | sex_from_behind | straddling | day | outdoors | blue_sky | beach | cloud | ocean | barefoot | black_bikini | frilled_bikini |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------|:---------------|:---------------|:--------------------|:-------------------|:---------------|:-------------|:-------------|:----------------|:--------|:--------------------|:---------------|:---------|:-------------|:------------|:-----------|:-----------|:-----------|:----------------------|:-------------|:---------|:----------------|:---------------------|:--------------|:----------------|:-----|:----------|:--------------|:--------|:-------------|:------------------|:----------------------------------------|:--------------|:--------------|:--------------|:-------|:--------|:----------------|:--------|:---------------|:--------------|:-----------------|:-------------------|:--------------|:----------|:-----------------|:--------------|:-----------|:----------------|:--------------|:-------------|:-----|:------------------|:--------------|:--------|:--------------|:---------------|:-----------|:--------------|:-------------|:-------------------|:-----------|:--------|:-------------|:------------|:---------|:-----------|:------------|:-----------------|:--------|:----------------|:-------------|:--------------|:----------------|:---------------|:-------|:---------|:----------|:-------------|:--------|:-------|:--------------|:----------|:--------|:-------------|:------------------|:---------------|:---------|:--------|:------------------|:-------------|:------|:-----------|:-----------|:--------|:--------|:--------|:-----------|:---------------|:-----------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | X | | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | X | X | X | | | | | | X | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | X | X | X | | | X | X | | X | | | | | | | | | | | X | X | | | | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 10 |  |  |  |  |  | X | X | X | X | | | X | X | X | X | X | | | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | X | X | X | | | X | X | | X | X | | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | X | X | X | | | X | | | X | X | | | | | | | | X | | | X | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | X | X | X | | | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 15 |  |  |  |  |  | X | X | | | | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 9 | 9 |  |  |  |  |  | X | X | X | X | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |
|
reeink/dota | ---
license: other
---
|
Pak-Speech-Processing/urdu-emotions | ---
license: mit
language:
- ur
size_categories:
- n<1K
task_categories:
- audio-classification
---
# URDU-Dataset
## 1. General information
URDU dataset contains emotional utterances of Urdu speech gathered from Urdu talk shows. It contains 300 utterances of four basic emotions: Angry, Happy, and Neutral. There are 38 speakers (27 male and 11 female). This data is created from YouTube. Speakers are selected randomly.
For more details about dataset, please refer the complete paper "Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages". https://arxiv.org/pdf/1812.10411.pdf |
CyberHarem/cecilia_shania_honkai3 | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of cecilia_shania (Houkai 3rd)
This is the dataset of cecilia_shania (Houkai 3rd), containing 88 images and their tags.
The core tags of this character are `long_hair, bangs, breasts, blue_eyes, hair_between_eyes, white_hair, hair_ornament, very_long_hair, earrings, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 88 | 121.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cecilia_shania_honkai3/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 88 | 65.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cecilia_shania_honkai3/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 217 | 138.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cecilia_shania_honkai3/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 88 | 106.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cecilia_shania_honkai3/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 217 | 200.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cecilia_shania_honkai3/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/cecilia_shania_honkai3',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, solo, looking_at_viewer, simple_background, smile, closed_mouth, jewelry, white_dress, bare_shoulders, hair_flower, white_background, cleavage, medium_breasts |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | simple_background | smile | closed_mouth | jewelry | white_dress | bare_shoulders | hair_flower | white_background | cleavage | medium_breasts |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------------------|:--------|:---------------|:----------|:--------------|:-----------------|:--------------|:-------------------|:-----------|:-----------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
1aurent/STORK | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': good
'1': poor
splits:
- name: train
num_bytes: 4513394
num_examples: 84
- name: test
num_bytes: 729815
num_examples: 14
download_size: 5243240
dataset_size: 5243209
license: mit
task_categories:
- image-classification
tags:
- biology
- IVF
- embryo
size_categories:
- n<1K
---
# Stork
**Homepage**: https://github.com/ih-lab/STORK/ \
**Publication Date**: 2019-01-18 \
**License**: [MIT](https://github.com/ih-lab/STORK/blob/master/LICENSE)
 |
vishruthnath/Calc-MAWPS-CalcBERT-Tags | ---
dataset_info:
features:
- name: chain
dtype: string
- name: equation
dtype: string
- name: expression
dtype: string
- name: id
dtype: string
- name: num_unique_ops
dtype: int64
- name: operand_tags
sequence: int64
- name: operands
sequence: float64
- name: operation
dtype: string
- name: question
dtype: string
- name: question_split
sequence: string
- name: result
dtype: string
- name: result_float
dtype: float64
- name: valid
dtype: bool
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 690330
num_examples: 1053
- name: validation
num_bytes: 659394
num_examples: 1016
- name: test
num_bytes: 333380
num_examples: 510
download_size: 473091
dataset_size: 1683104
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
gsoisson/alignment-internship-exercise | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
- conversational
language:
- en
size_categories:
- n<1K
---
# Dataset Card for the Alignement Internship Exercise
## Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
This dataset provides a list of questions accompanied by Phi-2's best answer to them, as ranked by OpenAssitant's reward model.
## Dataset Creation
The questions were handpicked from the LDJnr/Capybara, Open-Orca/OpenOrca and truthful_qa datasets, the coding exercise is from LeetCode's top 100 liked questions and I found the last prompt on a blog and modified it. I have chosen these prompts specifically to evaluate the model on different domains of knowledge (STEM, coding, humanities), different tasks (reasoning, writing, summarization, question-answering), different levels of complexity, different lengths of prompts as well as its safety and alignment with human values and ability to defend itself against adversarial prompts.
Then each prompt was generated using the following logic:
"""\<USER>: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
correct. If you don't know the answer to a question, please don't share false information.
Here is my question: {question}
\<ASSISTANT>:"""
After that, for each question we generate K=8 answers with Phi-2 by setting the maximum number of new tokens to 300, stopping if the end of text token is generated, doing sampling, and setting the temperature to some predefined value.
We then rank each answer using OpenAssitant's reward model and take the best one.
Finally, we perform a small temperature hyperparameter scan and found that the best answers according to the reward model were given using a temperature value of 0.4. So these are the answers that are in the dataset.
## Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Capybara Dataset:** [link](https://huggingface.co/datasets/LDJnr/Capybara)
- **OpenOrca Dataset:** [link](https://huggingface.co/datasets/Open-Orca/OpenOrca)
- **Truthful QA Dataset:** [link](https://huggingface.co/datasets/truthful_qa)
- **LeetCode's "Subsets" problem:** [link](https://leetcode.com/problem-list/top-100-liked-questions/)
- **DAN prompt:** [link](https://www.promptingguide.ai/risks/adversarial)
- **Llama's system prompt:** [link](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/tokenization_llama.py)
- **Micrososft's Phi-2:** [link](https://huggingface.co/microsoft/phi-2)
- **OpenAssistant's reward model:** [link](https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2)
|
HuggingFaceM4/DVQA | Invalid username or password. |
vntc/wiki-mini-corpus | ---
dataset_info:
features:
- name: id
dtype: int64
- name: passage
dtype: string
- name: metadata
struct:
- name: split
dtype: int64
- name: title
dtype: string
- name: token_count
dtype: int64
splits:
- name: train
num_bytes: 41455820
num_examples: 23039
download_size: 21018685
dataset_size: 41455820
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dkshjn/mixqa_v0.1 | ---
dataset_info:
features:
- name: question
dtype: string
- name: optionsKey
dtype: string
- name: prompt
dtype: string
- name: gold
dtype: string
splits:
- name: train
num_bytes: 373803.0598068066
num_examples: 500
download_size: 235529
dataset_size: 373803.0598068066
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mixqa_v0.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CVasNLPExperiments/DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_1880 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 809983
num_examples: 1880
- name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_dtd_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 631818
num_examples: 1880
- name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 834528
num_examples: 1880
- name: fewshot_1_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 1624239
num_examples: 1880
- name: fewshot_3_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 3204182
num_examples: 1880
- name: fewshot_0__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices
num_bytes: 835669
num_examples: 1880
- name: fewshot_1__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
num_bytes: 1578128
num_examples: 1880
- name: fewshot_1__Attributes_ViT_B_16_descriptors_text_davinci_003_full_clip_tags_ViT_B_16_simple_specific_rices
num_bytes: 1618889
num_examples: 1880
- name: fewshot_3__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
num_bytes: 3113380
num_examples: 1880
- name: fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
num_bytes: 787095
num_examples: 1880
download_size: 3231407
dataset_size: 15037911
configs:
- config_name: default
data_files:
- split: fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices
path: data/fewshot_0__Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_clip_tags_LAION_ViT_H_14_2B_simple_specific_rices-*
---
# Dataset Card for "DTD_parition1_test_google_flan_t5_xxl_mode_T_SPECIFIC_A_ns_1880"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irds/mr-tydi_bn | ---
pretty_name: '`mr-tydi/bn`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `mr-tydi/bn`
The `mr-tydi/bn` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mr-tydi#mr-tydi/bn).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=304,059
- `queries` (i.e., topics); count=2,264
- `qrels`: (relevance assessments); count=2,292
This dataset is used by: [`mr-tydi_bn_dev`](https://huggingface.co/datasets/irds/mr-tydi_bn_dev), [`mr-tydi_bn_test`](https://huggingface.co/datasets/irds/mr-tydi_bn_test), [`mr-tydi_bn_train`](https://huggingface.co/datasets/irds/mr-tydi_bn_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mr-tydi_bn', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
queries = load_dataset('irds/mr-tydi_bn', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mr-tydi_bn', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Zhang2021MrTyDi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
@article{Clark2020TyDiQa,
title={{TyDi QA}: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},
author={Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki},
year={2020},
journal={Transactions of the Association for Computational Linguistics}
}
```
|
open-llm-leaderboard/details_Technoculture__mtor-2x7b | ---
pretty_name: Evaluation run of Technoculture/mtor-2x7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Technoculture/mtor-2x7b](https://huggingface.co/Technoculture/mtor-2x7b) 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_Technoculture__mtor-2x7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-13T12:35:53.883707](https://huggingface.co/datasets/open-llm-leaderboard/details_Technoculture__mtor-2x7b/blob/main/results_2024-02-13T12-35-53.883707.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\": 0.513846349514935,\n\
\ \"acc_stderr\": 0.034127487865330444,\n \"acc_norm\": 0.5225503308269146,\n\
\ \"acc_norm_stderr\": 0.03496907428627984,\n \"mc1\": 0.3108935128518972,\n\
\ \"mc1_stderr\": 0.016203316673559693,\n \"mc2\": 0.48059153955864553,\n\
\ \"mc2_stderr\": 0.014969300928874024\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5162116040955631,\n \"acc_stderr\": 0.014603708567414947,\n\
\ \"acc_norm\": 0.5520477815699659,\n \"acc_norm_stderr\": 0.014532011498211678\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.543218482374029,\n\
\ \"acc_stderr\": 0.00497110626504655,\n \"acc_norm\": 0.7360087631945827,\n\
\ \"acc_norm_stderr\": 0.004398937225038412\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847415,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847415\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5407407407407407,\n\
\ \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.5407407407407407,\n\
\ \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5263157894736842,\n \"acc_stderr\": 0.04063302731486671,\n\
\ \"acc_norm\": 0.5263157894736842,\n \"acc_norm_stderr\": 0.04063302731486671\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.51,\n\
\ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \
\ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6037735849056604,\n \"acc_stderr\": 0.030102793781791197,\n\
\ \"acc_norm\": 0.6037735849056604,\n \"acc_norm_stderr\": 0.030102793781791197\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5347222222222222,\n\
\ \"acc_stderr\": 0.041711158581816184,\n \"acc_norm\": 0.5347222222222222,\n\
\ \"acc_norm_stderr\": 0.041711158581816184\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709390974,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709390974\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.48554913294797686,\n\
\ \"acc_stderr\": 0.03810871630454764,\n \"acc_norm\": 0.48554913294797686,\n\
\ \"acc_norm_stderr\": 0.03810871630454764\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n\
\ \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n\
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.43829787234042555,\n \"acc_stderr\": 0.03243618636108101,\n\
\ \"acc_norm\": 0.43829787234042555,\n \"acc_norm_stderr\": 0.03243618636108101\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.32456140350877194,\n\
\ \"acc_stderr\": 0.04404556157374766,\n \"acc_norm\": 0.32456140350877194,\n\
\ \"acc_norm_stderr\": 0.04404556157374766\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n\
\ \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3148148148148148,\n \"acc_stderr\": 0.023919984164047732,\n \"\
acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.023919984164047732\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.0404061017820884,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.0404061017820884\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5967741935483871,\n\
\ \"acc_stderr\": 0.027906150826041146,\n \"acc_norm\": 0.5967741935483871,\n\
\ \"acc_norm_stderr\": 0.027906150826041146\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3497536945812808,\n \"acc_stderr\": 0.03355400904969565,\n\
\ \"acc_norm\": 0.3497536945812808,\n \"acc_norm_stderr\": 0.03355400904969565\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\
: 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6787878787878788,\n \"acc_stderr\": 0.03646204963253812,\n\
\ \"acc_norm\": 0.6787878787878788,\n \"acc_norm_stderr\": 0.03646204963253812\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6515151515151515,\n \"acc_stderr\": 0.03394853965156402,\n \"\
acc_norm\": 0.6515151515151515,\n \"acc_norm_stderr\": 0.03394853965156402\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7461139896373057,\n \"acc_stderr\": 0.03141024780565319,\n\
\ \"acc_norm\": 0.7461139896373057,\n \"acc_norm_stderr\": 0.03141024780565319\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.44871794871794873,\n \"acc_stderr\": 0.025217315184846482,\n\
\ \"acc_norm\": 0.44871794871794873,\n \"acc_norm_stderr\": 0.025217315184846482\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.27037037037037037,\n \"acc_stderr\": 0.02708037281514566,\n \
\ \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.02708037281514566\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.4495798319327731,\n \"acc_stderr\": 0.03231293497137707,\n \
\ \"acc_norm\": 0.4495798319327731,\n \"acc_norm_stderr\": 0.03231293497137707\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2582781456953642,\n \"acc_stderr\": 0.035737053147634576,\n \"\
acc_norm\": 0.2582781456953642,\n \"acc_norm_stderr\": 0.035737053147634576\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7045871559633028,\n \"acc_stderr\": 0.019560619182976,\n \"acc_norm\"\
: 0.7045871559633028,\n \"acc_norm_stderr\": 0.019560619182976\n },\n\
\ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.3472222222222222,\n\
\ \"acc_stderr\": 0.032468872436376486,\n \"acc_norm\": 0.3472222222222222,\n\
\ \"acc_norm_stderr\": 0.032468872436376486\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.696078431372549,\n \"acc_stderr\": 0.03228210387037892,\n\
\ \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.03228210387037892\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.759493670886076,\n \"acc_stderr\": 0.027820781981149685,\n \
\ \"acc_norm\": 0.759493670886076,\n \"acc_norm_stderr\": 0.027820781981149685\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6457399103139013,\n\
\ \"acc_stderr\": 0.032100621541349864,\n \"acc_norm\": 0.6457399103139013,\n\
\ \"acc_norm_stderr\": 0.032100621541349864\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6030534351145038,\n \"acc_stderr\": 0.04291135671009225,\n\
\ \"acc_norm\": 0.6030534351145038,\n \"acc_norm_stderr\": 0.04291135671009225\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6859504132231405,\n \"acc_stderr\": 0.042369647530410184,\n \"\
acc_norm\": 0.6859504132231405,\n \"acc_norm_stderr\": 0.042369647530410184\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5833333333333334,\n\
\ \"acc_stderr\": 0.04766075165356461,\n \"acc_norm\": 0.5833333333333334,\n\
\ \"acc_norm_stderr\": 0.04766075165356461\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.5521472392638037,\n \"acc_stderr\": 0.03906947479456607,\n\
\ \"acc_norm\": 0.5521472392638037,\n \"acc_norm_stderr\": 0.03906947479456607\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n\
\ \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.4107142857142857,\n\
\ \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6601941747572816,\n \"acc_stderr\": 0.04689765937278135,\n\
\ \"acc_norm\": 0.6601941747572816,\n \"acc_norm_stderr\": 0.04689765937278135\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7692307692307693,\n\
\ \"acc_stderr\": 0.027601921381417604,\n \"acc_norm\": 0.7692307692307693,\n\
\ \"acc_norm_stderr\": 0.027601921381417604\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6934865900383141,\n\
\ \"acc_stderr\": 0.01648695289304151,\n \"acc_norm\": 0.6934865900383141,\n\
\ \"acc_norm_stderr\": 0.01648695289304151\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.026483392042098177,\n\
\ \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.026483392042098177\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2581005586592179,\n\
\ \"acc_stderr\": 0.014635185616527817,\n \"acc_norm\": 0.2581005586592179,\n\
\ \"acc_norm_stderr\": 0.014635185616527817\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.027826109307283693,\n\
\ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.027826109307283693\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5627009646302251,\n\
\ \"acc_stderr\": 0.0281739177617629,\n \"acc_norm\": 0.5627009646302251,\n\
\ \"acc_norm_stderr\": 0.0281739177617629\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5802469135802469,\n \"acc_stderr\": 0.027460099557005138,\n\
\ \"acc_norm\": 0.5802469135802469,\n \"acc_norm_stderr\": 0.027460099557005138\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.40070921985815605,\n \"acc_stderr\": 0.029233465745573083,\n \
\ \"acc_norm\": 0.40070921985815605,\n \"acc_norm_stderr\": 0.029233465745573083\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3474576271186441,\n\
\ \"acc_stderr\": 0.0121614177297498,\n \"acc_norm\": 0.3474576271186441,\n\
\ \"acc_norm_stderr\": 0.0121614177297498\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5183823529411765,\n \"acc_stderr\": 0.030352303395351964,\n\
\ \"acc_norm\": 0.5183823529411765,\n \"acc_norm_stderr\": 0.030352303395351964\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5163398692810458,\n \"acc_stderr\": 0.02021703065318646,\n \
\ \"acc_norm\": 0.5163398692810458,\n \"acc_norm_stderr\": 0.02021703065318646\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.0469237132203465,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.0469237132203465\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6285714285714286,\n \"acc_stderr\": 0.030932858792789848,\n\
\ \"acc_norm\": 0.6285714285714286,\n \"acc_norm_stderr\": 0.030932858792789848\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6417910447761194,\n\
\ \"acc_stderr\": 0.03390393042268814,\n \"acc_norm\": 0.6417910447761194,\n\
\ \"acc_norm_stderr\": 0.03390393042268814\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \
\ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.45180722891566266,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.45180722891566266,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6432748538011696,\n \"acc_stderr\": 0.03674013002860954,\n\
\ \"acc_norm\": 0.6432748538011696,\n \"acc_norm_stderr\": 0.03674013002860954\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3108935128518972,\n\
\ \"mc1_stderr\": 0.016203316673559693,\n \"mc2\": 0.48059153955864553,\n\
\ \"mc2_stderr\": 0.014969300928874024\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7063930544593529,\n \"acc_stderr\": 0.012799397296204164\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.03639120545868082,\n \
\ \"acc_stderr\": 0.005158113489231194\n }\n}\n```"
repo_url: https://huggingface.co/Technoculture/mtor-2x7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|arc:challenge|25_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|gsm8k|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hellaswag|10_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T12-35-53.883707.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-13T12-35-53.883707.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- '**/details_harness|winogrande|5_2024-02-13T12-35-53.883707.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-13T12-35-53.883707.parquet'
- config_name: results
data_files:
- split: 2024_02_13T12_35_53.883707
path:
- results_2024-02-13T12-35-53.883707.parquet
- split: latest
path:
- results_2024-02-13T12-35-53.883707.parquet
---
# Dataset Card for Evaluation run of Technoculture/mtor-2x7b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Technoculture/mtor-2x7b](https://huggingface.co/Technoculture/mtor-2x7b) 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_Technoculture__mtor-2x7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-13T12:35:53.883707](https://huggingface.co/datasets/open-llm-leaderboard/details_Technoculture__mtor-2x7b/blob/main/results_2024-02-13T12-35-53.883707.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": 0.513846349514935,
"acc_stderr": 0.034127487865330444,
"acc_norm": 0.5225503308269146,
"acc_norm_stderr": 0.03496907428627984,
"mc1": 0.3108935128518972,
"mc1_stderr": 0.016203316673559693,
"mc2": 0.48059153955864553,
"mc2_stderr": 0.014969300928874024
},
"harness|arc:challenge|25": {
"acc": 0.5162116040955631,
"acc_stderr": 0.014603708567414947,
"acc_norm": 0.5520477815699659,
"acc_norm_stderr": 0.014532011498211678
},
"harness|hellaswag|10": {
"acc": 0.543218482374029,
"acc_stderr": 0.00497110626504655,
"acc_norm": 0.7360087631945827,
"acc_norm_stderr": 0.004398937225038412
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847415,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847415
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5407407407407407,
"acc_stderr": 0.04304979692464242,
"acc_norm": 0.5407407407407407,
"acc_norm_stderr": 0.04304979692464242
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5263157894736842,
"acc_stderr": 0.04063302731486671,
"acc_norm": 0.5263157894736842,
"acc_norm_stderr": 0.04063302731486671
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6037735849056604,
"acc_stderr": 0.030102793781791197,
"acc_norm": 0.6037735849056604,
"acc_norm_stderr": 0.030102793781791197
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5347222222222222,
"acc_stderr": 0.041711158581816184,
"acc_norm": 0.5347222222222222,
"acc_norm_stderr": 0.041711158581816184
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709390974,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709390974
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.48554913294797686,
"acc_stderr": 0.03810871630454764,
"acc_norm": 0.48554913294797686,
"acc_norm_stderr": 0.03810871630454764
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.28431372549019607,
"acc_stderr": 0.04488482852329017,
"acc_norm": 0.28431372549019607,
"acc_norm_stderr": 0.04488482852329017
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.43829787234042555,
"acc_stderr": 0.03243618636108101,
"acc_norm": 0.43829787234042555,
"acc_norm_stderr": 0.03243618636108101
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.32456140350877194,
"acc_stderr": 0.04404556157374766,
"acc_norm": 0.32456140350877194,
"acc_norm_stderr": 0.04404556157374766
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.47586206896551725,
"acc_stderr": 0.041618085035015295,
"acc_norm": 0.47586206896551725,
"acc_norm_stderr": 0.041618085035015295
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.023919984164047732,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.023919984164047732
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.0404061017820884,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.0404061017820884
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5967741935483871,
"acc_stderr": 0.027906150826041146,
"acc_norm": 0.5967741935483871,
"acc_norm_stderr": 0.027906150826041146
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3497536945812808,
"acc_stderr": 0.03355400904969565,
"acc_norm": 0.3497536945812808,
"acc_norm_stderr": 0.03355400904969565
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6787878787878788,
"acc_stderr": 0.03646204963253812,
"acc_norm": 0.6787878787878788,
"acc_norm_stderr": 0.03646204963253812
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6515151515151515,
"acc_stderr": 0.03394853965156402,
"acc_norm": 0.6515151515151515,
"acc_norm_stderr": 0.03394853965156402
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7461139896373057,
"acc_stderr": 0.03141024780565319,
"acc_norm": 0.7461139896373057,
"acc_norm_stderr": 0.03141024780565319
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.44871794871794873,
"acc_stderr": 0.025217315184846482,
"acc_norm": 0.44871794871794873,
"acc_norm_stderr": 0.025217315184846482
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.27037037037037037,
"acc_stderr": 0.02708037281514566,
"acc_norm": 0.27037037037037037,
"acc_norm_stderr": 0.02708037281514566
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.4495798319327731,
"acc_stderr": 0.03231293497137707,
"acc_norm": 0.4495798319327731,
"acc_norm_stderr": 0.03231293497137707
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2582781456953642,
"acc_stderr": 0.035737053147634576,
"acc_norm": 0.2582781456953642,
"acc_norm_stderr": 0.035737053147634576
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7045871559633028,
"acc_stderr": 0.019560619182976,
"acc_norm": 0.7045871559633028,
"acc_norm_stderr": 0.019560619182976
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.3472222222222222,
"acc_stderr": 0.032468872436376486,
"acc_norm": 0.3472222222222222,
"acc_norm_stderr": 0.032468872436376486
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.696078431372549,
"acc_stderr": 0.03228210387037892,
"acc_norm": 0.696078431372549,
"acc_norm_stderr": 0.03228210387037892
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.759493670886076,
"acc_stderr": 0.027820781981149685,
"acc_norm": 0.759493670886076,
"acc_norm_stderr": 0.027820781981149685
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6457399103139013,
"acc_stderr": 0.032100621541349864,
"acc_norm": 0.6457399103139013,
"acc_norm_stderr": 0.032100621541349864
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6030534351145038,
"acc_stderr": 0.04291135671009225,
"acc_norm": 0.6030534351145038,
"acc_norm_stderr": 0.04291135671009225
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6859504132231405,
"acc_stderr": 0.042369647530410184,
"acc_norm": 0.6859504132231405,
"acc_norm_stderr": 0.042369647530410184
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5833333333333334,
"acc_stderr": 0.04766075165356461,
"acc_norm": 0.5833333333333334,
"acc_norm_stderr": 0.04766075165356461
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.5521472392638037,
"acc_stderr": 0.03906947479456607,
"acc_norm": 0.5521472392638037,
"acc_norm_stderr": 0.03906947479456607
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4107142857142857,
"acc_stderr": 0.04669510663875191,
"acc_norm": 0.4107142857142857,
"acc_norm_stderr": 0.04669510663875191
},
"harness|hendrycksTest-management|5": {
"acc": 0.6601941747572816,
"acc_stderr": 0.04689765937278135,
"acc_norm": 0.6601941747572816,
"acc_norm_stderr": 0.04689765937278135
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.7692307692307693,
"acc_stderr": 0.027601921381417604,
"acc_norm": 0.7692307692307693,
"acc_norm_stderr": 0.027601921381417604
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.59,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.59,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6934865900383141,
"acc_stderr": 0.01648695289304151,
"acc_norm": 0.6934865900383141,
"acc_norm_stderr": 0.01648695289304151
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5895953757225434,
"acc_stderr": 0.026483392042098177,
"acc_norm": 0.5895953757225434,
"acc_norm_stderr": 0.026483392042098177
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2581005586592179,
"acc_stderr": 0.014635185616527817,
"acc_norm": 0.2581005586592179,
"acc_norm_stderr": 0.014635185616527817
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6176470588235294,
"acc_stderr": 0.027826109307283693,
"acc_norm": 0.6176470588235294,
"acc_norm_stderr": 0.027826109307283693
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.5627009646302251,
"acc_stderr": 0.0281739177617629,
"acc_norm": 0.5627009646302251,
"acc_norm_stderr": 0.0281739177617629
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5802469135802469,
"acc_stderr": 0.027460099557005138,
"acc_norm": 0.5802469135802469,
"acc_norm_stderr": 0.027460099557005138
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.40070921985815605,
"acc_stderr": 0.029233465745573083,
"acc_norm": 0.40070921985815605,
"acc_norm_stderr": 0.029233465745573083
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3474576271186441,
"acc_stderr": 0.0121614177297498,
"acc_norm": 0.3474576271186441,
"acc_norm_stderr": 0.0121614177297498
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5183823529411765,
"acc_stderr": 0.030352303395351964,
"acc_norm": 0.5183823529411765,
"acc_norm_stderr": 0.030352303395351964
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5163398692810458,
"acc_stderr": 0.02021703065318646,
"acc_norm": 0.5163398692810458,
"acc_norm_stderr": 0.02021703065318646
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6,
"acc_stderr": 0.0469237132203465,
"acc_norm": 0.6,
"acc_norm_stderr": 0.0469237132203465
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6285714285714286,
"acc_stderr": 0.030932858792789848,
"acc_norm": 0.6285714285714286,
"acc_norm_stderr": 0.030932858792789848
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6417910447761194,
"acc_stderr": 0.03390393042268814,
"acc_norm": 0.6417910447761194,
"acc_norm_stderr": 0.03390393042268814
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-virology|5": {
"acc": 0.45180722891566266,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.45180722891566266,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6432748538011696,
"acc_stderr": 0.03674013002860954,
"acc_norm": 0.6432748538011696,
"acc_norm_stderr": 0.03674013002860954
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3108935128518972,
"mc1_stderr": 0.016203316673559693,
"mc2": 0.48059153955864553,
"mc2_stderr": 0.014969300928874024
},
"harness|winogrande|5": {
"acc": 0.7063930544593529,
"acc_stderr": 0.012799397296204164
},
"harness|gsm8k|5": {
"acc": 0.03639120545868082,
"acc_stderr": 0.005158113489231194
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
ovior/twitter_dataset_1713001370 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 2363044
num_examples: 6968
download_size: 1365933
dataset_size: 2363044
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA | ---
pretty_name: Evaluation run of PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA](https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-19T15:40:53.939427](https://huggingface.co/datasets/open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA_public/blob/main/results_2023-11-19T15-40-53.939427.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\": 0.6304901523986502,\n\
\ \"acc_stderr\": 0.03227432351145437,\n \"acc_norm\": 0.6396379138474626,\n\
\ \"acc_norm_stderr\": 0.03297469555234416,\n \"mc1\": 0.2974296205630355,\n\
\ \"mc1_stderr\": 0.016002651487361,\n \"mc2\": 0.449449453863883,\n\
\ \"mc2_stderr\": 0.014386188846092064,\n \"em\": 0.00576761744966443,\n\
\ \"em_stderr\": 0.0007755000442815149,\n \"f1\": 0.06506291946308734,\n\
\ \"f1_stderr\": 0.0015068091686217023\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5733788395904437,\n \"acc_stderr\": 0.014453185592920293,\n\
\ \"acc_norm\": 0.6075085324232082,\n \"acc_norm_stderr\": 0.014269634635670726\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6395140410276837,\n\
\ \"acc_stderr\": 0.0047916019756127646,\n \"acc_norm\": 0.8423620792670783,\n\
\ \"acc_norm_stderr\": 0.003636564286352675\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.038234289699266046,\n\
\ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.038234289699266046\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n\
\ \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\
\ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6011560693641619,\n\
\ \"acc_stderr\": 0.0373362665538351,\n \"acc_norm\": 0.6011560693641619,\n\
\ \"acc_norm_stderr\": 0.0373362665538351\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n\
\ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728762,\n\
\ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728762\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3994708994708995,\n \"acc_stderr\": 0.025225450284067884,\n \"\
acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.025225450284067884\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\
\ \"acc_stderr\": 0.04390259265377562,\n \"acc_norm\": 0.40476190476190477,\n\
\ \"acc_norm_stderr\": 0.04390259265377562\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7483870967741936,\n \"acc_stderr\": 0.024685979286239956,\n \"\
acc_norm\": 0.7483870967741936,\n \"acc_norm_stderr\": 0.024685979286239956\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n \"\
acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386414,\n \"\
acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386414\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.02381447708659355,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.02381447708659355\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6307692307692307,\n \"acc_stderr\": 0.02446861524147892,\n \
\ \"acc_norm\": 0.6307692307692307,\n \"acc_norm_stderr\": 0.02446861524147892\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3333333333333333,\n \"acc_stderr\": 0.02874204090394849,\n \
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.02874204090394849\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6218487394957983,\n \"acc_stderr\": 0.03149930577784906,\n \
\ \"acc_norm\": 0.6218487394957983,\n \"acc_norm_stderr\": 0.03149930577784906\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\"\
: 0.8146788990825689,\n \"acc_stderr\": 0.016659279700295845,\n \"\
acc_norm\": 0.8146788990825689,\n \"acc_norm_stderr\": 0.016659279700295845\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.7696078431372549,\n \"acc_stderr\": 0.02955429260569507,\n\
\ \"acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.02955429260569507\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7679324894514767,\n \"acc_stderr\": 0.027479744550808503,\n \
\ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.027479744550808503\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\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\
\ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286775,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286775\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\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\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.021586494001281382,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.021586494001281382\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.013890862162876166,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.013890862162876166\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577612,\n\
\ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577612\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.311731843575419,\n\
\ \"acc_stderr\": 0.015491756531894637,\n \"acc_norm\": 0.311731843575419,\n\
\ \"acc_norm_stderr\": 0.015491756531894637\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7745098039215687,\n \"acc_stderr\": 0.0239291555173513,\n\
\ \"acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.0239291555173513\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.024288533637726095,\n\
\ \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.024288533637726095\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5141843971631206,\n \"acc_stderr\": 0.02981549448368206,\n \
\ \"acc_norm\": 0.5141843971631206,\n \"acc_norm_stderr\": 0.02981549448368206\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45697522816166886,\n\
\ \"acc_stderr\": 0.012722869501611419,\n \"acc_norm\": 0.45697522816166886,\n\
\ \"acc_norm_stderr\": 0.012722869501611419\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6286764705882353,\n \"acc_stderr\": 0.029349803139765873,\n\
\ \"acc_norm\": 0.6286764705882353,\n \"acc_norm_stderr\": 0.029349803139765873\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.673202614379085,\n \"acc_stderr\": 0.018975427920507215,\n \
\ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.018975427920507215\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784603,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784603\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.026508590656233268,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.026508590656233268\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2974296205630355,\n\
\ \"mc1_stderr\": 0.016002651487361,\n \"mc2\": 0.449449453863883,\n\
\ \"mc2_stderr\": 0.014386188846092064\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7868981846882399,\n \"acc_stderr\": 0.011508957690722762\n\
\ },\n \"harness|drop|3\": {\n \"em\": 0.00576761744966443,\n \
\ \"em_stderr\": 0.0007755000442815149,\n \"f1\": 0.06506291946308734,\n\
\ \"f1_stderr\": 0.0015068091686217023\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.17134192570128887,\n \"acc_stderr\": 0.010379150273178359\n\
\ }\n}\n```"
repo_url: https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|arc:challenge|25_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|drop|3_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|gsm8k|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hellaswag|10_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-19T15-40-53.939427.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-19T15-40-53.939427.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- '**/details_harness|winogrande|5_2023-11-19T15-40-53.939427.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-19T15-40-53.939427.parquet'
- config_name: results
data_files:
- split: 2023_11_19T15_40_53.939427
path:
- results_2023-11-19T15-40-53.939427.parquet
- split: latest
path:
- results_2023-11-19T15-40-53.939427.parquet
---
# Dataset Card for Evaluation run of PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA](https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-19T15:40:53.939427](https://huggingface.co/datasets/open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.5-SFT-7B-QLoRA_public/blob/main/results_2023-11-19T15-40-53.939427.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": 0.6304901523986502,
"acc_stderr": 0.03227432351145437,
"acc_norm": 0.6396379138474626,
"acc_norm_stderr": 0.03297469555234416,
"mc1": 0.2974296205630355,
"mc1_stderr": 0.016002651487361,
"mc2": 0.449449453863883,
"mc2_stderr": 0.014386188846092064,
"em": 0.00576761744966443,
"em_stderr": 0.0007755000442815149,
"f1": 0.06506291946308734,
"f1_stderr": 0.0015068091686217023
},
"harness|arc:challenge|25": {
"acc": 0.5733788395904437,
"acc_stderr": 0.014453185592920293,
"acc_norm": 0.6075085324232082,
"acc_norm_stderr": 0.014269634635670726
},
"harness|hellaswag|10": {
"acc": 0.6395140410276837,
"acc_stderr": 0.0047916019756127646,
"acc_norm": 0.8423620792670783,
"acc_norm_stderr": 0.003636564286352675
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768081,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768081
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595852,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595852
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6710526315789473,
"acc_stderr": 0.038234289699266046,
"acc_norm": 0.6710526315789473,
"acc_norm_stderr": 0.038234289699266046
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
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}
```
### 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] |
CyberHarem/kasumi_nomura_asobiasobase | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Kasumi Nomura
This is the dataset of Kasumi Nomura, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 646 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 646 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 646 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 646 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
thongnef/MSB_done | ---
dataset_info:
features:
- name: sentence_idx
dtype: int64
- name: word
sequence: string
- name: pos
sequence: int64
- name: tag
sequence: int64
splits:
- name: train
num_bytes: 1470644.937352246
num_examples: 2391
- name: test
num_bytes: 367815.00315208826
num_examples: 598
download_size: 135996
dataset_size: 1838459.9405043342
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Simon-Kotchou/Lichess-960 | ---
task_categories:
- image-feature-extraction
dataset_info:
features:
- name: Event
dtype: string
- name: Site
dtype: string
- name: Date
dtype: string
- name: Round
dtype: string
- name: White
dtype: string
- name: Black
dtype: string
- name: Result
dtype: string
- name: Moves
dtype: string
- name: UTCDate
dtype: string
- name: UTCTime
dtype: string
splits:
- name: part_202402
num_bytes: 203050718
num_examples: 369322
- name: part_202401
num_bytes: 179938601
num_examples: 323714
- name: part_202312
num_bytes: 167122759
num_examples: 300955
- name: part_202311
num_bytes: 146909403
num_examples: 264995
- name: part_202310
num_bytes: 150554104
num_examples: 270087
- name: part_202309
num_bytes: 148359121
num_examples: 265993
download_size: 516815749
dataset_size: 995934706
configs:
- config_name: default
data_files:
- split: part_202402
path: data/part_202402-*
- split: part_202401
path: data/part_202401-*
- split: part_202312
path: data/part_202312-*
- split: part_202311
path: data/part_202311-*
- split: part_202310
path: data/part_202310-*
- split: part_202309
path: data/part_202309-*
---
|
FanChen0116/bus_few4_8x_pvi | ---
dataset_info:
features:
- name: id
dtype: int64
- name: tokens
sequence: string
- name: labels
sequence:
class_label:
names:
'0': O
'1': I-from_location
'2': B-from_location
'3': B-leaving_date
'4': I-leaving_date
'5': I-to_location
'6': B-to_location
- name: request_slot
sequence: string
splits:
- name: train
num_bytes: 68839
num_examples: 280
- name: validation
num_bytes: 6900
num_examples: 35
- name: test
num_bytes: 70618
num_examples: 377
download_size: 13438
dataset_size: 146357
---
# Dataset Card for "bus_few4_8x_pvi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lennardong/cells | ---
license: unknown
---
|
Razvan27/leading-comments-test | ---
dataset_info:
config_name: Test
features:
- name: comments
dtype: string
splits:
- name: train
num_bytes: 265
num_examples: 5
download_size: 1089
dataset_size: 265
configs:
- config_name: Test
data_files:
- split: train
path: data/Test/train-*
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
Gdacciaro/o_llam_napulitan-1k | ---
license: apache-2.0
---
|
jenpareto/product-photograph-test | ---
license: apache-2.0
---
|
dmrau/cqadubstack-programmers-qrels | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 45452
num_examples: 1675
download_size: 22632
dataset_size: 45452
---
# Dataset Card for "cqadubstack-programmers-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
barto17/gtzan_all_preprocessed | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': blues
'1': classical
'2': country
'3': disco
'4': hiphop
'5': jazz
'6': metal
'7': pop
'8': reggae
'9': rock
- name: input_values
sequence: float32
- name: attention_mask
sequence: int32
splits:
- name: train
num_bytes: 3452159816
num_examples: 899
- name: test
num_bytes: 384000696
num_examples: 100
download_size: 1923103923
dataset_size: 3836160512
---
# Dataset Card for "gtzan_all_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
andersonbcdefg/quora_triplets_with_margins | ---
dataset_info:
features:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
- name: source
dtype: string
- name: qp_sim
dtype: float32
- name: qn_sim
dtype: float32
- name: pn_sim
dtype: float32
- name: margin
dtype: float64
splits:
- name: train
num_bytes: 84326536.50607641
num_examples: 92677
download_size: 12123742
dataset_size: 84326536.50607641
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/takami_chika_lovelivesunshine | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of takami_chika/高海千歌/타카미치카 (Love Live! Sunshine!!)
This is the dataset of takami_chika/高海千歌/타카미치카 (Love Live! Sunshine!!), containing 500 images and their tags.
The core tags of this character are `orange_hair, ahoge, red_eyes, short_hair, bangs, braid, hair_ornament, bow, side_braid, hair_bow, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 739.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takami_chika_lovelivesunshine/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 381.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takami_chika_lovelivesunshine/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1243 | 862.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takami_chika_lovelivesunshine/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 634.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takami_chika_lovelivesunshine/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1243 | 1.27 GiB | [Download](https://huggingface.co/datasets/CyberHarem/takami_chika_lovelivesunshine/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/takami_chika_lovelivesunshine',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, serafuku, short_sleeves, solo, uranohoshi_school_uniform, neckerchief, pleated_skirt, cloud, grey_skirt, outdoors, day, ocean, clover_hair_ornament, blush, holding, open_mouth, :d, beach, blue_sky |
| 1 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, open_mouth, serafuku, smile, solo, uranohoshi_school_uniform, long_sleeves, upper_body |
| 2 | 11 |  |  |  |  |  | 1girl, serafuku, solo, uranohoshi_school_uniform, grey_skirt, long_sleeves, looking_at_viewer, pleated_skirt, red_bowtie, clover_hair_ornament, simple_background, white_background, yellow_bow, blush, grey_sailor_collar, :d, open_mouth, shirt, holding_fruit, medium_hair |
| 3 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, blush, hair_flower, white_dress, white_ribbon, earrings, hair_ribbon, cloud, elbow_gloves, open_mouth, white_gloves, sky, twintails, ocean |
| 4 | 5 |  |  |  |  |  | 1girl, character_name, english_text, looking_at_viewer, smile, solo, dated, happy_birthday, medium_breasts, collarbone, choker, sidelocks, thighhighs |
| 5 | 5 |  |  |  |  |  | 1girl, bowtie, bracelet, looking_at_viewer, open_mouth, short_sleeves, solo, :d, hair_flower, yellow_bow, blush, dress, apron, blue_shirt, frilled_sleeves, medium_breasts, skirt, upper_body |
| 6 | 5 |  |  |  |  |  | 1girl, earrings, long_sleeves, looking_at_viewer, solo, white_headwear, beret, miniskirt, open_mouth, :d, blue_bowtie, blue_jacket, blush, white_bow, white_skirt, blue_shirt, one_eye_closed, plaid, pleated_skirt, sailor_collar, sailor_hat, standing, striped_bowtie |
| 7 | 5 |  |  |  |  |  | 1girl, birthday, hair_ribbon, looking_at_viewer, solo, blue_feathers, feather_hair_ornament, upper_body, white_feathers, :d, blush, collarbone, open_mouth, signature, blue_choker, blue_ribbon, dress, shiny_hair |
| 8 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, solo, obi, open_mouth, blush, wide_sleeves, yukata, :d |
| 9 | 5 |  |  |  |  |  | 1girl, blush, bracelet, collarbone, looking_at_viewer, medium_breasts, nail_polish, navel, open_mouth, see-through, short_shorts, solo, :d, bikini_under_clothes, cleavage, striped_bikini, blue_shorts, outdoors, pink_bikini, side_ponytail, striped_shorts, blue_sky, character_name, day, earrings, english_text, groin, midriff, ocean, shiny_hair, shirt, thigh_strap, thighlet, yellow_bow |
| 10 | 5 |  |  |  |  |  | 1girl, collarbone, earrings, looking_at_viewer, medium_breasts, solo, bikini_under_clothes, see-through, short_shorts, smile, striped_bikini, blush, bracelet, cleavage, nail_polish, one_eye_closed, holding, orange_nails, sitting, thigh_strap, water_gun |
| 11 | 7 |  |  |  |  |  | 1girl, clothes_writing, facial_mark, hairband, jacket, long_sleeves, midriff, solo, tied_shirt, looking_at_viewer, navel, open_mouth, :d, black_shirt, fur_collar, miniskirt, star_earrings, bike_shorts, blush, boots, collarbone, frilled_sleeves, group_name, heart, ribbon, shorts_under_skirt, star_hair_ornament |
| 12 | 7 |  |  |  |  |  | 1girl, solo, looking_at_viewer, open_mouth, star_(symbol), striped, :d, double_bun, paw_gloves, blue_bow, blue_cape, earrings, fur-trimmed_cape, mini_crown, blush, bowtie, center_frills, cleavage, short_shorts, white_shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | serafuku | short_sleeves | solo | uranohoshi_school_uniform | neckerchief | pleated_skirt | cloud | grey_skirt | outdoors | day | ocean | clover_hair_ornament | blush | holding | open_mouth | :d | beach | blue_sky | smile | long_sleeves | upper_body | red_bowtie | simple_background | white_background | yellow_bow | grey_sailor_collar | shirt | holding_fruit | medium_hair | hair_flower | white_dress | white_ribbon | earrings | hair_ribbon | elbow_gloves | white_gloves | sky | twintails | character_name | english_text | dated | happy_birthday | medium_breasts | collarbone | choker | sidelocks | thighhighs | bowtie | bracelet | dress | apron | blue_shirt | frilled_sleeves | skirt | white_headwear | beret | miniskirt | blue_bowtie | blue_jacket | white_bow | white_skirt | one_eye_closed | plaid | sailor_collar | sailor_hat | standing | striped_bowtie | birthday | blue_feathers | feather_hair_ornament | white_feathers | signature | blue_choker | blue_ribbon | shiny_hair | obi | wide_sleeves | yukata | nail_polish | navel | see-through | short_shorts | bikini_under_clothes | cleavage | striped_bikini | blue_shorts | pink_bikini | side_ponytail | striped_shorts | groin | midriff | thigh_strap | thighlet | orange_nails | sitting | water_gun | clothes_writing | facial_mark | hairband | jacket | tied_shirt | black_shirt | fur_collar | star_earrings | bike_shorts | boots | group_name | heart | ribbon | shorts_under_skirt | star_hair_ornament | star_(symbol) | striped | double_bun | paw_gloves | blue_bow | blue_cape | fur-trimmed_cape | mini_crown | center_frills | white_shirt |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------------|:-----------|:----------------|:-------|:----------------------------|:--------------|:----------------|:--------|:-------------|:-----------|:------|:--------|:-----------------------|:--------|:----------|:-------------|:-----|:--------|:-----------|:--------|:---------------|:-------------|:-------------|:--------------------|:-------------------|:-------------|:---------------------|:--------|:----------------|:--------------|:--------------|:--------------|:---------------|:-----------|:--------------|:---------------|:---------------|:------|:------------|:-----------------|:---------------|:--------|:-----------------|:-----------------|:-------------|:---------|:------------|:-------------|:---------|:-----------|:--------|:--------|:-------------|:------------------|:--------|:-----------------|:--------|:------------|:--------------|:--------------|:------------|:--------------|:-----------------|:--------|:----------------|:-------------|:-----------|:-----------------|:-----------|:----------------|:------------------------|:-----------------|:------------|:--------------|:--------------|:-------------|:------|:---------------|:---------|:--------------|:--------|:--------------|:---------------|:-----------------------|:-----------|:-----------------|:--------------|:--------------|:----------------|:-----------------|:--------|:----------|:--------------|:-----------|:---------------|:----------|:------------|:------------------|:--------------|:-----------|:---------|:-------------|:--------------|:-------------|:----------------|:--------------|:--------|:-------------|:--------|:---------|:---------------------|:---------------------|:----------------|:----------|:-------------|:-------------|:-----------|:------------|:-------------------|:-------------|:----------------|:--------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | | X | X | | | | | | | | | | | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 11 |  |  |  |  |  | X | X | X | | X | X | | X | | X | | | | X | X | | X | X | | | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 12 |  |  |  |  |  | X | X | | | X | | | | X | | | | X | | X | | X | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | X | | X | X | | | | | | | | | | X | | X | X | | | | | X | | | | X | | | | | X | | | | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | X | | | X | | | X | | | | | | | X | | X | X | | | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | X | | | X | | | | | | | | | | X | | X | X | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 6 |  |  |  |  |  | X | X | | | X | | | | | | | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | X | | | X | | | | | | X | X | X | | X | | X | X | | X | | | | | | | X | | X | | | | | | X | | | | | | X | X | | | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 10 | 5 |  |  |  |  |  | X | X | | | X | | | | | | | | | | X | X | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | X | X | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | | X | X | X | X | X | | | | | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 11 | 7 |  |  |  |  |  | X | X | | | X | | | | | | | | | | X | | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 12 | 7 |  |  |  |  |  | X | X | | | X | | | | | | | | | | X | | X | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
InHawK/chapter-dataset-for-sales-training | ---
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- conversational
dataset_info:
features:
- name: text
dtype: string
splits:
- name: Train
num_bytes: 289127.32854209444
num_examples: 909
download_size: 154998
dataset_size: 289127.32854209444
configs:
- config_name: default
data_files:
- split: Train
path: data/Train-*
---
|
liuyanchen1015/MULTI_VALUE_qqp_finna_future | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 198403
num_examples: 1028
- name: test
num_bytes: 1773000
num_examples: 9289
- name: train
num_bytes: 1769246
num_examples: 9240
download_size: 2180207
dataset_size: 3740649
---
# Dataset Card for "MULTI_VALUE_qqp_finna_future"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_allknowingroger__limyClown-7B-slerp | ---
pretty_name: Evaluation run of allknowingroger/limyClown-7B-slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [allknowingroger/limyClown-7B-slerp](https://huggingface.co/allknowingroger/limyClown-7B-slerp)\
\ 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_allknowingroger__limyClown-7B-slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-11T05:04:40.249516](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__limyClown-7B-slerp/blob/main/results_2024-04-11T05-04-40.249516.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\": 0.6508460436098903,\n\
\ \"acc_stderr\": 0.03205919193276248,\n \"acc_norm\": 0.6497629905974438,\n\
\ \"acc_norm_stderr\": 0.03273571318942837,\n \"mc1\": 0.631578947368421,\n\
\ \"mc1_stderr\": 0.016886551261046046,\n \"mc2\": 0.7803533049335688,\n\
\ \"mc2_stderr\": 0.013700436959385495\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7133105802047781,\n \"acc_stderr\": 0.013214986329274777,\n\
\ \"acc_norm\": 0.7286689419795221,\n \"acc_norm_stderr\": 0.012993807727545796\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.716988647679745,\n\
\ \"acc_stderr\": 0.0044954128683246065,\n \"acc_norm\": 0.8911571400119498,\n\
\ \"acc_norm_stderr\": 0.0031080545633521087\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249387,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249387\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\"\
: 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\
\ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\
\ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\
\ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.47368421052631576,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\
\ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.02535574126305527,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.02535574126305527\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\
\ \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n\
\ \"acc_norm_stderr\": 0.02341529343356853\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.03287666758603491,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.03287666758603491\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\"\
: 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\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\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\
\ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \
\ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3841059602649007,\n \"acc_stderr\": 0.03971301814719197,\n \"\
acc_norm\": 0.3841059602649007,\n \"acc_norm_stderr\": 0.03971301814719197\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8440366972477065,\n \"acc_stderr\": 0.01555580271359017,\n \"\
acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.01555580271359017\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455335,\n \"\
acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455335\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\
: 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\
\ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.41964285714285715,\n\
\ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8212005108556832,\n\
\ \"acc_stderr\": 0.013702643715368983,\n \"acc_norm\": 0.8212005108556832,\n\
\ \"acc_norm_stderr\": 0.013702643715368983\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\
\ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.42793296089385474,\n\
\ \"acc_stderr\": 0.01654788799741611,\n \"acc_norm\": 0.42793296089385474,\n\
\ \"acc_norm_stderr\": 0.01654788799741611\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818733,\n\
\ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818733\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600712995,\n\
\ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600712995\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.475177304964539,\n \"acc_stderr\": 0.02979071924382972,\n \
\ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.02979071924382972\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4758800521512386,\n\
\ \"acc_stderr\": 0.012755368722863935,\n \"acc_norm\": 0.4758800521512386,\n\
\ \"acc_norm_stderr\": 0.012755368722863935\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.02824568739146292,\n\
\ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.02824568739146292\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6797385620915033,\n \"acc_stderr\": 0.018875682938069443,\n \
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.018875682938069443\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784596,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.631578947368421,\n\
\ \"mc1_stderr\": 0.016886551261046046,\n \"mc2\": 0.7803533049335688,\n\
\ \"mc2_stderr\": 0.013700436959385495\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8500394632991318,\n \"acc_stderr\": 0.010034394804580809\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7050796057619408,\n \
\ \"acc_stderr\": 0.012560698010954769\n }\n}\n```"
repo_url: https://huggingface.co/allknowingroger/limyClown-7B-slerp
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|arc:challenge|25_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|gsm8k|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hellaswag|10_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-11T05-04-40.249516.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-11T05-04-40.249516.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- '**/details_harness|winogrande|5_2024-04-11T05-04-40.249516.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-11T05-04-40.249516.parquet'
- config_name: results
data_files:
- split: 2024_04_11T05_04_40.249516
path:
- results_2024-04-11T05-04-40.249516.parquet
- split: latest
path:
- results_2024-04-11T05-04-40.249516.parquet
---
# Dataset Card for Evaluation run of allknowingroger/limyClown-7B-slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [allknowingroger/limyClown-7B-slerp](https://huggingface.co/allknowingroger/limyClown-7B-slerp) 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_allknowingroger__limyClown-7B-slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-11T05:04:40.249516](https://huggingface.co/datasets/open-llm-leaderboard/details_allknowingroger__limyClown-7B-slerp/blob/main/results_2024-04-11T05-04-40.249516.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": 0.6508460436098903,
"acc_stderr": 0.03205919193276248,
"acc_norm": 0.6497629905974438,
"acc_norm_stderr": 0.03273571318942837,
"mc1": 0.631578947368421,
"mc1_stderr": 0.016886551261046046,
"mc2": 0.7803533049335688,
"mc2_stderr": 0.013700436959385495
},
"harness|arc:challenge|25": {
"acc": 0.7133105802047781,
"acc_stderr": 0.013214986329274777,
"acc_norm": 0.7286689419795221,
"acc_norm_stderr": 0.012993807727545796
},
"harness|hellaswag|10": {
"acc": 0.716988647679745,
"acc_stderr": 0.0044954128683246065,
"acc_norm": 0.8911571400119498,
"acc_norm_stderr": 0.0031080545633521087
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.02815283794249387,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249387
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5702127659574469,
"acc_stderr": 0.03236214467715564,
"acc_norm": 0.5702127659574469,
"acc_norm_stderr": 0.03236214467715564
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.47368421052631576,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4126984126984127,
"acc_stderr": 0.02535574126305527,
"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.02535574126305527
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.47619047619047616,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.47619047619047616,
"acc_norm_stderr": 0.04467062628403273
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7838709677419354,
"acc_stderr": 0.02341529343356853,
"acc_norm": 0.7838709677419354,
"acc_norm_stderr": 0.02341529343356853
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.03287666758603491,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.03287666758603491
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.797979797979798,
"acc_stderr": 0.02860620428922987,
"acc_norm": 0.797979797979798,
"acc_norm_stderr": 0.02860620428922987
},
"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": 0.6641025641025641,
"acc_stderr": 0.023946724741563976,
"acc_norm": 0.6641025641025641,
"acc_norm_stderr": 0.023946724741563976
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32222222222222224,
"acc_stderr": 0.028493465091028593,
"acc_norm": 0.32222222222222224,
"acc_norm_stderr": 0.028493465091028593
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6680672268907563,
"acc_stderr": 0.03058869701378364,
"acc_norm": 0.6680672268907563,
"acc_norm_stderr": 0.03058869701378364
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3841059602649007,
"acc_stderr": 0.03971301814719197,
"acc_norm": 0.3841059602649007,
"acc_norm_stderr": 0.03971301814719197
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8440366972477065,
"acc_stderr": 0.01555580271359017,
"acc_norm": 0.8440366972477065,
"acc_norm_stderr": 0.01555580271359017
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5046296296296297,
"acc_stderr": 0.03409825519163572,
"acc_norm": 0.5046296296296297,
"acc_norm_stderr": 0.03409825519163572
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8431372549019608,
"acc_stderr": 0.02552472232455335,
"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.02552472232455335
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.810126582278481,
"acc_stderr": 0.02553010046023349,
"acc_norm": 0.810126582278481,
"acc_norm_stderr": 0.02553010046023349
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.034981493854624714,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.034981493854624714
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.768595041322314,
"acc_stderr": 0.03849856098794088,
"acc_norm": 0.768595041322314,
"acc_norm_stderr": 0.03849856098794088
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252626,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252626
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7852760736196319,
"acc_stderr": 0.032262193772867744,
"acc_norm": 0.7852760736196319,
"acc_norm_stderr": 0.032262193772867744
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.41964285714285715,
"acc_stderr": 0.04684099321077106,
"acc_norm": 0.41964285714285715,
"acc_norm_stderr": 0.04684099321077106
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8803418803418803,
"acc_stderr": 0.021262719400406964,
"acc_norm": 0.8803418803418803,
"acc_norm_stderr": 0.021262719400406964
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8212005108556832,
"acc_stderr": 0.013702643715368983,
"acc_norm": 0.8212005108556832,
"acc_norm_stderr": 0.013702643715368983
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7341040462427746,
"acc_stderr": 0.02378620325550829,
"acc_norm": 0.7341040462427746,
"acc_norm_stderr": 0.02378620325550829
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.42793296089385474,
"acc_stderr": 0.01654788799741611,
"acc_norm": 0.42793296089385474,
"acc_norm_stderr": 0.01654788799741611
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7189542483660131,
"acc_stderr": 0.025738854797818733,
"acc_norm": 0.7189542483660131,
"acc_norm_stderr": 0.025738854797818733
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.707395498392283,
"acc_stderr": 0.02583989833487798,
"acc_norm": 0.707395498392283,
"acc_norm_stderr": 0.02583989833487798
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7469135802469136,
"acc_stderr": 0.024191808600712995,
"acc_norm": 0.7469135802469136,
"acc_norm_stderr": 0.024191808600712995
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.475177304964539,
"acc_stderr": 0.02979071924382972,
"acc_norm": 0.475177304964539,
"acc_norm_stderr": 0.02979071924382972
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4758800521512386,
"acc_stderr": 0.012755368722863935,
"acc_norm": 0.4758800521512386,
"acc_norm_stderr": 0.012755368722863935
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6838235294117647,
"acc_stderr": 0.02824568739146292,
"acc_norm": 0.6838235294117647,
"acc_norm_stderr": 0.02824568739146292
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6797385620915033,
"acc_stderr": 0.018875682938069443,
"acc_norm": 0.6797385620915033,
"acc_norm_stderr": 0.018875682938069443
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7346938775510204,
"acc_stderr": 0.028263889943784596,
"acc_norm": 0.7346938775510204,
"acc_norm_stderr": 0.028263889943784596
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.835820895522388,
"acc_stderr": 0.026193923544454125,
"acc_norm": 0.835820895522388,
"acc_norm_stderr": 0.026193923544454125
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
"acc_stderr": 0.03869543323472101,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.631578947368421,
"mc1_stderr": 0.016886551261046046,
"mc2": 0.7803533049335688,
"mc2_stderr": 0.013700436959385495
},
"harness|winogrande|5": {
"acc": 0.8500394632991318,
"acc_stderr": 0.010034394804580809
},
"harness|gsm8k|5": {
"acc": 0.7050796057619408,
"acc_stderr": 0.012560698010954769
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
zolak/twitter_dataset_81_1713073656 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 3440301
num_examples: 8694
download_size: 1730508
dataset_size: 3440301
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
nlpai-lab/openassistant-guanaco-ko | ---
license: apache-2.0
task_categories:
- text-generation
- question-answering
- summarization
language:
- ko
size_categories:
- 1K<n<10K
---
### Dataset Summary
Korean translation of Guanaco via the DeepL API
Note: There are cases where multilingual data has been converted to monolingual data during batch translation to Korean using the API.
Below is Guanaco's README.
----
This dataset is a subset of the Open Assistant dataset, which you can find here: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main
This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
This dataset was used to train Guanaco with QLoRA.
For further information, please see the original dataset.
License: Apache 2.0 |
DBQ/Fendi.Product.prices.Singapore | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Singapore - Fendi - Product-level price list
tags:
- webscraping
- ecommerce
- Fendi
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 566974
num_examples: 1450
download_size: 187873
dataset_size: 566974
---
# Fendi web scraped data
## About the website
The luxury goods industry is in a dynamic state of expansion and growth in the Asia Pacific region. At the forefront of this trend is **Singapore**, a key player in this sector. The city-state is a hub for high-end fashion brands such as **Fendi**. Notably, **Fendi in Singapore** has widely adopted the use of technology, launching innovative digital campaigns and making excellent use of **Ecommerce**. Moreover, **Product-List Page (PLP) data** have played a significant role in the companys operations, allowing them to track consumer behavior and preferences, further tailoring their offerings to the discerning tastes of their customers.
## Link to **dataset**
[Singapore - Fendi - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Fendi%20Product-prices%20Singapore/r/receP8aWwUPlSSsvo)
|
open-llm-leaderboard/details_ikala__bloom-zh-3b-chat | ---
pretty_name: Evaluation run of ikala/bloom-zh-3b-chat
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ikala/bloom-zh-3b-chat](https://huggingface.co/ikala/bloom-zh-3b-chat) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ikala__bloom-zh-3b-chat\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T18:43:41.397434](https://huggingface.co/datasets/open-llm-leaderboard/details_ikala__bloom-zh-3b-chat/blob/main/results_2023-09-17T18-43-41.397434.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.08022231543624161,\n\
\ \"em_stderr\": 0.0027818178017908015,\n \"f1\": 0.1465918624161071,\n\
\ \"f1_stderr\": 0.003030605237968897,\n \"acc\": 0.2954867628904967,\n\
\ \"acc_stderr\": 0.007847263403599461\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.08022231543624161,\n \"em_stderr\": 0.0027818178017908015,\n\
\ \"f1\": 0.1465918624161071,\n \"f1_stderr\": 0.003030605237968897\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.004548900682335102,\n \
\ \"acc_stderr\": 0.0018535550440036198\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5864246250986582,\n \"acc_stderr\": 0.013840971763195304\n\
\ }\n}\n```"
repo_url: https://huggingface.co/ikala/bloom-zh-3b-chat
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T18_43_41.397434
path:
- '**/details_harness|drop|3_2023-09-17T18-43-41.397434.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T18-43-41.397434.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T18_43_41.397434
path:
- '**/details_harness|gsm8k|5_2023-09-17T18-43-41.397434.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T18-43-41.397434.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T18_43_41.397434
path:
- '**/details_harness|winogrande|5_2023-09-17T18-43-41.397434.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T18-43-41.397434.parquet'
- config_name: results
data_files:
- split: 2023_09_17T18_43_41.397434
path:
- results_2023-09-17T18-43-41.397434.parquet
- split: latest
path:
- results_2023-09-17T18-43-41.397434.parquet
---
# Dataset Card for Evaluation run of ikala/bloom-zh-3b-chat
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ikala/bloom-zh-3b-chat
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [ikala/bloom-zh-3b-chat](https://huggingface.co/ikala/bloom-zh-3b-chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ikala__bloom-zh-3b-chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T18:43:41.397434](https://huggingface.co/datasets/open-llm-leaderboard/details_ikala__bloom-zh-3b-chat/blob/main/results_2023-09-17T18-43-41.397434.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.08022231543624161,
"em_stderr": 0.0027818178017908015,
"f1": 0.1465918624161071,
"f1_stderr": 0.003030605237968897,
"acc": 0.2954867628904967,
"acc_stderr": 0.007847263403599461
},
"harness|drop|3": {
"em": 0.08022231543624161,
"em_stderr": 0.0027818178017908015,
"f1": 0.1465918624161071,
"f1_stderr": 0.003030605237968897
},
"harness|gsm8k|5": {
"acc": 0.004548900682335102,
"acc_stderr": 0.0018535550440036198
},
"harness|winogrande|5": {
"acc": 0.5864246250986582,
"acc_stderr": 0.013840971763195304
}
}
```
### 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] |
open-llm-leaderboard/details_Felladrin__Minueza-32M-Base | ---
pretty_name: Evaluation run of Felladrin/Minueza-32M-Base
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Felladrin/Minueza-32M-Base](https://huggingface.co/Felladrin/Minueza-32M-Base)\
\ 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_Felladrin__Minueza-32M-Base\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-29T16:20:03.165457](https://huggingface.co/datasets/open-llm-leaderboard/details_Felladrin__Minueza-32M-Base/blob/main/results_2024-02-29T16-20-03.165457.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\": 0.24752258552248374,\n\
\ \"acc_stderr\": 0.030252830829249223,\n \"acc_norm\": 0.24764798996528117,\n\
\ \"acc_norm_stderr\": 0.03102991624039624,\n \"mc1\": 0.26805385556915545,\n\
\ \"mc1_stderr\": 0.015506204722834553,\n \"mc2\": 0.47454377774666195,\n\
\ \"mc2_stderr\": 0.015653794086918325\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.1680887372013652,\n \"acc_stderr\": 0.010927715046124858,\n\
\ \"acc_norm\": 0.21331058020477817,\n \"acc_norm_stderr\": 0.011970971742326334\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2610037841067516,\n\
\ \"acc_stderr\": 0.004382844128643407,\n \"acc_norm\": 0.2638916550487951,\n\
\ \"acc_norm_stderr\": 0.00439840499293385\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2740740740740741,\n\
\ \"acc_stderr\": 0.03853254836552003,\n \"acc_norm\": 0.2740740740740741,\n\
\ \"acc_norm_stderr\": 0.03853254836552003\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.19078947368421054,\n \"acc_stderr\": 0.031975658210325,\n\
\ \"acc_norm\": 0.19078947368421054,\n \"acc_norm_stderr\": 0.031975658210325\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.2,\n\
\ \"acc_stderr\": 0.040201512610368445,\n \"acc_norm\": 0.2,\n \
\ \"acc_norm_stderr\": 0.040201512610368445\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.2188679245283019,\n \"acc_stderr\": 0.02544786382510861,\n\
\ \"acc_norm\": 0.2188679245283019,\n \"acc_norm_stderr\": 0.02544786382510861\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.19444444444444445,\n\
\ \"acc_stderr\": 0.03309615177059004,\n \"acc_norm\": 0.19444444444444445,\n\
\ \"acc_norm_stderr\": 0.03309615177059004\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.15,\n \"acc_stderr\": 0.035887028128263714,\n \
\ \"acc_norm\": 0.15,\n \"acc_norm_stderr\": 0.035887028128263714\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \
\ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.1791907514450867,\n\
\ \"acc_stderr\": 0.029242513059063294,\n \"acc_norm\": 0.1791907514450867,\n\
\ \"acc_norm_stderr\": 0.029242513059063294\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.16666666666666666,\n \"acc_stderr\": 0.03708284662416542,\n\
\ \"acc_norm\": 0.16666666666666666,\n \"acc_norm_stderr\": 0.03708284662416542\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.17,\n\
\ \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.2680851063829787,\n \"acc_stderr\": 0.028957342788342343,\n\
\ \"acc_norm\": 0.2680851063829787,\n \"acc_norm_stderr\": 0.028957342788342343\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n\
\ \"acc_stderr\": 0.04049339297748141,\n \"acc_norm\": 0.24561403508771928,\n\
\ \"acc_norm_stderr\": 0.04049339297748141\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2206896551724138,\n \"acc_stderr\": 0.03455930201924811,\n\
\ \"acc_norm\": 0.2206896551724138,\n \"acc_norm_stderr\": 0.03455930201924811\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"\
acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15079365079365079,\n\
\ \"acc_stderr\": 0.03200686497287392,\n \"acc_norm\": 0.15079365079365079,\n\
\ \"acc_norm_stderr\": 0.03200686497287392\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3161290322580645,\n\
\ \"acc_stderr\": 0.02645087448904277,\n \"acc_norm\": 0.3161290322580645,\n\
\ \"acc_norm_stderr\": 0.02645087448904277\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.2955665024630542,\n \"acc_stderr\": 0.032104944337514575,\n\
\ \"acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.032104944337514575\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952344,\n \"acc_norm\"\
: 0.34,\n \"acc_norm_stderr\": 0.047609522856952344\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.2787878787878788,\n \"acc_stderr\": 0.035014387062967806,\n\
\ \"acc_norm\": 0.2787878787878788,\n \"acc_norm_stderr\": 0.035014387062967806\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.23232323232323232,\n \"acc_stderr\": 0.030088629490217483,\n \"\
acc_norm\": 0.23232323232323232,\n \"acc_norm_stderr\": 0.030088629490217483\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.21761658031088082,\n \"acc_stderr\": 0.02977866303775296,\n\
\ \"acc_norm\": 0.21761658031088082,\n \"acc_norm_stderr\": 0.02977866303775296\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.27692307692307694,\n \"acc_stderr\": 0.022688042352424994,\n\
\ \"acc_norm\": 0.27692307692307694,\n \"acc_norm_stderr\": 0.022688042352424994\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.24814814814814815,\n \"acc_stderr\": 0.026335739404055803,\n \
\ \"acc_norm\": 0.24814814814814815,\n \"acc_norm_stderr\": 0.026335739404055803\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.36554621848739494,\n \"acc_stderr\": 0.031282177063684614,\n\
\ \"acc_norm\": 0.36554621848739494,\n \"acc_norm_stderr\": 0.031282177063684614\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2251655629139073,\n \"acc_stderr\": 0.03410435282008936,\n \"\
acc_norm\": 0.2251655629139073,\n \"acc_norm_stderr\": 0.03410435282008936\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.22201834862385322,\n \"acc_stderr\": 0.017818849564796624,\n \"\
acc_norm\": 0.22201834862385322,\n \"acc_norm_stderr\": 0.017818849564796624\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\
: 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.2549019607843137,\n\
\ \"acc_stderr\": 0.030587591351604246,\n \"acc_norm\": 0.2549019607843137,\n\
\ \"acc_norm_stderr\": 0.030587591351604246\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.25316455696202533,\n \"acc_stderr\": 0.028304657943035296,\n\
\ \"acc_norm\": 0.25316455696202533,\n \"acc_norm_stderr\": 0.028304657943035296\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.21973094170403587,\n\
\ \"acc_stderr\": 0.027790177064383605,\n \"acc_norm\": 0.21973094170403587,\n\
\ \"acc_norm_stderr\": 0.027790177064383605\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.25190839694656486,\n \"acc_stderr\": 0.03807387116306086,\n\
\ \"acc_norm\": 0.25190839694656486,\n \"acc_norm_stderr\": 0.03807387116306086\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.24793388429752067,\n \"acc_stderr\": 0.03941897526516303,\n \"\
acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.03941897526516303\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n\
\ \"acc_stderr\": 0.03957835471980979,\n \"acc_norm\": 0.21296296296296297,\n\
\ \"acc_norm_stderr\": 0.03957835471980979\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.03259177392742178,\n\
\ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.03259177392742178\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.23214285714285715,\n\
\ \"acc_stderr\": 0.040073418097558065,\n \"acc_norm\": 0.23214285714285715,\n\
\ \"acc_norm_stderr\": 0.040073418097558065\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\
\ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.19658119658119658,\n\
\ \"acc_stderr\": 0.02603538609895129,\n \"acc_norm\": 0.19658119658119658,\n\
\ \"acc_norm_stderr\": 0.02603538609895129\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2835249042145594,\n\
\ \"acc_stderr\": 0.016117318166832272,\n \"acc_norm\": 0.2835249042145594,\n\
\ \"acc_norm_stderr\": 0.016117318166832272\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.02317629820399201,\n\
\ \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.02317629820399201\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24804469273743016,\n\
\ \"acc_stderr\": 0.014444157808261431,\n \"acc_norm\": 0.24804469273743016,\n\
\ \"acc_norm_stderr\": 0.014444157808261431\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.238562091503268,\n \"acc_stderr\": 0.024404394928087873,\n\
\ \"acc_norm\": 0.238562091503268,\n \"acc_norm_stderr\": 0.024404394928087873\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2990353697749196,\n\
\ \"acc_stderr\": 0.026003301117885135,\n \"acc_norm\": 0.2990353697749196,\n\
\ \"acc_norm_stderr\": 0.026003301117885135\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.27469135802469136,\n \"acc_stderr\": 0.024836057868294677,\n\
\ \"acc_norm\": 0.27469135802469136,\n \"acc_norm_stderr\": 0.024836057868294677\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.26595744680851063,\n \"acc_stderr\": 0.026358065698880592,\n \
\ \"acc_norm\": 0.26595744680851063,\n \"acc_norm_stderr\": 0.026358065698880592\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.23859191655801826,\n\
\ \"acc_stderr\": 0.010885929742002221,\n \"acc_norm\": 0.23859191655801826,\n\
\ \"acc_norm_stderr\": 0.010885929742002221\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4485294117647059,\n \"acc_stderr\": 0.030211479609121593,\n\
\ \"acc_norm\": 0.4485294117647059,\n \"acc_norm_stderr\": 0.030211479609121593\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2238562091503268,\n \"acc_stderr\": 0.016863008585416617,\n \
\ \"acc_norm\": 0.2238562091503268,\n \"acc_norm_stderr\": 0.016863008585416617\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.22727272727272727,\n\
\ \"acc_stderr\": 0.04013964554072774,\n \"acc_norm\": 0.22727272727272727,\n\
\ \"acc_norm_stderr\": 0.04013964554072774\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.23673469387755103,\n \"acc_stderr\": 0.02721283588407316,\n\
\ \"acc_norm\": 0.23673469387755103,\n \"acc_norm_stderr\": 0.02721283588407316\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23880597014925373,\n\
\ \"acc_stderr\": 0.030147775935409224,\n \"acc_norm\": 0.23880597014925373,\n\
\ \"acc_norm_stderr\": 0.030147775935409224\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.25301204819277107,\n\
\ \"acc_stderr\": 0.03384429155233135,\n \"acc_norm\": 0.25301204819277107,\n\
\ \"acc_norm_stderr\": 0.03384429155233135\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.0312678171466318,\n\
\ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.0312678171466318\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.26805385556915545,\n\
\ \"mc1_stderr\": 0.015506204722834553,\n \"mc2\": 0.47454377774666195,\n\
\ \"mc2_stderr\": 0.015653794086918325\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.531965272296764,\n \"acc_stderr\": 0.014023739221166386\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0037907505686125853,\n \
\ \"acc_stderr\": 0.001692700740150178\n }\n}\n```"
repo_url: https://huggingface.co/Felladrin/Minueza-32M-Base
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|arc:challenge|25_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|gsm8k|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hellaswag|10_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T16-20-03.165457.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-29T16-20-03.165457.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- '**/details_harness|winogrande|5_2024-02-29T16-20-03.165457.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-29T16-20-03.165457.parquet'
- config_name: results
data_files:
- split: 2024_02_29T16_20_03.165457
path:
- results_2024-02-29T16-20-03.165457.parquet
- split: latest
path:
- results_2024-02-29T16-20-03.165457.parquet
---
# Dataset Card for Evaluation run of Felladrin/Minueza-32M-Base
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Felladrin/Minueza-32M-Base](https://huggingface.co/Felladrin/Minueza-32M-Base) 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_Felladrin__Minueza-32M-Base",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-29T16:20:03.165457](https://huggingface.co/datasets/open-llm-leaderboard/details_Felladrin__Minueza-32M-Base/blob/main/results_2024-02-29T16-20-03.165457.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": 0.24752258552248374,
"acc_stderr": 0.030252830829249223,
"acc_norm": 0.24764798996528117,
"acc_norm_stderr": 0.03102991624039624,
"mc1": 0.26805385556915545,
"mc1_stderr": 0.015506204722834553,
"mc2": 0.47454377774666195,
"mc2_stderr": 0.015653794086918325
},
"harness|arc:challenge|25": {
"acc": 0.1680887372013652,
"acc_stderr": 0.010927715046124858,
"acc_norm": 0.21331058020477817,
"acc_norm_stderr": 0.011970971742326334
},
"harness|hellaswag|10": {
"acc": 0.2610037841067516,
"acc_stderr": 0.004382844128643407,
"acc_norm": 0.2638916550487951,
"acc_norm_stderr": 0.00439840499293385
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2740740740740741,
"acc_stderr": 0.03853254836552003,
"acc_norm": 0.2740740740740741,
"acc_norm_stderr": 0.03853254836552003
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.19078947368421054,
"acc_stderr": 0.031975658210325,
"acc_norm": 0.19078947368421054,
"acc_norm_stderr": 0.031975658210325
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.2,
"acc_stderr": 0.040201512610368445,
"acc_norm": 0.2,
"acc_norm_stderr": 0.040201512610368445
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2188679245283019,
"acc_stderr": 0.02544786382510861,
"acc_norm": 0.2188679245283019,
"acc_norm_stderr": 0.02544786382510861
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.19444444444444445,
"acc_stderr": 0.03309615177059004,
"acc_norm": 0.19444444444444445,
"acc_norm_stderr": 0.03309615177059004
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.15,
"acc_stderr": 0.035887028128263714,
"acc_norm": 0.15,
"acc_norm_stderr": 0.035887028128263714
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.1791907514450867,
"acc_stderr": 0.029242513059063294,
"acc_norm": 0.1791907514450867,
"acc_norm_stderr": 0.029242513059063294
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.16666666666666666,
"acc_stderr": 0.03708284662416542,
"acc_norm": 0.16666666666666666,
"acc_norm_stderr": 0.03708284662416542
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.17,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.17,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.2680851063829787,
"acc_stderr": 0.028957342788342343,
"acc_norm": 0.2680851063829787,
"acc_norm_stderr": 0.028957342788342343
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.24561403508771928,
"acc_stderr": 0.04049339297748141,
"acc_norm": 0.24561403508771928,
"acc_norm_stderr": 0.04049339297748141
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2206896551724138,
"acc_stderr": 0.03455930201924811,
"acc_norm": 0.2206896551724138,
"acc_norm_stderr": 0.03455930201924811
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2566137566137566,
"acc_stderr": 0.022494510767503154,
"acc_norm": 0.2566137566137566,
"acc_norm_stderr": 0.022494510767503154
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.15079365079365079,
"acc_stderr": 0.03200686497287392,
"acc_norm": 0.15079365079365079,
"acc_norm_stderr": 0.03200686497287392
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.3161290322580645,
"acc_stderr": 0.02645087448904277,
"acc_norm": 0.3161290322580645,
"acc_norm_stderr": 0.02645087448904277
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.2955665024630542,
"acc_stderr": 0.032104944337514575,
"acc_norm": 0.2955665024630542,
"acc_norm_stderr": 0.032104944337514575
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952344,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952344
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.2787878787878788,
"acc_stderr": 0.035014387062967806,
"acc_norm": 0.2787878787878788,
"acc_norm_stderr": 0.035014387062967806
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.23232323232323232,
"acc_stderr": 0.030088629490217483,
"acc_norm": 0.23232323232323232,
"acc_norm_stderr": 0.030088629490217483
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.21761658031088082,
"acc_stderr": 0.02977866303775296,
"acc_norm": 0.21761658031088082,
"acc_norm_stderr": 0.02977866303775296
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.27692307692307694,
"acc_stderr": 0.022688042352424994,
"acc_norm": 0.27692307692307694,
"acc_norm_stderr": 0.022688042352424994
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.24814814814814815,
"acc_stderr": 0.026335739404055803,
"acc_norm": 0.24814814814814815,
"acc_norm_stderr": 0.026335739404055803
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.36554621848739494,
"acc_stderr": 0.031282177063684614,
"acc_norm": 0.36554621848739494,
"acc_norm_stderr": 0.031282177063684614
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2251655629139073,
"acc_stderr": 0.03410435282008936,
"acc_norm": 0.2251655629139073,
"acc_norm_stderr": 0.03410435282008936
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.22201834862385322,
"acc_stderr": 0.017818849564796624,
"acc_norm": 0.22201834862385322,
"acc_norm_stderr": 0.017818849564796624
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4722222222222222,
"acc_stderr": 0.0340470532865388,
"acc_norm": 0.4722222222222222,
"acc_norm_stderr": 0.0340470532865388
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.2549019607843137,
"acc_stderr": 0.030587591351604246,
"acc_norm": 0.2549019607843137,
"acc_norm_stderr": 0.030587591351604246
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.25316455696202533,
"acc_stderr": 0.028304657943035296,
"acc_norm": 0.25316455696202533,
"acc_norm_stderr": 0.028304657943035296
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.21973094170403587,
"acc_stderr": 0.027790177064383605,
"acc_norm": 0.21973094170403587,
"acc_norm_stderr": 0.027790177064383605
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.25190839694656486,
"acc_stderr": 0.03807387116306086,
"acc_norm": 0.25190839694656486,
"acc_norm_stderr": 0.03807387116306086
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.24793388429752067,
"acc_stderr": 0.03941897526516303,
"acc_norm": 0.24793388429752067,
"acc_norm_stderr": 0.03941897526516303
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.21296296296296297,
"acc_stderr": 0.03957835471980979,
"acc_norm": 0.21296296296296297,
"acc_norm_stderr": 0.03957835471980979
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.22085889570552147,
"acc_stderr": 0.03259177392742178,
"acc_norm": 0.22085889570552147,
"acc_norm_stderr": 0.03259177392742178
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.23214285714285715,
"acc_stderr": 0.040073418097558065,
"acc_norm": 0.23214285714285715,
"acc_norm_stderr": 0.040073418097558065
},
"harness|hendrycksTest-management|5": {
"acc": 0.17475728155339806,
"acc_stderr": 0.037601780060266224,
"acc_norm": 0.17475728155339806,
"acc_norm_stderr": 0.037601780060266224
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.19658119658119658,
"acc_stderr": 0.02603538609895129,
"acc_norm": 0.19658119658119658,
"acc_norm_stderr": 0.02603538609895129
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.2835249042145594,
"acc_stderr": 0.016117318166832272,
"acc_norm": 0.2835249042145594,
"acc_norm_stderr": 0.016117318166832272
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.24566473988439305,
"acc_stderr": 0.02317629820399201,
"acc_norm": 0.24566473988439305,
"acc_norm_stderr": 0.02317629820399201
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24804469273743016,
"acc_stderr": 0.014444157808261431,
"acc_norm": 0.24804469273743016,
"acc_norm_stderr": 0.014444157808261431
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.238562091503268,
"acc_stderr": 0.024404394928087873,
"acc_norm": 0.238562091503268,
"acc_norm_stderr": 0.024404394928087873
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.2990353697749196,
"acc_stderr": 0.026003301117885135,
"acc_norm": 0.2990353697749196,
"acc_norm_stderr": 0.026003301117885135
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.27469135802469136,
"acc_stderr": 0.024836057868294677,
"acc_norm": 0.27469135802469136,
"acc_norm_stderr": 0.024836057868294677
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.26595744680851063,
"acc_stderr": 0.026358065698880592,
"acc_norm": 0.26595744680851063,
"acc_norm_stderr": 0.026358065698880592
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.23859191655801826,
"acc_stderr": 0.010885929742002221,
"acc_norm": 0.23859191655801826,
"acc_norm_stderr": 0.010885929742002221
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4485294117647059,
"acc_stderr": 0.030211479609121593,
"acc_norm": 0.4485294117647059,
"acc_norm_stderr": 0.030211479609121593
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.2238562091503268,
"acc_stderr": 0.016863008585416617,
"acc_norm": 0.2238562091503268,
"acc_norm_stderr": 0.016863008585416617
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.22727272727272727,
"acc_stderr": 0.04013964554072774,
"acc_norm": 0.22727272727272727,
"acc_norm_stderr": 0.04013964554072774
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.23673469387755103,
"acc_stderr": 0.02721283588407316,
"acc_norm": 0.23673469387755103,
"acc_norm_stderr": 0.02721283588407316
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.23880597014925373,
"acc_stderr": 0.030147775935409224,
"acc_norm": 0.23880597014925373,
"acc_norm_stderr": 0.030147775935409224
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-virology|5": {
"acc": 0.25301204819277107,
"acc_stderr": 0.03384429155233135,
"acc_norm": 0.25301204819277107,
"acc_norm_stderr": 0.03384429155233135
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.21052631578947367,
"acc_stderr": 0.0312678171466318,
"acc_norm": 0.21052631578947367,
"acc_norm_stderr": 0.0312678171466318
},
"harness|truthfulqa:mc|0": {
"mc1": 0.26805385556915545,
"mc1_stderr": 0.015506204722834553,
"mc2": 0.47454377774666195,
"mc2_stderr": 0.015653794086918325
},
"harness|winogrande|5": {
"acc": 0.531965272296764,
"acc_stderr": 0.014023739221166386
},
"harness|gsm8k|5": {
"acc": 0.0037907505686125853,
"acc_stderr": 0.001692700740150178
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
bayuncao/cwec-v4.14-weaknesses-1.0 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
pretty_name: cwe-weaknesses
size_categories:
- n<1K
---
## Introduction
This dataset is based on the complete XML file of [CWE List Version 4.14](https://cwe.mitre.org/data/index.html) and is intended to provide researchers and security experts with structured data on Common Weakness Enumeration (CWE) for software and hardware. The dataset contains **963** entries in Alpaca format, each providing detailed information about a specific weakness.
## Dataset Structure
Each entry in the dataset includes the following fields:
*ID*: The unique identifier for the weakness (e.g., CWE-1004).
*Name*: The name of the weakness, describing the essence of the issue (e.g., Sensitive Cookie Without 'HttpOnly' Flag).
*Abstraction*: The level of abstraction indicating the conceptual level of the weakness (e.g., Variant).
*Structure*: The type of weakness structure (e.g., Simple).
*Status*: The completion status of the weakness description (e.g., Incomplete).
*Description*: A brief description of the issue regarding sensitive cookies without the HttpOnly flag.
*Extended Description*: Provides a more detailed description of the issue, explaining the role of the HttpOnly flag and the security risks associated with not using it.
*Related Weaknesses*: Describes other weaknesses related to this one.
*Applicable Platforms*: Describes the programming languages and technology platforms to which this weakness applies.
*Background Details*: Provides background information about HTTP cookies, explaining how cookies work and their purposes.
*Modes Of Introduction*: Describes the phases during the software development cycle in which this weakness may be introduced.
*Likelihood Of Exploit*: Indicates the likelihood that this weakness will be exploited (e.g., Medium).
*Common Consequences*: Describes the potential impacts on the system if this weakness is exploited.
*Detection Methods*: Describes methods for detecting the presence of this weakness.
*Potential Mitigations*: Provides recommended measures for mitigating this weakness.
*Demonstrative Examples*: Provides example code to demonstrate this weakness and how it can be mitigated.
*Observed Examples*: Lists observed instances of this kind of weakness in the real world, including related CVE numbers.
*References*: Provides links to related reference materials.
*Mapping Notes*: Contains notes on the mapping and use of this weakness entry.
*Content History*: Provides the historical revision record of the content of this weakness description.
## Usage Instructions
This dataset is suitable for security research, educational training, tool development, and more. Users can directly load the dataset via the Hugging Face Datasets library for analysis and research.
```python
Copy code
from datasets import load_dataset
dataset = load_dataset("bayuncao/cwec-v4.14-weaknesses-1.0")
``` |
confit/emodb | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: emotion
dtype: string
- name: label
dtype:
class_label:
names:
'0': anxiety
'1': disgust
'2': happiness
'3': boredom
'4': neutral
'5': sadness
'6': anger
splits:
- name: train
num_bytes: 26772110
num_examples: 304
- name: test
num_bytes: 20866781
num_examples: 231
download_size: 46818101
dataset_size: 47638891
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- audio-classification
tags:
- audio
- paralinguistic
- multiclass
---
# EmoDB
The EmoDB is the freely available German emotional database, containing a total of 535 utterances.
It comprises of seven emotions: 1) anger; 2) boredom; 3) anxiety; 4) happiness; 5) sadness; 6) disgust; and 7) neutral.
The data was recorded at a 48-kHz sampling rate and then down-sampled to 16-kHz.
We follow the unofficial speaker-independent train/test split from [here](https://github.com/audeering/emodb/blob/master/CHANGELOG.md).
## Citations
```bibtex
@inproceedings{burkhardt2005database,
title={A database of German emotional speech.},
author={Burkhardt, Felix and Paeschke, Astrid and Rolfes, Miriam and Sendlmeier, Walter F and Weiss, Benjamin and others},
booktitle={Interspeech},
volume={5},
pages={1517--1520},
year={2005}
}
``` |
simonycl/multi-task | ---
dataset_info:
features:
- name: dataset
dtype: string
- name: id
dtype: string
- name: messages
list:
- name: role
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 168736978
num_examples: 100000
download_size: 78501687
dataset_size: 168736978
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
JuliaGL/code_dataset_llama2 | ---
dataset_info:
features:
- name: message
dtype: string
splits:
- name: train
num_bytes: 28212608
num_examples: 4000
- name: test
num_bytes: 7013719
num_examples: 1000
download_size: 15105165
dataset_size: 35226327
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
tyzhu/squad_qa_no_id_v5_full_random_permute_4 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: context_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 6649828.427453769
num_examples: 4345
- name: validation
num_bytes: 342766
num_examples: 300
download_size: 1347848
dataset_size: 6992594.427453769
---
# Dataset Card for "squad_qa_no_id_v5_full_random_permute_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CATIE-AQ/termith-eval_fr_prompt_data_to_text | ---
language:
- fr
license:
- cc-by-4.0
size_categories:
- 10K<n<100K
task_categories:
- text-generation
tags:
- data-to-text
- DFP
- french prompts
annotations_creators:
- found
language_creators:
- found
multilinguality:
- monolingual
source_datasets:
- taln-ls2n/termith-eval
---
# termith-eval_fr_prompt_data_to_text
## Summary
**termith-eval_fr_prompt_data_to_text** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).
It contains **11,886** rows that can be used for a data-to-text task.
The original data (without prompts) comes from the dataset [termith-eval](https://huggingface.co/datasets/taln-ls2n/termith-eval).
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al.
## Prompts used
### List
30 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.
```
'Assembler les concepts suivants pour former une phrase : "'+concepts+'".',
'Assemble les concepts suivants pour former une phrase : "'+concepts+'".',
'Assemblez les concepts suivants pour former une phrase : "'+concepts+'".',
'Étant donné la liste des concepts : "'+concepts+'". Générer une phrase avec tous les concepts : ',
'Étant donné la liste des concepts : "'+concepts+'". Génère une phrase avec tous les concepts : ',
'Étant donné la liste des concepts : "'+concepts+'". Générez une phrase avec tous les concepts : ',
'Convertir les concepts en une phrase : "'+concepts+'".',
'Convertis les concepts en une phrase : "'+concepts+'".',
'Convertissez les concepts en une phrase : "'+concepts+'".',
'Combiner tous les concepts suivants dans un texte concis et grammaticalement correct "'+concepts+'". Texte : ',
'Combine tous les concepts suivants dans un texte concis et grammaticalement correct "'+concepts+'". Texte : ',
'Combinez tous les concepts suivants dans un texte concis et grammaticalement correct "'+concepts+'". Texte : ',
'Générer une phrase à partir des informations fournies ci-contre : "'+concepts+'".',
'Génère une phrase à partir des informations fournies ci-contre : "'+concepts+'".',
'Générez une phrase à partir des informations fournies ci-contre : "'+concepts+'".',
'Verbaliser les concepts suivants séparés par une virgule : "'+concepts+'".',
'Verbalise les concepts suivants séparés par une virgule : "'+concepts+'".',
'Verbalisez les concepts suivants séparés par une virgule : "'+concepts+'".',
'Générer un texte intégrant les concepts suivants '+concepts+'". Texte :',
'Génère un texte intégrant les concepts suivants '+concepts+'". Texte :',
'Générez un texte intégrant les concepts suivants '+concepts+'". Texte :',
'"'+concepts+'". Ecrire 1 à 5 phrases sur les concepts précédents.',
'"'+concepts+'". Ecris 1 à 5 phrases sur les concepts précédents.',
'"'+concepts+'". Ecrivez 1 à 5 phrases sur les concepts précédents.',
'Rédiger un texte avec : "'+concepts+'".',
'Rédige un texte avec : "'+concepts+'".',
'Rédigez un texte avec : "'+concepts+'".',
'Écrire un texte sur les concepts suivants : "'+concepts+'".',
'Écris un texte sur les concepts suivants : "'+concepts+'".',
'Écrivez un texte sur les concepts suivants : "'+concepts+'".',
```
# Splits
- `train` with 11,886 samples
- no `valid` split
- no `test` split
# How to use?
```
from datasets import load_dataset
dataset = load_dataset("CATIE-AQ/termith-eval_fr_prompt_data_to_text")
```
# Citation
## Original data
> - (Boudin, 2013) Florian Boudin. 2013.
[TALN Archives : a digital archive of French research articles in Natural Language Processing (TALN Archives : une archive numérique francophone des articles de recherche en Traitement Automatique de la Langue) [in French]][boudin-2013].
In Proceedings of TALN 2013 (Volume 2: Short Papers), pages 507–514, Les Sables d’Olonne, France. ATALA.
>- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021].
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
[boudin-2013]: https://aclanthology.org/F13-2001/
[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/
## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,
author = { {Centre Aquitain des Technologies de l'Information et Electroniques} },
title = { DFP (Revision 1d24c09) },
year = 2023,
url = { https://huggingface.co/datasets/CATIE-AQ/DFP },
doi = { 10.57967/hf/1200 },
publisher = { Hugging Face }
}
## License
cc-by-4.0 |
aureliojafer/twitter_dataset_1709832136 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
splits:
- name: train
num_bytes: 61810
num_examples: 200
download_size: 39919
dataset_size: 61810
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
wbxlala/Dreamer_Dominance_shuffled | ---
dataset_info:
features:
- name: image
sequence:
sequence:
sequence: float64
- name: label
dtype: float64
splits:
- name: train
num_bytes: 499671504.0
num_examples: 414
download_size: 492768642
dataset_size: 499671504.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_YouKnwMe__Mistral-7B-Instruct-exp-e2 | ---
pretty_name: Evaluation run of YouKnwMe/Mistral-7B-Instruct-exp-e2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [YouKnwMe/Mistral-7B-Instruct-exp-e2](https://huggingface.co/YouKnwMe/Mistral-7B-Instruct-exp-e2)\
\ 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_YouKnwMe__Mistral-7B-Instruct-exp-e2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-26T17:27:37.810259](https://huggingface.co/datasets/open-llm-leaderboard/details_YouKnwMe__Mistral-7B-Instruct-exp-e2/blob/main/results_2024-01-26T17-27-37.810259.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\": 0.6558565508085182,\n\
\ \"acc_stderr\": 0.03205699333246102,\n \"acc_norm\": 0.6552801158659124,\n\
\ \"acc_norm_stderr\": 0.03272709560202178,\n \"mc1\": 0.5667074663402693,\n\
\ \"mc1_stderr\": 0.017347024450107478,\n \"mc2\": 0.7126457863777319,\n\
\ \"mc2_stderr\": 0.014796561609011638\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7022184300341296,\n \"acc_stderr\": 0.013363080107244484,\n\
\ \"acc_norm\": 0.7252559726962458,\n \"acc_norm_stderr\": 0.013044617212771227\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7127066321449911,\n\
\ \"acc_stderr\": 0.004515748192605716,\n \"acc_norm\": 0.8849830711013742,\n\
\ \"acc_norm_stderr\": 0.0031839033919416975\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\
\ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700914,\n\
\ \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700914\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\
: 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n\
\ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\
\ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287533,\n\
\ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287533\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\
\ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4312169312169312,\n \"acc_stderr\": 0.025506481698138208,\n \"\
acc_norm\": 0.4312169312169312,\n \"acc_norm_stderr\": 0.025506481698138208\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n\
\ \"acc_stderr\": 0.02328766512726854,\n \"acc_norm\": 0.7870967741935484,\n\
\ \"acc_norm_stderr\": 0.02328766512726854\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\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\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\
acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n\
\ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \
\ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \
\ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.0302839955258844,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.0302839955258844\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\
acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8440366972477065,\n \"acc_stderr\": 0.01555580271359017,\n \"\
acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.01555580271359017\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\
acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290902,\n \
\ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290902\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\
\ \"acc_stderr\": 0.04133119440243839,\n \"acc_norm\": 0.7592592592592593,\n\
\ \"acc_norm_stderr\": 0.04133119440243839\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406974,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406974\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8301404853128991,\n\
\ \"acc_stderr\": 0.013428186370608306,\n \"acc_norm\": 0.8301404853128991,\n\
\ \"acc_norm_stderr\": 0.013428186370608306\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.02353292543104429,\n\
\ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.02353292543104429\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.42793296089385474,\n\
\ \"acc_stderr\": 0.01654788799741611,\n \"acc_norm\": 0.42793296089385474,\n\
\ \"acc_norm_stderr\": 0.01654788799741611\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.02609016250427905,\n\
\ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.02609016250427905\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7234726688102894,\n\
\ \"acc_stderr\": 0.025403832978179615,\n \"acc_norm\": 0.7234726688102894,\n\
\ \"acc_norm_stderr\": 0.025403832978179615\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7561728395061729,\n \"acc_stderr\": 0.023891879541959607,\n\
\ \"acc_norm\": 0.7561728395061729,\n \"acc_norm_stderr\": 0.023891879541959607\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\": 0.4667535853976532,\n\
\ \"acc_stderr\": 0.012741974333897229,\n \"acc_norm\": 0.4667535853976532,\n\
\ \"acc_norm_stderr\": 0.012741974333897229\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.028332959514031208,\n\
\ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.028332959514031208\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6813725490196079,\n \"acc_stderr\": 0.01885008469646872,\n \
\ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.01885008469646872\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.044612721759105085,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.044612721759105085\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5667074663402693,\n\
\ \"mc1_stderr\": 0.017347024450107478,\n \"mc2\": 0.7126457863777319,\n\
\ \"mc2_stderr\": 0.014796561609011638\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8389897395422258,\n \"acc_stderr\": 0.010329712832785722\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7020470053070508,\n \
\ \"acc_stderr\": 0.01259793223291452\n }\n}\n```"
repo_url: https://huggingface.co/YouKnwMe/Mistral-7B-Instruct-exp-e2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|arc:challenge|25_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|gsm8k|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hellaswag|10_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T17-27-37.810259.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-26T17-27-37.810259.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- '**/details_harness|winogrande|5_2024-01-26T17-27-37.810259.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-26T17-27-37.810259.parquet'
- config_name: results
data_files:
- split: 2024_01_26T17_27_37.810259
path:
- results_2024-01-26T17-27-37.810259.parquet
- split: latest
path:
- results_2024-01-26T17-27-37.810259.parquet
---
# Dataset Card for Evaluation run of YouKnwMe/Mistral-7B-Instruct-exp-e2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [YouKnwMe/Mistral-7B-Instruct-exp-e2](https://huggingface.co/YouKnwMe/Mistral-7B-Instruct-exp-e2) 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_YouKnwMe__Mistral-7B-Instruct-exp-e2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-26T17:27:37.810259](https://huggingface.co/datasets/open-llm-leaderboard/details_YouKnwMe__Mistral-7B-Instruct-exp-e2/blob/main/results_2024-01-26T17-27-37.810259.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": 0.6558565508085182,
"acc_stderr": 0.03205699333246102,
"acc_norm": 0.6552801158659124,
"acc_norm_stderr": 0.03272709560202178,
"mc1": 0.5667074663402693,
"mc1_stderr": 0.017347024450107478,
"mc2": 0.7126457863777319,
"mc2_stderr": 0.014796561609011638
},
"harness|arc:challenge|25": {
"acc": 0.7022184300341296,
"acc_stderr": 0.013363080107244484,
"acc_norm": 0.7252559726962458,
"acc_norm_stderr": 0.013044617212771227
},
"harness|hellaswag|10": {
"acc": 0.7127066321449911,
"acc_stderr": 0.004515748192605716,
"acc_norm": 0.8849830711013742,
"acc_norm_stderr": 0.0031839033919416975
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.04072314811876837,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.04072314811876837
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7105263157894737,
"acc_stderr": 0.03690677986137283,
"acc_norm": 0.7105263157894737,
"acc_norm_stderr": 0.03690677986137283
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7169811320754716,
"acc_stderr": 0.027724236492700914,
"acc_norm": 0.7169811320754716,
"acc_norm_stderr": 0.027724236492700914
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.653179190751445,
"acc_stderr": 0.036291466701596636,
"acc_norm": 0.653179190751445,
"acc_norm_stderr": 0.036291466701596636
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.43137254901960786,
"acc_stderr": 0.04928099597287533,
"acc_norm": 0.43137254901960786,
"acc_norm_stderr": 0.04928099597287533
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5914893617021276,
"acc_stderr": 0.032134180267015755,
"acc_norm": 0.5914893617021276,
"acc_norm_stderr": 0.032134180267015755
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5586206896551724,
"acc_stderr": 0.04137931034482757,
"acc_norm": 0.5586206896551724,
"acc_norm_stderr": 0.04137931034482757
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4312169312169312,
"acc_stderr": 0.025506481698138208,
"acc_norm": 0.4312169312169312,
"acc_norm_stderr": 0.025506481698138208
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4603174603174603,
"acc_stderr": 0.04458029125470973,
"acc_norm": 0.4603174603174603,
"acc_norm_stderr": 0.04458029125470973
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7870967741935484,
"acc_stderr": 0.02328766512726854,
"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.02328766512726854
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4876847290640394,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.4876847290640394,
"acc_norm_stderr": 0.035169204442208966
},
"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": 0.7818181818181819,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.7818181818181819,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.803030303030303,
"acc_stderr": 0.028335609732463362,
"acc_norm": 0.803030303030303,
"acc_norm_stderr": 0.028335609732463362
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8911917098445595,
"acc_stderr": 0.022473253332768763,
"acc_norm": 0.8911917098445595,
"acc_norm_stderr": 0.022473253332768763
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.676923076923077,
"acc_stderr": 0.02371088850197057,
"acc_norm": 0.676923076923077,
"acc_norm_stderr": 0.02371088850197057
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34444444444444444,
"acc_stderr": 0.02897264888484427,
"acc_norm": 0.34444444444444444,
"acc_norm_stderr": 0.02897264888484427
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.680672268907563,
"acc_stderr": 0.0302839955258844,
"acc_norm": 0.680672268907563,
"acc_norm_stderr": 0.0302839955258844
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
"acc_stderr": 0.03929111781242742,
"acc_norm": 0.36423841059602646,
"acc_norm_stderr": 0.03929111781242742
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8440366972477065,
"acc_stderr": 0.01555580271359017,
"acc_norm": 0.8440366972477065,
"acc_norm_stderr": 0.01555580271359017
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.03407632093854051,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.03407632093854051
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931045,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931045
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8059071729957806,
"acc_stderr": 0.025744902532290902,
"acc_norm": 0.8059071729957806,
"acc_norm_stderr": 0.025744902532290902
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057221,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057221
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7862595419847328,
"acc_stderr": 0.0359546161177469,
"acc_norm": 0.7862595419847328,
"acc_norm_stderr": 0.0359546161177469
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228732,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228732
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7592592592592593,
"acc_stderr": 0.04133119440243839,
"acc_norm": 0.7592592592592593,
"acc_norm_stderr": 0.04133119440243839
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7607361963190185,
"acc_stderr": 0.0335195387952127,
"acc_norm": 0.7607361963190185,
"acc_norm_stderr": 0.0335195387952127
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.7864077669902912,
"acc_stderr": 0.040580420156460344,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.040580420156460344
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8803418803418803,
"acc_stderr": 0.021262719400406974,
"acc_norm": 0.8803418803418803,
"acc_norm_stderr": 0.021262719400406974
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.72,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.72,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8301404853128991,
"acc_stderr": 0.013428186370608306,
"acc_norm": 0.8301404853128991,
"acc_norm_stderr": 0.013428186370608306
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7427745664739884,
"acc_stderr": 0.02353292543104429,
"acc_norm": 0.7427745664739884,
"acc_norm_stderr": 0.02353292543104429
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.42793296089385474,
"acc_stderr": 0.01654788799741611,
"acc_norm": 0.42793296089385474,
"acc_norm_stderr": 0.01654788799741611
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7058823529411765,
"acc_stderr": 0.02609016250427905,
"acc_norm": 0.7058823529411765,
"acc_norm_stderr": 0.02609016250427905
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7234726688102894,
"acc_stderr": 0.025403832978179615,
"acc_norm": 0.7234726688102894,
"acc_norm_stderr": 0.025403832978179615
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7561728395061729,
"acc_stderr": 0.023891879541959607,
"acc_norm": 0.7561728395061729,
"acc_norm_stderr": 0.023891879541959607
},
"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": 0.4667535853976532,
"acc_stderr": 0.012741974333897229,
"acc_norm": 0.4667535853976532,
"acc_norm_stderr": 0.012741974333897229
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6801470588235294,
"acc_stderr": 0.028332959514031208,
"acc_norm": 0.6801470588235294,
"acc_norm_stderr": 0.028332959514031208
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6813725490196079,
"acc_stderr": 0.01885008469646872,
"acc_norm": 0.6813725490196079,
"acc_norm_stderr": 0.01885008469646872
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
"acc_stderr": 0.044612721759105085,
"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.044612721759105085
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7346938775510204,
"acc_stderr": 0.028263889943784593,
"acc_norm": 0.7346938775510204,
"acc_norm_stderr": 0.028263889943784593
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.845771144278607,
"acc_stderr": 0.025538433368578337,
"acc_norm": 0.845771144278607,
"acc_norm_stderr": 0.025538433368578337
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.0348735088019777,
"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5481927710843374,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.5481927710843374,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727665,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727665
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5667074663402693,
"mc1_stderr": 0.017347024450107478,
"mc2": 0.7126457863777319,
"mc2_stderr": 0.014796561609011638
},
"harness|winogrande|5": {
"acc": 0.8389897395422258,
"acc_stderr": 0.010329712832785722
},
"harness|gsm8k|5": {
"acc": 0.7020470053070508,
"acc_stderr": 0.01259793223291452
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
yzhuang/autotree_pmlb_magic_sgosdt_l256_d3_sd0 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 205680000
num_examples: 10000
- name: validation
num_bytes: 205680000
num_examples: 10000
download_size: 187642849
dataset_size: 411360000
---
# Dataset Card for "autotree_pmlb_magic_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
abi0235/guanaco-llama2-1k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966692
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
bene-ges/wiki-en-asr-adapt | ---
license: cc-by-sa-4.0
language:
- en
size_categories:
- 10M<n<100M
---
This is the dataset presented in my [ASRU-2023 paper](https://arxiv.org/abs/2309.17267).
It consists of multiple files:
Keys2Paragraphs.txt (internal name in scripts: yago_wiki.txt):
4.3 million unique words/phrases (English Wikipedia titles or their parts) occurring in 33.8 million English Wikipedia paragraphs.
Keys2Corruptions.txt (internal name in scripts: sub_misspells.txt):
26 million phrase pairs in the corrupted phrase inventory, as recognized by different ASR models
Keys2Related.txt (internal name in scripts: related_phrases.txt):
62.7 million phrase pairs in the related phrase inventory
FalsePositives.txt (internal name in scripts: false_positives.txt):
449 thousand phrase pairs in the false positive phrase inventory
NgramMappings.txt (internal name in scripts: replacement_vocab_filt.txt):
5.5 million character n-gram mappings dictionary
asr
outputs of g2p+tts+asr using 4 different ASR systems (conformer ctc was used twice),
gives pairs of initial phrase and its recognition result.
Does not include .wav files, but these can be reproduced by feeding g2p to tts
giza
raw outputs of GIZA++ alignments for each corpus,
from these we get NgramMappings.txt and Keys2Corruptions.txt
This [example code](https://github.com/bene-ges/nemo_compatible/blob/spellmapper_new_false_positive_sampling/scripts/nlp/en_spellmapper/dataset_preparation/build_training_data_from_wiki_en_asr_adapt.sh) shows how to generate training data from this dataset.
|
nbtpj/bionlp2021MAS | ---
license: afl-3.0
---
## MEDIQUA2012-MAS task
source data is available [here](https://github.com/abachaa/MEDIQA2021/tree/main/Task2)
des:
1. data features
Multiple Answer Summarization with:
* key: key of each question
* question: question
* text: merge all text of all answers (if it is train-split, a merge of article and section part)
* sum\_abs: abstractive multiple answer summarization
* sum\_ext: extractive multiple answer summarization
2. train\_article / train\_sec
Same structure with train, but:
* train: text: merge all text of all answers (if it is train-split, a merge of article and section part)
* train\_article: text is a merge of all subanswers 's articles
* train\_sec: text is a merge of all subanswers 's sections
|
Lv5Shira/mini-platypus | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4104439
num_examples: 1000
download_size: 2231404
dataset_size: 4104439
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Multimodal-Fatima/FGVC_Aircraft_test_facebook_opt_6.7b_Visclues_ns_3333 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
- name: scores
sequence: float64
splits:
- name: fewshot_0_bs_16
num_bytes: 299562491.375
num_examples: 3333
- name: fewshot_1_bs_16
num_bytes: 300685243.375
num_examples: 3333
- name: fewshot_3_bs_16
num_bytes: 302937632.375
num_examples: 3333
download_size: 886179506
dataset_size: 903185367.125
---
# Dataset Card for "FGVC_Aircraft_test_facebook_opt_6.7b_Visclues_ns_3333"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_350m_Attributes_ns_5647 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
- name: scores
sequence: float64
splits:
- name: fewshot_0_bs_16
num_bytes: 84091856.125
num_examples: 5647
- name: fewshot_1_bs_16
num_bytes: 85276115.125
num_examples: 5647
- name: fewshot_3_bs_16
num_bytes: 87656033.125
num_examples: 5647
- name: fewshot_5_bs_16
num_bytes: 90033855.125
num_examples: 5647
- name: fewshot_8_bs_16
num_bytes: 93580332.125
num_examples: 5647
download_size: 415578350
dataset_size: 440638191.625
---
# Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Attributes_ns_5647"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
keremberke/smoke-object-detection | ---
task_categories:
- object-detection
tags:
- roboflow
---
### Roboflow Dataset Page
https://universe.roboflow.com/smoke-detection/smoke100-uwe4t/dataset/4
### Dataset Labels
```
['smoke']
```
### Citation
```
@misc{ smoke100-uwe4t_dataset,
title = { Smoke100 Dataset },
type = { Open Source Dataset },
author = { Smoke Detection },
howpublished = { \\url{ https://universe.roboflow.com/smoke-detection/smoke100-uwe4t } },
url = { https://universe.roboflow.com/smoke-detection/smoke100-uwe4t },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { dec },
note = { visited on 2023-01-02 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.ai on March 17, 2022 at 3:42 PM GMT
It includes 21578 images.
Smoke are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 640x640 (Stretch)
No image augmentation techniques were applied.
|
Princess3/NZlegislation | ---
license: wtfpl
---
|
liuyanchen1015/MULTI_VALUE_qqp_it_dobj | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 148781
num_examples: 705
- name: test
num_bytes: 1519695
num_examples: 7400
- name: train
num_bytes: 1439099
num_examples: 6741
download_size: 1865130
dataset_size: 3107575
---
# Dataset Card for "MULTI_VALUE_qqp_it_dobj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lmms-lab/MMMU | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: options
dtype: string
- name: explanation
dtype: string
- name: image_1
dtype: image
- name: image_2
dtype: image
- name: image_3
dtype: image
- name: image_4
dtype: image
- name: image_5
dtype: image
- name: image_6
dtype: image
- name: image_7
dtype: image
- name: img_type
dtype: string
- name: answer
dtype: string
- name: topic_difficulty
dtype: string
- name: question_type
dtype: string
- name: subfield
dtype: string
splits:
- name: dev
num_bytes: 57719107.0
num_examples: 150
- name: validation
num_bytes: 347519954.0
num_examples: 900
- name: test
num_bytes: 3271046267.0
num_examples: 10500
download_size: 3377778136
dataset_size: 3676285328.0
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
This is a merged version of [MMMU/MMMU](https://huggingface.co/datasets/MMMU/MMMU) with all subsets concatenated.
<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
</p>
# Large-scale Multi-modality Models Evaluation Suite
> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)
# This Dataset
This is a formatted version of [MMMU](https://github.com/MMMU-Benchmark/MMMU). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@article{yue2023mmmu,
title={Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi},
author={Yue, Xiang and Ni, Yuansheng and Zhang, Kai and Zheng, Tianyu and Liu, Ruoqi and Zhang, Ge and Stevens, Samuel and Jiang, Dongfu and Ren, Weiming and Sun, Yuxuan and others},
journal={arXiv preprint arXiv:2311.16502},
year={2023}
}
``` |
beephids/paper-llm-prompts | ---
license: mit
---
|
irds/wikiclir_ja | ---
pretty_name: '`wikiclir/ja`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `wikiclir/ja`
The `wikiclir/ja` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/wikiclir#wikiclir/ja).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=1,071,292
- `queries` (i.e., topics); count=426,431
- `qrels`: (relevance assessments); count=3,338,667
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/wikiclir_ja', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'text': ...}
queries = load_dataset('irds/wikiclir_ja', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/wikiclir_ja', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{sasaki-etal-2018-cross,
title = "Cross-Lingual Learning-to-Rank with Shared Representations",
author = "Sasaki, Shota and
Sun, Shuo and
Schamoni, Shigehiko and
Duh, Kevin and
Inui, Kentaro",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2073",
doi = "10.18653/v1/N18-2073",
pages = "458--463"
}
```
|
vilm/OpenOrca-Viet | ---
license: apache-2.0
---
## 🇻🇳 Vietnamese OpenOrca is here 🐋
<img src="https://i.ibb.co/kgmJG96/orca-viet.png" alt="drawing" width="512"/>
Dive into the Vietnamese linguistic landscape with OpenOrca, a cutting-edge dataset crafted through a pioneering partnership between **Virtual Interactive** and **Alignment Lab AI**. Drawing inspiration and methodology from the renowned [Orca paper](https://arxiv.org/abs/2306.02707), we've expanded our horizons to distill knowledge from a more eclectic mix of leading LLMs including GPT-4, PaLM-2, and Claude. Our vision with this dataset is to fuel research and development that will catapult the performance of Vietnamese Language Models into uncharted territories. Join us on this exhilarating journey to redefine AI's linguistic prowess.
The main original source of tasks/questions is a translated version of *FLAN*, **vi-FLAN**. We further augmented **vi-FLAN** on better state-of-the-art LLMs.
## Citation
```
@misc{OpenOrcaViet,
title = {OpenOrca-Viet: GPT Augmented FLAN Reasoning for Vietnamese},
author = {Virtual Interactive and Alignment Lab AI},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/vilm/OpenOrca-Viet}},
}
``` |
zjhqss/test | ---
license: mit
task_categories:
- table-question-answering
size_categories:
- n<1K
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
WILSONBRUZA/TK | ---
license: openrail
---
|
cahya/soda-id | ---
license: cc-by-4.0
---
|
reralle/s-f-o | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': arabic
'1': dutch
'2': french
'3': korean
'4': mandarin
'5': portuguese
'6': russian
'7': spanish
'8': uk
'9': usa
splits:
- name: train
num_bytes: 3711642651.2
num_examples: 4200
- name: test
num_bytes: 51756368.0
num_examples: 60
- name: validation
num_bytes: 51955368.0
num_examples: 60
download_size: 1232088535
dataset_size: 3815354387.2
---
# Dataset Card for "s-f-o"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_61 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1288082504.0
num_examples: 252962
download_size: 1307009191
dataset_size: 1288082504.0
---
# Dataset Card for "chunk_61"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lang-uk/malyuk | ---
language:
- uk
size_categories:
- 10B<n<100B
---
## Malyuk [mɐˈlʲuk]
Combined corpus: [UberText 2.0](https://lang.org.ua/en/ubertext/), [Oscar](https://huggingface.co/datasets/oscar), [Ukrainian News](https://huggingface.co/datasets/zeusfsx/ukrainian-news)
This is not an official release by any means. It is just a compilation made by me to simplify the training of the Ukrainian LLM. Nothing is guaranteed, no support requests, nothing.
* 113GB of texts in jsonl.
* 38941863 articles.
|
SayaliB/mini-platypus | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 14061558
num_examples: 6000
download_size: 7568759
dataset_size: 14061558
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
facet/dalle-3-contrastive-captions | ---
dataset_info:
features:
- name: caption
dtype: string
- name: image
dtype: image
- name: link
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
- name: dense_caption_1
dtype: string
- name: dense_caption_2
dtype: string
- name: dense_caption_3
dtype: string
- name: dense_caption_4
dtype: string
- name: dense_caption_5
dtype: string
- name: dense_caption_6
dtype: string
- name: dense_caption_7
dtype: string
- name: dense_caption_8
dtype: string
- name: dense_caption_9
dtype: string
- name: dense_caption_10
dtype: string
splits:
- name: train
num_bytes: 7529944312.638
num_examples: 4806
download_size: 7512650231
dataset_size: 7529944312.638
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dalle-3-contrastive-captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BangumiBase/yuunaandthehauntedhotsprings | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Yuuna And The Haunted Hot Springs
This is the image base of bangumi Yuuna and the Haunted Hot Springs, we detected 28 characters, 2185 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 388 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 107 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 15 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 25 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 476 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 64 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 7 | [Download](6/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 7 | 21 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 11 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 202 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 152 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 22 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 14 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 9 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 94 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 128 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 48 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 6 | [Download](17/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 18 | 13 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 8 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 8 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 77 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 11 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 11 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 125 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 10 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 12 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 121 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
andersonbcdefg/specter-title-to-abs | ---
dataset_info:
features:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 2167460181.5859566
num_examples: 871720
- name: validation
num_bytes: 49972604.880258456
num_examples: 22346
download_size: 1307985129
dataset_size: 2217432786.466215
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
aneeshas/tla_masked_code_eval | ---
dataset_info:
features:
- name: protocol
dtype: string
- name: prompt
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 139933
num_examples: 18
download_size: 52239
dataset_size: 139933
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tla_masked_code_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
KidKaito/exty_v2_dataset | ---
license: mit
---
|
open-llm-leaderboard/details_dfurman__llama-2-7b-instruct-peft | ---
pretty_name: Evaluation run of dfurman/llama-2-7b-instruct-peft
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [dfurman/llama-2-7b-instruct-peft](https://huggingface.co/dfurman/llama-2-7b-instruct-peft)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_dfurman__llama-2-7b-instruct-peft\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T03:15:50.340712](https://huggingface.co/datasets/open-llm-leaderboard/details_dfurman__llama-2-7b-instruct-peft/blob/main/results_2023-10-24T03-15-50.340712.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0010486577181208054,\n\
\ \"em_stderr\": 0.0003314581465219154,\n \"f1\": 0.05818687080536916,\n\
\ \"f1_stderr\": 0.0013326120366464343,\n \"acc\": 0.4020858403049834,\n\
\ \"acc_stderr\": 0.009398700998364592\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.0003314581465219154,\n\
\ \"f1\": 0.05818687080536916,\n \"f1_stderr\": 0.0013326120366464343\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05989385898407885,\n \
\ \"acc_stderr\": 0.006536148151288708\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.744277821625888,\n \"acc_stderr\": 0.012261253845440474\n\
\ }\n}\n```"
repo_url: https://huggingface.co/dfurman/llama-2-7b-instruct-peft
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|arc:challenge|25_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T03_15_50.340712
path:
- '**/details_harness|drop|3_2023-10-24T03-15-50.340712.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T03-15-50.340712.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T03_15_50.340712
path:
- '**/details_harness|gsm8k|5_2023-10-24T03-15-50.340712.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T03-15-50.340712.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hellaswag|10_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T14-29-36.510142.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T14-29-36.510142.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T14-29-36.510142.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T03_15_50.340712
path:
- '**/details_harness|winogrande|5_2023-10-24T03-15-50.340712.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T03-15-50.340712.parquet'
- config_name: results
data_files:
- split: 2023_10_03T14_29_36.510142
path:
- results_2023-10-03T14-29-36.510142.parquet
- split: 2023_10_24T03_15_50.340712
path:
- results_2023-10-24T03-15-50.340712.parquet
- split: latest
path:
- results_2023-10-24T03-15-50.340712.parquet
---
# Dataset Card for Evaluation run of dfurman/llama-2-7b-instruct-peft
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/dfurman/llama-2-7b-instruct-peft
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [dfurman/llama-2-7b-instruct-peft](https://huggingface.co/dfurman/llama-2-7b-instruct-peft) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_dfurman__llama-2-7b-instruct-peft",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T03:15:50.340712](https://huggingface.co/datasets/open-llm-leaderboard/details_dfurman__llama-2-7b-instruct-peft/blob/main/results_2023-10-24T03-15-50.340712.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0010486577181208054,
"em_stderr": 0.0003314581465219154,
"f1": 0.05818687080536916,
"f1_stderr": 0.0013326120366464343,
"acc": 0.4020858403049834,
"acc_stderr": 0.009398700998364592
},
"harness|drop|3": {
"em": 0.0010486577181208054,
"em_stderr": 0.0003314581465219154,
"f1": 0.05818687080536916,
"f1_stderr": 0.0013326120366464343
},
"harness|gsm8k|5": {
"acc": 0.05989385898407885,
"acc_stderr": 0.006536148151288708
},
"harness|winogrande|5": {
"acc": 0.744277821625888,
"acc_stderr": 0.012261253845440474
}
}
```
### 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
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### Citation Information
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### Contributions
[More Information Needed] |
argilla/ultrafeedback-binarized-preferences-cleaned | ---
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- text-generation
pretty_name: UltraFeedback Binarized Preferences Cleaned
dataset_info:
features:
- name: source
dtype: string
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: chosen-rating
dtype: float64
- name: chosen-model
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected-rating
dtype: float64
- name: rejected-model
dtype: string
splits:
- name: train
num_bytes: 284937773
num_examples: 60917
download_size: 143257393
dataset_size: 284937773
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- dpo
- preference
- ultrafeedback
---
# UltraFeedback - Binarized using the Average of Preference Ratings (Cleaned)
This dataset represents a new iteration on top of [`argilla/ultrafeedback-binarized-preferences`](https://huggingface.co/argilla/ultrafeedback-binarized-preferences),
and is the **recommended and preferred dataset by Argilla to use from now on when fine-tuning on UltraFeedback**.
Read more about Argilla's approach towards UltraFeedback binarization at [`argilla/ultrafeedback-binarized-preferences/README.md`](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences/blob/main/README.md).
## Differences with `argilla/ultrafeedback-binarized-preferences`
Thanks to the recent issue identified by [AllenAI](https://huggingface.co/allenai) related to the TruthfulQA contamination within the
original UltraFeedback dataset due to some prompts being reused from the TruthfulQA dataset (used for benchmarking
in the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) from HuggingFace H4), we also decided
to follow AllenAI's advice and remove those from the UltraFeedback dataset that we binarized using a completely different approach, which
implied using the average of the preference ratings rather than the critique overall score, as
[`HuggingFaceH4/ultrafeedback_binarized`](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) did.
Besides that, we also saw that not only the rows with the `source=truthful_qa` were contamined (for obvious reasons), but also some
coming from ShareGPT, so we also removed those doing a left join with both subsets from the [`truthful_qa`](https://huggingface.co/datasets/truthful_qa) dataset.
Additionally, we also modified the formatting to be aligned with both [`HuggingFaceH4/ultrafeedback_binarized`](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized),
and [`allenai/ultrafeedback_binarized_cleaned`](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned) in order to ease
the integration within the [`huggingface/alignment-handbook`](https://github.com/huggingface/alignment-handbook) so that the formatting is standardized.
## Reproduce
<a target="_blank" href="https://colab.research.google.com/drive/1XR9P1St4yTNY0tjti_tIjm-yzP5Bfqc0?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
To reproduce the data processing combining both our approach and the suggestions from HuggingFace H4 w.r.t. the formatting and the ones from AllenAI to
remove the TruthfulQA contamination, feel free to run the attached Colab Notebook or just view it at [`notebook.ipynb`](./notebook.ipynb) within this repository.
From Argilla we encourage anyone out there to play around, investigate, and experiment with the data, and we firmly believe on open sourcing what we do, as
ourselves, as well as the whole community, benefit a lot from open source and we also want to give back.
## Citation
If you find this dataset is useful in your work, please cite the original UltraFeedback dataset: https://huggingface.co/datasets/openbmb/UltraFeedback
Additionally, you may also want to cite our work with Notus 7B, which lead the curation of the UltraFeedback dataset:
```bibtex
@misc{notus2023,
author = {Alvaro Bartolome and Gabriel Martin and Daniel Vila},
title = {Notus},
year = {2023},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/argilla-io/notus}}
}
```
> Alphabetically ordered by last name due to equal contribution. |
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