1114 lines
41 KiB
JSON
1114 lines
41 KiB
JSON
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{
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"results": {
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"agieval": {
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"acc,none": 0.3056361877116594,
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"acc_stderr,none": 0.004828557526230232,
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||
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"alias": "agieval"
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},
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||
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"agieval_aqua_rat": {
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"alias": " - agieval_aqua_rat",
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"acc,none": 0.1889763779527559,
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"acc_stderr,none": 0.02461275630319305,
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"acc_norm,none": 0.2047244094488189,
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"acc_norm_stderr,none": 0.025367833544738514
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},
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"agieval_gaokao_biology": {
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"alias": " - agieval_gaokao_biology",
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"acc,none": 0.2619047619047619,
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"acc_stderr,none": 0.03041268445992877,
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"acc_norm,none": 0.2904761904761905,
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"acc_norm_stderr,none": 0.03140260048069876
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},
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"agieval_gaokao_chemistry": {
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"alias": " - agieval_gaokao_chemistry",
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"acc,none": 0.21739130434782608,
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"acc_stderr,none": 0.02873821625473249,
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"acc_norm,none": 0.23671497584541062,
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"acc_norm_stderr,none": 0.02961574266946006
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},
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"agieval_gaokao_chinese": {
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"alias": " - agieval_gaokao_chinese",
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"acc,none": 0.21544715447154472,
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"acc_stderr,none": 0.026266272165576837,
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"acc_norm,none": 0.2032520325203252,
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"acc_norm_stderr,none": 0.0257095744729136
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},
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"agieval_gaokao_english": {
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"alias": " - agieval_gaokao_english",
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"acc,none": 0.5065359477124183,
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"acc_stderr,none": 0.02862747055055606,
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"acc_norm,none": 0.49673202614379086,
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"acc_norm_stderr,none": 0.02862930519400355
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},
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"agieval_gaokao_geography": {
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"alias": " - agieval_gaokao_geography",
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"acc,none": 0.2914572864321608,
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"acc_stderr,none": 0.03229519279811605,
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"acc_norm,none": 0.3065326633165829,
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"acc_norm_stderr,none": 0.032765650099572274
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},
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"agieval_gaokao_history": {
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"alias": " - agieval_gaokao_history",
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"acc,none": 0.28936170212765955,
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"acc_stderr,none": 0.029644006577009618,
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"acc_norm,none": 0.24680851063829787,
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"acc_norm_stderr,none": 0.02818544130123409
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},
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"agieval_gaokao_mathcloze": {
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"alias": " - agieval_gaokao_mathcloze",
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"acc,none": 0.03389830508474576,
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"acc_stderr,none": 0.016730444637044904
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},
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"agieval_gaokao_mathqa": {
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"alias": " - agieval_gaokao_mathqa",
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"acc,none": 0.2706552706552707,
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"acc_stderr,none": 0.02374874403426679,
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"acc_norm,none": 0.29914529914529914,
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"acc_norm_stderr,none": 0.02447490780047234
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},
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"agieval_gaokao_physics": {
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"alias": " - agieval_gaokao_physics",
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"acc,none": 0.27,
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"acc_stderr,none": 0.031471451528433385,
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"acc_norm,none": 0.305,
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"acc_norm_stderr,none": 0.032637417254205714
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},
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"agieval_jec_qa_ca": {
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"alias": " - agieval_jec_qa_ca",
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"acc,none": 0.47847847847847846,
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"acc_stderr,none": 0.015812555072068857,
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"acc_norm,none": 0.44644644644644643,
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"acc_norm_stderr,none": 0.015736177154718242
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},
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"agieval_jec_qa_kd": {
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"alias": " - agieval_jec_qa_kd",
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"acc,none": 0.491,
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"acc_stderr,none": 0.015816736995005392,
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"acc_norm,none": 0.5,
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"acc_norm_stderr,none": 0.015819299929208316
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},
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"agieval_logiqa_en": {
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"alias": " - agieval_logiqa_en",
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"acc,none": 0.2764976958525346,
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"acc_stderr,none": 0.017543209075825187,
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"acc_norm,none": 0.30261136712749614,
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"acc_norm_stderr,none": 0.01801869659815883
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},
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"agieval_logiqa_zh": {
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"alias": " - agieval_logiqa_zh",
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"acc,none": 0.250384024577573,
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"acc_stderr,none": 0.016992843055190048,
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"acc_norm,none": 0.27956989247311825,
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"acc_norm_stderr,none": 0.01760290918682245
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},
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"agieval_lsat_ar": {
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"alias": " - agieval_lsat_ar",
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"acc,none": 0.1565217391304348,
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"acc_stderr,none": 0.02401079490762759,
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"acc_norm,none": 0.16956521739130434,
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"acc_norm_stderr,none": 0.024797243687717647
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},
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"agieval_lsat_lr": {
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"alias": " - agieval_lsat_lr",
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"acc,none": 0.30980392156862746,
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"acc_stderr,none": 0.020496080019546087,
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"acc_norm,none": 0.2784313725490196,
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"acc_norm_stderr,none": 0.019867307525414934
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},
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"agieval_lsat_rc": {
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"alias": " - agieval_lsat_rc",
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"acc,none": 0.30855018587360594,
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"acc_stderr,none": 0.02821472627233907,
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"acc_norm,none": 0.25650557620817843,
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"acc_norm_stderr,none": 0.026675948246675078
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},
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"agieval_math": {
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"alias": " - agieval_math",
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"acc,none": 0.065,
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"acc_stderr,none": 0.007799733061832023
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},
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"agieval_sat_en": {
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"alias": " - agieval_sat_en",
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"acc,none": 0.46601941747572817,
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"acc_stderr,none": 0.03484077510348,
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"acc_norm,none": 0.36893203883495146,
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"acc_norm_stderr,none": 0.03370034302177868
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},
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"agieval_sat_en_without_passage": {
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"alias": " - agieval_sat_en_without_passage",
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"acc,none": 0.35436893203883496,
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"acc_stderr,none": 0.03340743250473595,
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"acc_norm,none": 0.30097087378640774,
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"acc_norm_stderr,none": 0.03203560571847412
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},
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"agieval_sat_math": {
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"alias": " - agieval_sat_math",
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"acc,none": 0.31363636363636366,
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"acc_stderr,none": 0.031352218760292705,
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"acc_norm,none": 0.2636363636363636,
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"acc_norm_stderr,none": 0.029773285764727497
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}
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},
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"groups": {
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"agieval": {
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"acc,none": 0.3056361877116594,
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"acc_stderr,none": 0.004828557526230232,
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"alias": "agieval"
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}
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},
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"group_subtasks": {
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"agieval": [
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"agieval_gaokao_biology",
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"agieval_gaokao_chemistry",
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"agieval_gaokao_chinese",
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"agieval_gaokao_geography",
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"agieval_gaokao_history",
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"agieval_gaokao_mathcloze",
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"agieval_gaokao_mathqa",
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"agieval_gaokao_physics",
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"agieval_jec_qa_ca",
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"agieval_jec_qa_kd",
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"agieval_logiqa_zh",
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"agieval_aqua_rat",
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"agieval_gaokao_english",
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"agieval_logiqa_en",
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"agieval_lsat_ar",
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"agieval_lsat_lr",
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"agieval_lsat_rc",
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"agieval_math",
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"agieval_sat_en_without_passage",
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"agieval_sat_en",
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"agieval_sat_math"
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]
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},
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"configs": {
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"agieval_aqua_rat": {
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"task": "agieval_aqua_rat",
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"dataset_path": "hails/agieval-aqua-rat",
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"test_split": "test",
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||
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"doc_to_text": "{{query}}",
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"doc_to_target": "{{gold}}",
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"doc_to_choice": "{{choices}}",
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||
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"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
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"description": "",
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||
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
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"metric_list": [
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{
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"metric": "acc",
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||
|
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"aggregation": "mean",
|
||
|
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"higher_is_better": true
|
||
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},
|
||
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{
|
||
|
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"metric": "acc_norm",
|
||
|
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"aggregation": "mean",
|
||
|
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"higher_is_better": true
|
||
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}
|
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],
|
||
|
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"output_type": "multiple_choice",
|
||
|
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"repeats": 1,
|
||
|
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"should_decontaminate": false,
|
||
|
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"metadata": {
|
||
|
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"version": 1.0
|
||
|
|
}
|
||
|
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},
|
||
|
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"agieval_gaokao_biology": {
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||
|
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"task": "agieval_gaokao_biology",
|
||
|
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"dataset_path": "hails/agieval-gaokao-biology",
|
||
|
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"test_split": "test",
|
||
|
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"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
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"description": "",
|
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|
|
"target_delimiter": " ",
|
||
|
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"fewshot_delimiter": "\n\n",
|
||
|
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"num_fewshot": 0,
|
||
|
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"metric_list": [
|
||
|
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{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
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|
|
"higher_is_better": true
|
||
|
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},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_gaokao_chemistry": {
|
||
|
|
"task": "agieval_gaokao_chemistry",
|
||
|
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"dataset_path": "hails/agieval-gaokao-chemistry",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
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"metric_list": [
|
||
|
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{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_gaokao_chinese": {
|
||
|
|
"task": "agieval_gaokao_chinese",
|
||
|
|
"dataset_path": "hails/agieval-gaokao-chinese",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_gaokao_english": {
|
||
|
|
"task": "agieval_gaokao_english",
|
||
|
|
"dataset_path": "hails/agieval-gaokao-english",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_gaokao_geography": {
|
||
|
|
"task": "agieval_gaokao_geography",
|
||
|
|
"dataset_path": "hails/agieval-gaokao-geography",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_gaokao_history": {
|
||
|
|
"task": "agieval_gaokao_history",
|
||
|
|
"dataset_path": "hails/agieval-gaokao-history",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_gaokao_mathcloze": {
|
||
|
|
"task": "agieval_gaokao_mathcloze",
|
||
|
|
"dataset_path": "hails/agieval-gaokao-mathcloze",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{answer}}",
|
||
|
|
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidate = results[0]\n\n gold = doc[\"answer\"]\n\n if not gold:\n print(doc, candidate, gold)\n if is_equiv(candidate, gold):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"acc\": retval,\n }\n return results\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "generate_until",
|
||
|
|
"generation_kwargs": {
|
||
|
|
"max_gen_toks": 32,
|
||
|
|
"do_sample": false,
|
||
|
|
"temperature": 0.0,
|
||
|
|
"until": [
|
||
|
|
"Q:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_gaokao_mathqa": {
|
||
|
|
"task": "agieval_gaokao_mathqa",
|
||
|
|
"dataset_path": "hails/agieval-gaokao-mathqa",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_gaokao_physics": {
|
||
|
|
"task": "agieval_gaokao_physics",
|
||
|
|
"dataset_path": "hails/agieval-gaokao-physics",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_jec_qa_ca": {
|
||
|
|
"task": "agieval_jec_qa_ca",
|
||
|
|
"dataset_path": "hails/agieval-jec-qa-ca",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_jec_qa_kd": {
|
||
|
|
"task": "agieval_jec_qa_kd",
|
||
|
|
"dataset_path": "hails/agieval-jec-qa-kd",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_logiqa_en": {
|
||
|
|
"task": "agieval_logiqa_en",
|
||
|
|
"dataset_path": "hails/agieval-logiqa-en",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_logiqa_zh": {
|
||
|
|
"task": "agieval_logiqa_zh",
|
||
|
|
"dataset_path": "hails/agieval-logiqa-zh",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_lsat_ar": {
|
||
|
|
"task": "agieval_lsat_ar",
|
||
|
|
"dataset_path": "hails/agieval-lsat-ar",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_lsat_lr": {
|
||
|
|
"task": "agieval_lsat_lr",
|
||
|
|
"dataset_path": "hails/agieval-lsat-lr",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_lsat_rc": {
|
||
|
|
"task": "agieval_lsat_rc",
|
||
|
|
"dataset_path": "hails/agieval-lsat-rc",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_math": {
|
||
|
|
"task": "agieval_math",
|
||
|
|
"dataset_path": "hails/agieval-math",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{answer}}",
|
||
|
|
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidate = results[0]\n\n gold = doc[\"answer\"]\n\n if not gold:\n print(doc, candidate, gold)\n if is_equiv(candidate, gold):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"acc\": retval,\n }\n return results\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "generate_until",
|
||
|
|
"generation_kwargs": {
|
||
|
|
"max_gen_toks": 32,
|
||
|
|
"do_sample": false,
|
||
|
|
"temperature": 0.0,
|
||
|
|
"until": [
|
||
|
|
"Q:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_sat_en": {
|
||
|
|
"task": "agieval_sat_en",
|
||
|
|
"dataset_path": "hails/agieval-sat-en",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_sat_en_without_passage": {
|
||
|
|
"task": "agieval_sat_en_without_passage",
|
||
|
|
"dataset_path": "hails/agieval-sat-en-without-passage",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_sat_math": {
|
||
|
|
"task": "agieval_sat_math",
|
||
|
|
"dataset_path": "hails/agieval-sat-math",
|
||
|
|
"test_split": "test",
|
||
|
|
"doc_to_text": "{{query}}",
|
||
|
|
"doc_to_target": "{{gold}}",
|
||
|
|
"doc_to_choice": "{{choices}}",
|
||
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"versions": {
|
||
|
|
"agieval": 0.0,
|
||
|
|
"agieval_aqua_rat": 1.0,
|
||
|
|
"agieval_gaokao_biology": 1.0,
|
||
|
|
"agieval_gaokao_chemistry": 1.0,
|
||
|
|
"agieval_gaokao_chinese": 1.0,
|
||
|
|
"agieval_gaokao_english": 1.0,
|
||
|
|
"agieval_gaokao_geography": 1.0,
|
||
|
|
"agieval_gaokao_history": 1.0,
|
||
|
|
"agieval_gaokao_mathcloze": 1.0,
|
||
|
|
"agieval_gaokao_mathqa": 1.0,
|
||
|
|
"agieval_gaokao_physics": 1.0,
|
||
|
|
"agieval_jec_qa_ca": 1.0,
|
||
|
|
"agieval_jec_qa_kd": 1.0,
|
||
|
|
"agieval_logiqa_en": 1.0,
|
||
|
|
"agieval_logiqa_zh": 1.0,
|
||
|
|
"agieval_lsat_ar": 1.0,
|
||
|
|
"agieval_lsat_lr": 1.0,
|
||
|
|
"agieval_lsat_rc": 1.0,
|
||
|
|
"agieval_math": 1.0,
|
||
|
|
"agieval_sat_en": 1.0,
|
||
|
|
"agieval_sat_en_without_passage": 1.0,
|
||
|
|
"agieval_sat_math": 1.0
|
||
|
|
},
|
||
|
|
"n-shot": {
|
||
|
|
"agieval_aqua_rat": 0,
|
||
|
|
"agieval_gaokao_biology": 0,
|
||
|
|
"agieval_gaokao_chemistry": 0,
|
||
|
|
"agieval_gaokao_chinese": 0,
|
||
|
|
"agieval_gaokao_english": 0,
|
||
|
|
"agieval_gaokao_geography": 0,
|
||
|
|
"agieval_gaokao_history": 0,
|
||
|
|
"agieval_gaokao_mathcloze": 0,
|
||
|
|
"agieval_gaokao_mathqa": 0,
|
||
|
|
"agieval_gaokao_physics": 0,
|
||
|
|
"agieval_jec_qa_ca": 0,
|
||
|
|
"agieval_jec_qa_kd": 0,
|
||
|
|
"agieval_logiqa_en": 0,
|
||
|
|
"agieval_logiqa_zh": 0,
|
||
|
|
"agieval_lsat_ar": 0,
|
||
|
|
"agieval_lsat_lr": 0,
|
||
|
|
"agieval_lsat_rc": 0,
|
||
|
|
"agieval_math": 0,
|
||
|
|
"agieval_sat_en": 0,
|
||
|
|
"agieval_sat_en_without_passage": 0,
|
||
|
|
"agieval_sat_math": 0
|
||
|
|
},
|
||
|
|
"higher_is_better": {
|
||
|
|
"agieval": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_aqua_rat": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_gaokao_biology": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_gaokao_chemistry": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_gaokao_chinese": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_gaokao_english": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_gaokao_geography": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_gaokao_history": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_gaokao_mathcloze": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"agieval_gaokao_mathqa": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_gaokao_physics": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_jec_qa_ca": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_jec_qa_kd": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_logiqa_en": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_logiqa_zh": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_lsat_ar": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_lsat_lr": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_lsat_rc": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_math": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"agieval_sat_en": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_sat_en_without_passage": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
},
|
||
|
|
"agieval_sat_math": {
|
||
|
|
"acc": true,
|
||
|
|
"acc_norm": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"n-samples": {
|
||
|
|
"agieval_gaokao_biology": {
|
||
|
|
"original": 210,
|
||
|
|
"effective": 210
|
||
|
|
},
|
||
|
|
"agieval_gaokao_chemistry": {
|
||
|
|
"original": 207,
|
||
|
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"effective": 207
|
||
|
|
},
|
||
|
|
"agieval_gaokao_chinese": {
|
||
|
|
"original": 246,
|
||
|
|
"effective": 246
|
||
|
|
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|
||
|
|
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|
||
|
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"original": 199,
|
||
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"effective": 199
|
||
|
|
},
|
||
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|
||
|
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"original": 235,
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||
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"effective": 235
|
||
|
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},
|
||
|
|
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|
||
|
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"original": 118,
|
||
|
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"effective": 118
|
||
|
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},
|
||
|
|
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|
||
|
|
"original": 351,
|
||
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"effective": 351
|
||
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},
|
||
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||
|
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"original": 200,
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||
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||
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},
|
||
|
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|
||
|
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"original": 999,
|
||
|
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"effective": 999
|
||
|
|
},
|
||
|
|
"agieval_jec_qa_kd": {
|
||
|
|
"original": 1000,
|
||
|
|
"effective": 1000
|
||
|
|
},
|
||
|
|
"agieval_logiqa_zh": {
|
||
|
|
"original": 651,
|
||
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"effective": 651
|
||
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},
|
||
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|
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|
||
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"original": 254,
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||
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"effective": 254
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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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||
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|
||
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"original": 510,
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||
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"effective": 510
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||
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||
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|
||
|
|
"original": 269,
|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
"original": 1000,
|
||
|
|
"effective": 1000
|
||
|
|
},
|
||
|
|
"agieval_sat_en_without_passage": {
|
||
|
|
"original": 206,
|
||
|
|
"effective": 206
|
||
|
|
},
|
||
|
|
"agieval_sat_en": {
|
||
|
|
"original": 206,
|
||
|
|
"effective": 206
|
||
|
|
},
|
||
|
|
"agieval_sat_math": {
|
||
|
|
"original": 220,
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||
|
|
"effective": 220
|
||
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}
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||
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},
|
||
|
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"config": {
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||
|
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"model": "hf",
|
||
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"model_args": "pretrained=inceptionai/jais-family-6p7b-chat,trust_remote_code=True,cache_dir=/tmp,parallelize=False",
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"batch_sizes": [
|
||
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|
8
|
||
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],
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"device": null,
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"limit": null,
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"gen_kwargs": null,
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"fewshot_seed": 1234
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||
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},
|
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|
|
"git_hash": "150ae04f",
|
||
|
|
"date": 1737025229.8171139,
|
||
|
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"pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.27.1\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-1064-azure-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.128\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100 80GB PCIe\nGPU 1: NVIDIA A100 80GB PCIe\n\nNvidia driver version: 535.161.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 48\nOn-line CPU(s) list: 0-47\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V13 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 1\nStepping: 1\nBogoMIPS: 4890.86\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 24 MiB (48 instances)\nL3 cache: 192 MiB (6 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.0.0.dev0\n[pip3] torchaudio==2.1.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchmetrics==1.2.
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"model_name": "inceptionai/jais-family-6p7b-chat",
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"start_time": 4542.127713328,
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"end_time": 5688.623230107,
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}
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