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README.md
CHANGED
@@ -3,8 +3,8 @@ pretty_name: Evaluation run of NewstaR/Koss-7B-chat
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dataset_summary: "Dataset automatically created during the evaluation run of model\
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\ [NewstaR/Koss-7B-chat](https://huggingface.co/NewstaR/Koss-7B-chat) on the [Open\
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\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
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\nThe dataset is composed of
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\ evaluated task.\n\nThe dataset has been created from
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\ found as a specific split in each configuration, the split being named using the\
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\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
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\nAn additional configuration \"results\" store all the aggregated results of the\
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@@ -12,173 +12,20 @@ dataset_summary: "Dataset automatically created during the evaluation run of mod
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\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
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\nTo load the details from a run, you can for instance do the following:\n```python\n\
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from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NewstaR__Koss-7B-chat\"\
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,\n\t\"
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-
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\ that their might be results for other tasks in the repos if successive evals didn't\
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\ cover the same tasks. You find each in the results and the \"latest\" split for\
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\ each eval):\n\n```python\n{\n \"all\": {\n \"
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\ \"
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\ \"
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\ \"
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\ \"
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\
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\ },\n \"harness|
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\ \"
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\
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: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \
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\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n\
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\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4148148148148148,\n\
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\ \"acc_stderr\": 0.042561937679014075,\n \"acc_norm\": 0.4148148148148148,\n\
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\ \"acc_norm_stderr\": 0.042561937679014075\n },\n \"harness|hendrycksTest-astronomy|5\"\
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: {\n \"acc\": 0.4934210526315789,\n \"acc_stderr\": 0.040685900502249704,\n\
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\ \"acc_norm\": 0.4934210526315789,\n \"acc_norm_stderr\": 0.040685900502249704\n\
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\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.43,\n\
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\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \
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\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
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: {\n \"acc\": 0.5169811320754717,\n \"acc_stderr\": 0.030755120364119905,\n\
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\ \"acc_norm\": 0.5169811320754717,\n \"acc_norm_stderr\": 0.030755120364119905\n\
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\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5416666666666666,\n\
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\ \"acc_stderr\": 0.041666666666666644,\n \"acc_norm\": 0.5416666666666666,\n\
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\ \"acc_norm_stderr\": 0.041666666666666644\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
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: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
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\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
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\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
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: 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n\
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\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
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-
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952344,\n \
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\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952344\n \
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\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4046242774566474,\n\
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\ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.4046242774566474,\n\
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\ \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness|hendrycksTest-college_physics|5\"\
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: {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.04280105837364396,\n\
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\ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.04280105837364396\n\
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\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
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\ 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\
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\ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
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: {\n \"acc\": 0.39148936170212767,\n \"acc_stderr\": 0.03190701242326812,\n\
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\ \"acc_norm\": 0.39148936170212767,\n \"acc_norm_stderr\": 0.03190701242326812\n\
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\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3333333333333333,\n\
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\ \"acc_stderr\": 0.044346007015849245,\n \"acc_norm\": 0.3333333333333333,\n\
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\ \"acc_norm_stderr\": 0.044346007015849245\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
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: {\n \"acc\": 0.42758620689655175,\n \"acc_stderr\": 0.04122737111370332,\n\
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\ \"acc_norm\": 0.42758620689655175,\n \"acc_norm_stderr\": 0.04122737111370332\n\
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\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
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: 0.28835978835978837,\n \"acc_stderr\": 0.0233306540545359,\n \"\
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acc_norm\": 0.28835978835978837,\n \"acc_norm_stderr\": 0.0233306540545359\n\
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\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.24603174603174602,\n\
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\ \"acc_stderr\": 0.038522733649243156,\n \"acc_norm\": 0.24603174603174602,\n\
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\ \"acc_norm_stderr\": 0.038522733649243156\n },\n \"harness|hendrycksTest-global_facts|5\"\
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: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
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\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
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\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
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: 0.5258064516129032,\n \"acc_stderr\": 0.028406095057653315,\n \"\
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acc_norm\": 0.5258064516129032,\n \"acc_norm_stderr\": 0.028406095057653315\n\
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\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
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: 0.3497536945812808,\n \"acc_stderr\": 0.03355400904969565,\n \"\
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acc_norm\": 0.3497536945812808,\n \"acc_norm_stderr\": 0.03355400904969565\n\
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\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
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\ \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n\
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\ \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
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: {\n \"acc\": 0.5454545454545454,\n \"acc_stderr\": 0.038881769216741004,\n\
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\ \"acc_norm\": 0.5454545454545454,\n \"acc_norm_stderr\": 0.038881769216741004\n\
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\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
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: 0.5959595959595959,\n \"acc_stderr\": 0.03496130972056129,\n \"\
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acc_norm\": 0.5959595959595959,\n \"acc_norm_stderr\": 0.03496130972056129\n\
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\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
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\ \"acc\": 0.6683937823834197,\n \"acc_stderr\": 0.03397636541089118,\n\
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\ \"acc_norm\": 0.6683937823834197,\n \"acc_norm_stderr\": 0.03397636541089118\n\
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\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
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\ \"acc\": 0.4128205128205128,\n \"acc_stderr\": 0.024962683564331796,\n\
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\ \"acc_norm\": 0.4128205128205128,\n \"acc_norm_stderr\": 0.024962683564331796\n\
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\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
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acc\": 0.25555555555555554,\n \"acc_stderr\": 0.02659393910184407,\n \
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\ \"acc_norm\": 0.25555555555555554,\n \"acc_norm_stderr\": 0.02659393910184407\n\
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\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
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\ \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.032145368597886394,\n\
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\ \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.032145368597886394\n\
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\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
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: 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\
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acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\
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\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
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: 0.6495412844036698,\n \"acc_stderr\": 0.02045607759982446,\n \"\
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acc_norm\": 0.6495412844036698,\n \"acc_norm_stderr\": 0.02045607759982446\n\
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\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
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: 0.3287037037037037,\n \"acc_stderr\": 0.032036140846700596,\n \"\
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acc_norm\": 0.3287037037037037,\n \"acc_norm_stderr\": 0.032036140846700596\n\
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\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
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: 0.6666666666666666,\n \"acc_stderr\": 0.03308611113236434,\n \"\
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acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.03308611113236434\n\
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\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
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acc\": 0.6582278481012658,\n \"acc_stderr\": 0.03087453753755362,\n \
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\ \"acc_norm\": 0.6582278481012658,\n \"acc_norm_stderr\": 0.03087453753755362\n\
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\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5695067264573991,\n\
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\ \"acc_stderr\": 0.033231973029429394,\n \"acc_norm\": 0.5695067264573991,\n\
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\ \"acc_norm_stderr\": 0.033231973029429394\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
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: {\n \"acc\": 0.4961832061068702,\n \"acc_stderr\": 0.043851623256015534,\n\
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\ \"acc_norm\": 0.4961832061068702,\n \"acc_norm_stderr\": 0.043851623256015534\n\
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\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
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\ 0.5619834710743802,\n \"acc_stderr\": 0.04529146804435792,\n \"\
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acc_norm\": 0.5619834710743802,\n \"acc_norm_stderr\": 0.04529146804435792\n\
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\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5833333333333334,\n\
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\ \"acc_stderr\": 0.04766075165356461,\n \"acc_norm\": 0.5833333333333334,\n\
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\ \"acc_norm_stderr\": 0.04766075165356461\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
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: {\n \"acc\": 0.5398773006134969,\n \"acc_stderr\": 0.03915857291436971,\n\
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\ \"acc_norm\": 0.5398773006134969,\n \"acc_norm_stderr\": 0.03915857291436971\n\
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\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3392857142857143,\n\
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\ \"acc_stderr\": 0.04493949068613539,\n \"acc_norm\": 0.3392857142857143,\n\
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\ \"acc_norm_stderr\": 0.04493949068613539\n },\n \"harness|hendrycksTest-management|5\"\
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: {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.0465614711001235,\n\
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\ \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.0465614711001235\n\
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\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7393162393162394,\n\
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\ \"acc_stderr\": 0.028760348956523414,\n \"acc_norm\": 0.7393162393162394,\n\
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\ \"acc_norm_stderr\": 0.028760348956523414\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
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: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
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\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
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\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6730523627075351,\n\
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\ \"acc_stderr\": 0.016774908180131467,\n \"acc_norm\": 0.6730523627075351,\n\
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\ \"acc_norm_stderr\": 0.016774908180131467\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
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: {\n \"acc\": 0.5202312138728323,\n \"acc_stderr\": 0.026897049996382875,\n\
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\ \"acc_norm\": 0.5202312138728323,\n \"acc_norm_stderr\": 0.026897049996382875\n\
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\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.21564245810055865,\n\
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\ \"acc_stderr\": 0.013754835975482351,\n \"acc_norm\": 0.21564245810055865,\n\
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\ \"acc_norm_stderr\": 0.013754835975482351\n },\n \"harness|hendrycksTest-nutrition|5\"\
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: {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.028607893699576066,\n\
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\ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.028607893699576066\n\
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\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5691318327974276,\n\
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\ \"acc_stderr\": 0.028125340983972714,\n \"acc_norm\": 0.5691318327974276,\n\
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\ \"acc_norm_stderr\": 0.028125340983972714\n },\n \"harness|hendrycksTest-prehistory|5\"\
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: {\n \"acc\": 0.5648148148148148,\n \"acc_stderr\": 0.027586006221607697,\n\
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\ \"acc_norm\": 0.5648148148148148,\n \"acc_norm_stderr\": 0.027586006221607697\n\
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\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
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acc\": 0.3546099290780142,\n \"acc_stderr\": 0.028538650028878638,\n \
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\ \"acc_norm\": 0.3546099290780142,\n \"acc_norm_stderr\": 0.028538650028878638\n\
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\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.333116036505867,\n\
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\ \"acc_stderr\": 0.012037930451512052,\n \"acc_norm\": 0.333116036505867,\n\
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\ \"acc_norm_stderr\": 0.012037930451512052\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
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: {\n \"acc\": 0.35661764705882354,\n \"acc_stderr\": 0.029097209568411945,\n\
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\ \"acc_norm\": 0.35661764705882354,\n \"acc_norm_stderr\": 0.029097209568411945\n\
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\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
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acc\": 0.4722222222222222,\n \"acc_stderr\": 0.02019659493354119,\n \
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\ \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.02019659493354119\n\
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\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5181818181818182,\n\
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\ \"acc_stderr\": 0.04785964010794915,\n \"acc_norm\": 0.5181818181818182,\n\
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\ \"acc_norm_stderr\": 0.04785964010794915\n },\n \"harness|hendrycksTest-security_studies|5\"\
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: {\n \"acc\": 0.4775510204081633,\n \"acc_stderr\": 0.03197694118713672,\n\
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\ \"acc_norm\": 0.4775510204081633,\n \"acc_norm_stderr\": 0.03197694118713672\n\
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\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5771144278606966,\n\
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\ \"acc_stderr\": 0.034932317774212816,\n \"acc_norm\": 0.5771144278606966,\n\
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\ \"acc_norm_stderr\": 0.034932317774212816\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
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: {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \
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\ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n \
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\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.42771084337349397,\n\
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\ \"acc_stderr\": 0.038515976837185335,\n \"acc_norm\": 0.42771084337349397,\n\
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\ \"acc_norm_stderr\": 0.038515976837185335\n },\n \"harness|hendrycksTest-world_religions|5\"\
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-
: {\n \"acc\": 0.7017543859649122,\n \"acc_stderr\": 0.035087719298245626,\n\
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\ \"acc_norm\": 0.7017543859649122,\n \"acc_norm_stderr\": 0.035087719298245626\n\
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\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2913096695226438,\n\
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\ \"mc1_stderr\": 0.015905987048184828,\n \"mc2\": 0.4396876190519236,\n\
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\ \"mc2_stderr\": 0.015652499203021628\n }\n}\n```"
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repo_url: https://huggingface.co/NewstaR/Koss-7B-chat
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leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
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point_of_contact: clementine@hf.co
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- split: latest
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path:
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- '**/details_harness|arc:challenge|25_2023-10-04T03-19-48.694479.parquet'
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- config_name: harness_hellaswag_10
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data_files:
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- split: 2023_10_04T03_19_48.694479
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- split: latest
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path:
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- '**/details_harness|truthfulqa:mc|0_2023-10-04T03-19-48.694479.parquet'
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- config_name: results
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data_files:
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- split: 2023_10_04T03_19_48.694479
|
789 |
path:
|
790 |
- results_2023-10-04T03-19-48.694479.parquet
|
|
|
|
|
|
|
791 |
- split: latest
|
792 |
path:
|
793 |
-
- results_2023-10-
|
794 |
---
|
795 |
|
796 |
# Dataset Card for Evaluation run of NewstaR/Koss-7B-chat
|
@@ -807,9 +681,9 @@ configs:
|
|
807 |
|
808 |
Dataset automatically created during the evaluation run of model [NewstaR/Koss-7B-chat](https://huggingface.co/NewstaR/Koss-7B-chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
|
809 |
|
810 |
-
The dataset is composed of
|
811 |
|
812 |
-
The dataset has been created from
|
813 |
|
814 |
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)).
|
815 |
|
@@ -817,385 +691,37 @@ To load the details from a run, you can for instance do the following:
|
|
817 |
```python
|
818 |
from datasets import load_dataset
|
819 |
data = load_dataset("open-llm-leaderboard/details_NewstaR__Koss-7B-chat",
|
820 |
-
"
|
821 |
split="train")
|
822 |
```
|
823 |
|
824 |
## Latest results
|
825 |
|
826 |
-
These are the [latest results from run 2023-10-
|
827 |
|
828 |
```python
|
829 |
{
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"all": {
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"
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"
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"
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"
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"
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"
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"
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"
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851 |
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857 |
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865 |
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869 |
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|
1141 |
-
"acc": 0.333116036505867,
|
1142 |
-
"acc_stderr": 0.012037930451512052,
|
1143 |
-
"acc_norm": 0.333116036505867,
|
1144 |
-
"acc_norm_stderr": 0.012037930451512052
|
1145 |
-
},
|
1146 |
-
"harness|hendrycksTest-professional_medicine|5": {
|
1147 |
-
"acc": 0.35661764705882354,
|
1148 |
-
"acc_stderr": 0.029097209568411945,
|
1149 |
-
"acc_norm": 0.35661764705882354,
|
1150 |
-
"acc_norm_stderr": 0.029097209568411945
|
1151 |
-
},
|
1152 |
-
"harness|hendrycksTest-professional_psychology|5": {
|
1153 |
-
"acc": 0.4722222222222222,
|
1154 |
-
"acc_stderr": 0.02019659493354119,
|
1155 |
-
"acc_norm": 0.4722222222222222,
|
1156 |
-
"acc_norm_stderr": 0.02019659493354119
|
1157 |
-
},
|
1158 |
-
"harness|hendrycksTest-public_relations|5": {
|
1159 |
-
"acc": 0.5181818181818182,
|
1160 |
-
"acc_stderr": 0.04785964010794915,
|
1161 |
-
"acc_norm": 0.5181818181818182,
|
1162 |
-
"acc_norm_stderr": 0.04785964010794915
|
1163 |
-
},
|
1164 |
-
"harness|hendrycksTest-security_studies|5": {
|
1165 |
-
"acc": 0.4775510204081633,
|
1166 |
-
"acc_stderr": 0.03197694118713672,
|
1167 |
-
"acc_norm": 0.4775510204081633,
|
1168 |
-
"acc_norm_stderr": 0.03197694118713672
|
1169 |
-
},
|
1170 |
-
"harness|hendrycksTest-sociology|5": {
|
1171 |
-
"acc": 0.5771144278606966,
|
1172 |
-
"acc_stderr": 0.034932317774212816,
|
1173 |
-
"acc_norm": 0.5771144278606966,
|
1174 |
-
"acc_norm_stderr": 0.034932317774212816
|
1175 |
-
},
|
1176 |
-
"harness|hendrycksTest-us_foreign_policy|5": {
|
1177 |
-
"acc": 0.67,
|
1178 |
-
"acc_stderr": 0.04725815626252607,
|
1179 |
-
"acc_norm": 0.67,
|
1180 |
-
"acc_norm_stderr": 0.04725815626252607
|
1181 |
-
},
|
1182 |
-
"harness|hendrycksTest-virology|5": {
|
1183 |
-
"acc": 0.42771084337349397,
|
1184 |
-
"acc_stderr": 0.038515976837185335,
|
1185 |
-
"acc_norm": 0.42771084337349397,
|
1186 |
-
"acc_norm_stderr": 0.038515976837185335
|
1187 |
-
},
|
1188 |
-
"harness|hendrycksTest-world_religions|5": {
|
1189 |
-
"acc": 0.7017543859649122,
|
1190 |
-
"acc_stderr": 0.035087719298245626,
|
1191 |
-
"acc_norm": 0.7017543859649122,
|
1192 |
-
"acc_norm_stderr": 0.035087719298245626
|
1193 |
-
},
|
1194 |
-
"harness|truthfulqa:mc|0": {
|
1195 |
-
"mc1": 0.2913096695226438,
|
1196 |
-
"mc1_stderr": 0.015905987048184828,
|
1197 |
-
"mc2": 0.4396876190519236,
|
1198 |
-
"mc2_stderr": 0.015652499203021628
|
1199 |
}
|
1200 |
}
|
1201 |
```
|
|
|
3 |
dataset_summary: "Dataset automatically created during the evaluation run of model\
|
4 |
\ [NewstaR/Koss-7B-chat](https://huggingface.co/NewstaR/Koss-7B-chat) on the [Open\
|
5 |
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
|
6 |
+
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
|
7 |
+
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
|
8 |
\ found as a specific split in each configuration, the split being named using the\
|
9 |
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
|
10 |
\nAn additional configuration \"results\" store all the aggregated results of the\
|
|
|
12 |
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
|
13 |
\nTo load the details from a run, you can for instance do the following:\n```python\n\
|
14 |
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NewstaR__Koss-7B-chat\"\
|
15 |
+
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
|
16 |
+
These are the [latest results from run 2023-10-23T08:06:32.820862](https://huggingface.co/datasets/open-llm-leaderboard/details_NewstaR__Koss-7B-chat/blob/main/results_2023-10-23T08-06-32.820862.json)(note\
|
17 |
\ that their might be results for other tasks in the repos if successive evals didn't\
|
18 |
\ cover the same tasks. You find each in the results and the \"latest\" split for\
|
19 |
+
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.06333892617449664,\n\
|
20 |
+
\ \"em_stderr\": 0.002494400790190545,\n \"f1\": 0.12617449664429503,\n\
|
21 |
+
\ \"f1_stderr\": 0.002812859883562843,\n \"acc\": 0.39549166962367155,\n\
|
22 |
+
\ \"acc_stderr\": 0.009921949302668327\n },\n \"harness|drop|3\": {\n\
|
23 |
+
\ \"em\": 0.06333892617449664,\n \"em_stderr\": 0.002494400790190545,\n\
|
24 |
+
\ \"f1\": 0.12617449664429503,\n \"f1_stderr\": 0.002812859883562843\n\
|
25 |
+
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07354056103108415,\n \
|
26 |
+
\ \"acc_stderr\": 0.0071898357543652685\n },\n \"harness|winogrande|5\"\
|
27 |
+
: {\n \"acc\": 0.7174427782162589,\n \"acc_stderr\": 0.012654062850971384\n\
|
28 |
+
\ }\n}\n```"
|
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|
29 |
repo_url: https://huggingface.co/NewstaR/Koss-7B-chat
|
30 |
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
|
31 |
point_of_contact: clementine@hf.co
|
|
|
38 |
- split: latest
|
39 |
path:
|
40 |
- '**/details_harness|arc:challenge|25_2023-10-04T03-19-48.694479.parquet'
|
41 |
+
- config_name: harness_drop_3
|
42 |
+
data_files:
|
43 |
+
- split: 2023_10_23T08_06_32.820862
|
44 |
+
path:
|
45 |
+
- '**/details_harness|drop|3_2023-10-23T08-06-32.820862.parquet'
|
46 |
+
- split: latest
|
47 |
+
path:
|
48 |
+
- '**/details_harness|drop|3_2023-10-23T08-06-32.820862.parquet'
|
49 |
+
- config_name: harness_gsm8k_5
|
50 |
+
data_files:
|
51 |
+
- split: 2023_10_23T08_06_32.820862
|
52 |
+
path:
|
53 |
+
- '**/details_harness|gsm8k|5_2023-10-23T08-06-32.820862.parquet'
|
54 |
+
- split: latest
|
55 |
+
path:
|
56 |
+
- '**/details_harness|gsm8k|5_2023-10-23T08-06-32.820862.parquet'
|
57 |
- config_name: harness_hellaswag_10
|
58 |
data_files:
|
59 |
- split: 2023_10_04T03_19_48.694479
|
|
|
646 |
- split: latest
|
647 |
path:
|
648 |
- '**/details_harness|truthfulqa:mc|0_2023-10-04T03-19-48.694479.parquet'
|
649 |
+
- config_name: harness_winogrande_5
|
650 |
+
data_files:
|
651 |
+
- split: 2023_10_23T08_06_32.820862
|
652 |
+
path:
|
653 |
+
- '**/details_harness|winogrande|5_2023-10-23T08-06-32.820862.parquet'
|
654 |
+
- split: latest
|
655 |
+
path:
|
656 |
+
- '**/details_harness|winogrande|5_2023-10-23T08-06-32.820862.parquet'
|
657 |
- config_name: results
|
658 |
data_files:
|
659 |
- split: 2023_10_04T03_19_48.694479
|
660 |
path:
|
661 |
- results_2023-10-04T03-19-48.694479.parquet
|
662 |
+
- split: 2023_10_23T08_06_32.820862
|
663 |
+
path:
|
664 |
+
- results_2023-10-23T08-06-32.820862.parquet
|
665 |
- split: latest
|
666 |
path:
|
667 |
+
- results_2023-10-23T08-06-32.820862.parquet
|
668 |
---
|
669 |
|
670 |
# Dataset Card for Evaluation run of NewstaR/Koss-7B-chat
|
|
|
681 |
|
682 |
Dataset automatically created during the evaluation run of model [NewstaR/Koss-7B-chat](https://huggingface.co/NewstaR/Koss-7B-chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
|
683 |
|
684 |
+
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
|
685 |
|
686 |
+
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.
|
687 |
|
688 |
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)).
|
689 |
|
|
|
691 |
```python
|
692 |
from datasets import load_dataset
|
693 |
data = load_dataset("open-llm-leaderboard/details_NewstaR__Koss-7B-chat",
|
694 |
+
"harness_winogrande_5",
|
695 |
split="train")
|
696 |
```
|
697 |
|
698 |
## Latest results
|
699 |
|
700 |
+
These are the [latest results from run 2023-10-23T08:06:32.820862](https://huggingface.co/datasets/open-llm-leaderboard/details_NewstaR__Koss-7B-chat/blob/main/results_2023-10-23T08-06-32.820862.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):
|
701 |
|
702 |
```python
|
703 |
{
|
704 |
"all": {
|
705 |
+
"em": 0.06333892617449664,
|
706 |
+
"em_stderr": 0.002494400790190545,
|
707 |
+
"f1": 0.12617449664429503,
|
708 |
+
"f1_stderr": 0.002812859883562843,
|
709 |
+
"acc": 0.39549166962367155,
|
710 |
+
"acc_stderr": 0.009921949302668327
|
711 |
+
},
|
712 |
+
"harness|drop|3": {
|
713 |
+
"em": 0.06333892617449664,
|
714 |
+
"em_stderr": 0.002494400790190545,
|
715 |
+
"f1": 0.12617449664429503,
|
716 |
+
"f1_stderr": 0.002812859883562843
|
717 |
+
},
|
718 |
+
"harness|gsm8k|5": {
|
719 |
+
"acc": 0.07354056103108415,
|
720 |
+
"acc_stderr": 0.0071898357543652685
|
721 |
+
},
|
722 |
+
"harness|winogrande|5": {
|
723 |
+
"acc": 0.7174427782162589,
|
724 |
+
"acc_stderr": 0.012654062850971384
|
|
|
|
|
|
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|
725 |
}
|
726 |
}
|
727 |
```
|