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README.md
CHANGED
@@ -3,8 +3,8 @@ pretty_name: Evaluation run of Undi95/ReMM-v2.1-L2-13B
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dataset_summary: "Dataset automatically created during the evaluation run of model\
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\ [Undi95/ReMM-v2.1-L2-13B](https://huggingface.co/Undi95/ReMM-v2.1-L2-13B) on the\
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\ [Open 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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@@ -12,172 +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_Undi95__ReMM-v2.1-L2-13B\"\
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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\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \
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\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n \
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\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5185185185185185,\n\
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\ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.5185185185185185,\n\
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\ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\
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: {\n \"acc\": 0.5263157894736842,\n \"acc_stderr\": 0.04063302731486671,\n\
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\ \"acc_norm\": 0.5263157894736842,\n \"acc_norm_stderr\": 0.04063302731486671\n\
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\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
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\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \
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\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
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: {\n \"acc\": 0.5886792452830188,\n \"acc_stderr\": 0.030285009259009794,\n\
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\ \"acc_norm\": 0.5886792452830188,\n \"acc_norm_stderr\": 0.030285009259009794\n\
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\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5972222222222222,\n\
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\ \"acc_stderr\": 0.04101405519842426,\n \"acc_norm\": 0.5972222222222222,\n\
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\ \"acc_norm_stderr\": 0.04101405519842426\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
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: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
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\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
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\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
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: 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n\
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\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
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: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
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\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
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\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5317919075144508,\n\
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\ \"acc_stderr\": 0.03804749744364764,\n \"acc_norm\": 0.5317919075144508,\n\
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\ \"acc_norm_stderr\": 0.03804749744364764\n },\n \"harness|hendrycksTest-college_physics|5\"\
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: {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.042801058373643966,\n\
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\ \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.042801058373643966\n\
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\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
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\ 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n\
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\ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
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: {\n \"acc\": 0.4808510638297872,\n \"acc_stderr\": 0.03266204299064678,\n\
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\ \"acc_norm\": 0.4808510638297872,\n \"acc_norm_stderr\": 0.03266204299064678\n\
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\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n\
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\ \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n\
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\ \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
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: {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.041665675771015785,\n\
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\ \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.041665675771015785\n\
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\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
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: 0.3386243386243386,\n \"acc_stderr\": 0.024373197867983063,\n \"\
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acc_norm\": 0.3386243386243386,\n \"acc_norm_stderr\": 0.024373197867983063\n\
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\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3253968253968254,\n\
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\ \"acc_stderr\": 0.041905964388711366,\n \"acc_norm\": 0.3253968253968254,\n\
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\ \"acc_norm_stderr\": 0.041905964388711366\n },\n \"harness|hendrycksTest-global_facts|5\"\
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: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
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\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \
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\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.635483870967742,\n\
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\ \"acc_stderr\": 0.027379871229943255,\n \"acc_norm\": 0.635483870967742,\n\
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\ \"acc_norm_stderr\": 0.027379871229943255\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
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: {\n \"acc\": 0.4433497536945813,\n \"acc_stderr\": 0.03495334582162934,\n\
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\ \"acc_norm\": 0.4433497536945813,\n \"acc_norm_stderr\": 0.03495334582162934\n\
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\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
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\ \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\"\
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: 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
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: {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.037131580674819135,\n\
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\ \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.037131580674819135\n\
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\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
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: 0.7121212121212122,\n \"acc_stderr\": 0.03225883512300992,\n \"\
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acc_norm\": 0.7121212121212122,\n \"acc_norm_stderr\": 0.03225883512300992\n\
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\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
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\ \"acc\": 0.7927461139896373,\n \"acc_stderr\": 0.029252823291803638,\n\
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\ \"acc_norm\": 0.7927461139896373,\n \"acc_norm_stderr\": 0.029252823291803638\n\
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\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
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\ \"acc\": 0.5205128205128206,\n \"acc_stderr\": 0.02532966316348994,\n \
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\ \"acc_norm\": 0.5205128205128206,\n \"acc_norm_stderr\": 0.02532966316348994\n\
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\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
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acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \
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\ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n\
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\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
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\ \"acc\": 0.5840336134453782,\n \"acc_stderr\": 0.032016501007396114,\n\
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\ \"acc_norm\": 0.5840336134453782,\n \"acc_norm_stderr\": 0.032016501007396114\n\
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\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
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: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
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acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
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\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
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: 0.7174311926605504,\n \"acc_stderr\": 0.01930424349770715,\n \"\
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acc_norm\": 0.7174311926605504,\n \"acc_norm_stderr\": 0.01930424349770715\n\
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\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
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: 0.41203703703703703,\n \"acc_stderr\": 0.03356787758160835,\n \"\
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acc_norm\": 0.41203703703703703,\n \"acc_norm_stderr\": 0.03356787758160835\n\
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\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
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: 0.7598039215686274,\n \"acc_stderr\": 0.02998373305591361,\n \"\
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acc_norm\": 0.7598039215686274,\n \"acc_norm_stderr\": 0.02998373305591361\n\
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\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
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acc\": 0.759493670886076,\n \"acc_stderr\": 0.02782078198114968,\n \
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\ \"acc_norm\": 0.759493670886076,\n \"acc_norm_stderr\": 0.02782078198114968\n\
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\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
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\ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\
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\ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
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: {\n \"acc\": 0.6183206106870229,\n \"acc_stderr\": 0.04260735157644559,\n\
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\ \"acc_norm\": 0.6183206106870229,\n \"acc_norm_stderr\": 0.04260735157644559\n\
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\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
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\ 0.743801652892562,\n \"acc_stderr\": 0.039849796533028725,\n \"\
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acc_norm\": 0.743801652892562,\n \"acc_norm_stderr\": 0.039849796533028725\n\
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\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\
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\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \
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\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
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: {\n \"acc\": 0.6809815950920245,\n \"acc_stderr\": 0.03661997551073836,\n\
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\ \"acc_norm\": 0.6809815950920245,\n \"acc_norm_stderr\": 0.03661997551073836\n\
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\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.35714285714285715,\n\
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\ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.35714285714285715,\n\
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\ \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness|hendrycksTest-management|5\"\
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: {\n \"acc\": 0.6990291262135923,\n \"acc_stderr\": 0.04541609446503948,\n\
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\ \"acc_norm\": 0.6990291262135923,\n \"acc_norm_stderr\": 0.04541609446503948\n\
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\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7948717948717948,\n\
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\ \"acc_stderr\": 0.026453508054040332,\n \"acc_norm\": 0.7948717948717948,\n\
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\ \"acc_norm_stderr\": 0.026453508054040332\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
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: {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \
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\ \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n \
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\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7662835249042146,\n\
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\ \"acc_stderr\": 0.01513338327898883,\n \"acc_norm\": 0.7662835249042146,\n\
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\ \"acc_norm_stderr\": 0.01513338327898883\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
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: {\n \"acc\": 0.6329479768786127,\n \"acc_stderr\": 0.02595005433765407,\n\
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\ \"acc_norm\": 0.6329479768786127,\n \"acc_norm_stderr\": 0.02595005433765407\n\
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\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.45027932960893857,\n\
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\ \"acc_stderr\": 0.016639615236845803,\n \"acc_norm\": 0.45027932960893857,\n\
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\ \"acc_norm_stderr\": 0.016639615236845803\n },\n \"harness|hendrycksTest-nutrition|5\"\
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: {\n \"acc\": 0.6078431372549019,\n \"acc_stderr\": 0.027956046165424523,\n\
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\ \"acc_norm\": 0.6078431372549019,\n \"acc_norm_stderr\": 0.027956046165424523\n\
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\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6430868167202572,\n\
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\ \"acc_stderr\": 0.027210420375934023,\n \"acc_norm\": 0.6430868167202572,\n\
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\ \"acc_norm_stderr\": 0.027210420375934023\n },\n \"harness|hendrycksTest-prehistory|5\"\
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: {\n \"acc\": 0.6203703703703703,\n \"acc_stderr\": 0.027002521034516478,\n\
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\ \"acc_norm\": 0.6203703703703703,\n \"acc_norm_stderr\": 0.027002521034516478\n\
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\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
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acc\": 0.4219858156028369,\n \"acc_stderr\": 0.029462189233370593,\n \
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\ \"acc_norm\": 0.4219858156028369,\n \"acc_norm_stderr\": 0.029462189233370593\n\
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\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4276401564537158,\n\
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\ \"acc_stderr\": 0.012635799922765848,\n \"acc_norm\": 0.4276401564537158,\n\
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\ \"acc_norm_stderr\": 0.012635799922765848\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
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: {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.03035969707904612,\n\
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\ \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03035969707904612\n\
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\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
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acc\": 0.5833333333333334,\n \"acc_stderr\": 0.019944914136873586,\n \
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\ \"acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.019944914136873586\n\
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\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
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\ \"acc_stderr\": 0.046075820907199756,\n \"acc_norm\": 0.6363636363636364,\n\
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\ \"acc_norm_stderr\": 0.046075820907199756\n },\n \"harness|hendrycksTest-security_studies|5\"\
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: {\n \"acc\": 0.6204081632653061,\n \"acc_stderr\": 0.03106721126287247,\n\
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\ \"acc_norm\": 0.6204081632653061,\n \"acc_norm_stderr\": 0.03106721126287247\n\
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\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.681592039800995,\n\
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\ \"acc_stderr\": 0.03294118479054095,\n \"acc_norm\": 0.681592039800995,\n\
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\ \"acc_norm_stderr\": 0.03294118479054095\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
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: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \
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\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \
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\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4759036144578313,\n\
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\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.4759036144578313,\n\
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\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
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: {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.03158149539338733,\n\
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\ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.03158149539338733\n\
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\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3659730722154223,\n\
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\ \"mc1_stderr\": 0.016862941684088376,\n \"mc2\": 0.502982198851368,\n\
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\ \"mc2_stderr\": 0.015602709737779776\n }\n}\n```"
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repo_url: https://huggingface.co/Undi95/ReMM-v2.1-L2-13B
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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_2023-09-18T13-43-56.304128.parquet'
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- config_name: harness_hellaswag_10
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data_files:
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- split: 2023_09_18T13_43_56.304128
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- split: latest
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path:
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- '**/details_harness|truthfulqa:mc|0_2023-09-18T13-43-56.304128.parquet'
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- config_name: results
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data_files:
|
787 |
- split: 2023_09_18T13_43_56.304128
|
788 |
path:
|
789 |
- results_2023-09-18T13-43-56.304128.parquet
|
|
|
|
|
|
|
790 |
- split: latest
|
791 |
path:
|
792 |
-
- results_2023-
|
793 |
---
|
794 |
|
795 |
# Dataset Card for Evaluation run of Undi95/ReMM-v2.1-L2-13B
|
@@ -806,9 +681,9 @@ configs:
|
|
806 |
|
807 |
Dataset automatically created during the evaluation run of model [Undi95/ReMM-v2.1-L2-13B](https://huggingface.co/Undi95/ReMM-v2.1-L2-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
|
808 |
|
809 |
-
The dataset is composed of
|
810 |
|
811 |
-
The dataset has been created from
|
812 |
|
813 |
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)).
|
814 |
|
@@ -816,385 +691,37 @@ To load the details from a run, you can for instance do the following:
|
|
816 |
```python
|
817 |
from datasets import load_dataset
|
818 |
data = load_dataset("open-llm-leaderboard/details_Undi95__ReMM-v2.1-L2-13B",
|
819 |
-
"
|
820 |
split="train")
|
821 |
```
|
822 |
|
823 |
## Latest results
|
824 |
|
825 |
-
These are the [latest results from run 2023-
|
826 |
|
827 |
```python
|
828 |
{
|
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"all": {
|
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"
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"
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"
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833 |
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"
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"
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"
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"
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"
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848 |
-
"
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849 |
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"
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850 |
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851 |
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856 |
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862 |
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868 |
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892 |
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895 |
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-
"acc_norm_stderr": 0.029462189233370593
|
1138 |
-
},
|
1139 |
-
"harness|hendrycksTest-professional_law|5": {
|
1140 |
-
"acc": 0.4276401564537158,
|
1141 |
-
"acc_stderr": 0.012635799922765848,
|
1142 |
-
"acc_norm": 0.4276401564537158,
|
1143 |
-
"acc_norm_stderr": 0.012635799922765848
|
1144 |
-
},
|
1145 |
-
"harness|hendrycksTest-professional_medicine|5": {
|
1146 |
-
"acc": 0.5147058823529411,
|
1147 |
-
"acc_stderr": 0.03035969707904612,
|
1148 |
-
"acc_norm": 0.5147058823529411,
|
1149 |
-
"acc_norm_stderr": 0.03035969707904612
|
1150 |
-
},
|
1151 |
-
"harness|hendrycksTest-professional_psychology|5": {
|
1152 |
-
"acc": 0.5833333333333334,
|
1153 |
-
"acc_stderr": 0.019944914136873586,
|
1154 |
-
"acc_norm": 0.5833333333333334,
|
1155 |
-
"acc_norm_stderr": 0.019944914136873586
|
1156 |
-
},
|
1157 |
-
"harness|hendrycksTest-public_relations|5": {
|
1158 |
-
"acc": 0.6363636363636364,
|
1159 |
-
"acc_stderr": 0.046075820907199756,
|
1160 |
-
"acc_norm": 0.6363636363636364,
|
1161 |
-
"acc_norm_stderr": 0.046075820907199756
|
1162 |
-
},
|
1163 |
-
"harness|hendrycksTest-security_studies|5": {
|
1164 |
-
"acc": 0.6204081632653061,
|
1165 |
-
"acc_stderr": 0.03106721126287247,
|
1166 |
-
"acc_norm": 0.6204081632653061,
|
1167 |
-
"acc_norm_stderr": 0.03106721126287247
|
1168 |
-
},
|
1169 |
-
"harness|hendrycksTest-sociology|5": {
|
1170 |
-
"acc": 0.681592039800995,
|
1171 |
-
"acc_stderr": 0.03294118479054095,
|
1172 |
-
"acc_norm": 0.681592039800995,
|
1173 |
-
"acc_norm_stderr": 0.03294118479054095
|
1174 |
-
},
|
1175 |
-
"harness|hendrycksTest-us_foreign_policy|5": {
|
1176 |
-
"acc": 0.84,
|
1177 |
-
"acc_stderr": 0.03684529491774708,
|
1178 |
-
"acc_norm": 0.84,
|
1179 |
-
"acc_norm_stderr": 0.03684529491774708
|
1180 |
-
},
|
1181 |
-
"harness|hendrycksTest-virology|5": {
|
1182 |
-
"acc": 0.4759036144578313,
|
1183 |
-
"acc_stderr": 0.03887971849597264,
|
1184 |
-
"acc_norm": 0.4759036144578313,
|
1185 |
-
"acc_norm_stderr": 0.03887971849597264
|
1186 |
-
},
|
1187 |
-
"harness|hendrycksTest-world_religions|5": {
|
1188 |
-
"acc": 0.783625730994152,
|
1189 |
-
"acc_stderr": 0.03158149539338733,
|
1190 |
-
"acc_norm": 0.783625730994152,
|
1191 |
-
"acc_norm_stderr": 0.03158149539338733
|
1192 |
-
},
|
1193 |
-
"harness|truthfulqa:mc|0": {
|
1194 |
-
"mc1": 0.3659730722154223,
|
1195 |
-
"mc1_stderr": 0.016862941684088376,
|
1196 |
-
"mc2": 0.502982198851368,
|
1197 |
-
"mc2_stderr": 0.015602709737779776
|
1198 |
}
|
1199 |
}
|
1200 |
```
|
|
|
3 |
dataset_summary: "Dataset automatically created during the evaluation run of model\
|
4 |
\ [Undi95/ReMM-v2.1-L2-13B](https://huggingface.co/Undi95/ReMM-v2.1-L2-13B) on the\
|
5 |
\ [Open 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_Undi95__ReMM-v2.1-L2-13B\"\
|
15 |
+
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
|
16 |
+
These are the [latest results from run 2023-10-28T01:20:40.320894](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__ReMM-v2.1-L2-13B/blob/main/results_2023-10-28T01-20-40.320894.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\": 0.061556208053691275,\n\
|
20 |
+
\ \"em_stderr\": 0.0024613859292232257,\n \"f1\": 0.12617135067114038,\n\
|
21 |
+
\ \"f1_stderr\": 0.002725179835134867,\n \"acc\": 0.44332154720067884,\n\
|
22 |
+
\ \"acc_stderr\": 0.010599334769481\n },\n \"harness|drop|3\": {\n\
|
23 |
+
\ \"em\": 0.061556208053691275,\n \"em_stderr\": 0.0024613859292232257,\n\
|
24 |
+
\ \"f1\": 0.12617135067114038,\n \"f1_stderr\": 0.002725179835134867\n\
|
25 |
+
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12736921910538287,\n \
|
26 |
+
\ \"acc_stderr\": 0.009183110326737822\n },\n \"harness|winogrande|5\"\
|
27 |
+
: {\n \"acc\": 0.7592738752959748,\n \"acc_stderr\": 0.012015559212224178\n\
|
28 |
+
\ }\n}\n```"
|
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|
29 |
repo_url: https://huggingface.co/Undi95/ReMM-v2.1-L2-13B
|
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_2023-09-18T13-43-56.304128.parquet'
|
41 |
+
- config_name: harness_drop_3
|
42 |
+
data_files:
|
43 |
+
- split: 2023_10_28T01_20_40.320894
|
44 |
+
path:
|
45 |
+
- '**/details_harness|drop|3_2023-10-28T01-20-40.320894.parquet'
|
46 |
+
- split: latest
|
47 |
+
path:
|
48 |
+
- '**/details_harness|drop|3_2023-10-28T01-20-40.320894.parquet'
|
49 |
+
- config_name: harness_gsm8k_5
|
50 |
+
data_files:
|
51 |
+
- split: 2023_10_28T01_20_40.320894
|
52 |
+
path:
|
53 |
+
- '**/details_harness|gsm8k|5_2023-10-28T01-20-40.320894.parquet'
|
54 |
+
- split: latest
|
55 |
+
path:
|
56 |
+
- '**/details_harness|gsm8k|5_2023-10-28T01-20-40.320894.parquet'
|
57 |
- config_name: harness_hellaswag_10
|
58 |
data_files:
|
59 |
- split: 2023_09_18T13_43_56.304128
|
|
|
646 |
- split: latest
|
647 |
path:
|
648 |
- '**/details_harness|truthfulqa:mc|0_2023-09-18T13-43-56.304128.parquet'
|
649 |
+
- config_name: harness_winogrande_5
|
650 |
+
data_files:
|
651 |
+
- split: 2023_10_28T01_20_40.320894
|
652 |
+
path:
|
653 |
+
- '**/details_harness|winogrande|5_2023-10-28T01-20-40.320894.parquet'
|
654 |
+
- split: latest
|
655 |
+
path:
|
656 |
+
- '**/details_harness|winogrande|5_2023-10-28T01-20-40.320894.parquet'
|
657 |
- config_name: results
|
658 |
data_files:
|
659 |
- split: 2023_09_18T13_43_56.304128
|
660 |
path:
|
661 |
- results_2023-09-18T13-43-56.304128.parquet
|
662 |
+
- split: 2023_10_28T01_20_40.320894
|
663 |
+
path:
|
664 |
+
- results_2023-10-28T01-20-40.320894.parquet
|
665 |
- split: latest
|
666 |
path:
|
667 |
+
- results_2023-10-28T01-20-40.320894.parquet
|
668 |
---
|
669 |
|
670 |
# Dataset Card for Evaluation run of Undi95/ReMM-v2.1-L2-13B
|
|
|
681 |
|
682 |
Dataset automatically created during the evaluation run of model [Undi95/ReMM-v2.1-L2-13B](https://huggingface.co/Undi95/ReMM-v2.1-L2-13B) 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_Undi95__ReMM-v2.1-L2-13B",
|
694 |
+
"harness_winogrande_5",
|
695 |
split="train")
|
696 |
```
|
697 |
|
698 |
## Latest results
|
699 |
|
700 |
+
These are the [latest results from run 2023-10-28T01:20:40.320894](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__ReMM-v2.1-L2-13B/blob/main/results_2023-10-28T01-20-40.320894.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": 0.061556208053691275,
|
706 |
+
"em_stderr": 0.0024613859292232257,
|
707 |
+
"f1": 0.12617135067114038,
|
708 |
+
"f1_stderr": 0.002725179835134867,
|
709 |
+
"acc": 0.44332154720067884,
|
710 |
+
"acc_stderr": 0.010599334769481
|
711 |
+
},
|
712 |
+
"harness|drop|3": {
|
713 |
+
"em": 0.061556208053691275,
|
714 |
+
"em_stderr": 0.0024613859292232257,
|
715 |
+
"f1": 0.12617135067114038,
|
716 |
+
"f1_stderr": 0.002725179835134867
|
717 |
+
},
|
718 |
+
"harness|gsm8k|5": {
|
719 |
+
"acc": 0.12736921910538287,
|
720 |
+
"acc_stderr": 0.009183110326737822
|
721 |
+
},
|
722 |
+
"harness|winogrande|5": {
|
723 |
+
"acc": 0.7592738752959748,
|
724 |
+
"acc_stderr": 0.012015559212224178
|
|
|
|
|
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|
725 |
}
|
726 |
}
|
727 |
```
|