1108 lines
41 KiB
JSON
1108 lines
41 KiB
JSON
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{
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||
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"results": {
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"agieval": {
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||
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"acc,none": 0.3289791969037252,
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"acc_stderr,none": 0.004884128051037663,
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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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||
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"alias": " - agieval_aqua_rat",
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||
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"acc,none": 0.25196850393700787,
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||
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"acc_stderr,none": 0.027294353392553594,
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"acc_norm,none": 0.29133858267716534,
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"acc_norm_stderr,none": 0.02856657247427776
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},
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"agieval_gaokao_biology": {
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||
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"alias": " - agieval_gaokao_biology",
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||
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"acc,none": 0.3238095238095238,
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"acc_stderr,none": 0.03236727895404352,
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"acc_norm,none": 0.32857142857142857,
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"acc_norm_stderr,none": 0.03248939796876841
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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": 0.2463768115942029,
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"acc_stderr,none": 0.030022263446335143,
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"acc_norm,none": 0.2753623188405797,
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"acc_norm_stderr,none": 0.031122831519058175
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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": 0.15853658536585366,
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"acc_stderr,none": 0.02333454446028325,
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"acc_norm,none": 0.17073170731707318,
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"acc_norm_stderr,none": 0.02403928684412588
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},
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"agieval_gaokao_english": {
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"alias": " - agieval_gaokao_english",
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||
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"acc,none": 0.6372549019607843,
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"acc_stderr,none": 0.02753007844711031,
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"acc_norm,none": 0.630718954248366,
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"acc_norm_stderr,none": 0.027634176689602667
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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": 0.35175879396984927,
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"acc_stderr,none": 0.03393580874720542,
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"acc_norm,none": 0.39195979899497485,
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"acc_norm_stderr,none": 0.034693995271705115
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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": 0.42127659574468085,
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"acc_stderr,none": 0.03227834510146267,
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"acc_norm,none": 0.39148936170212767,
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"acc_norm_stderr,none": 0.031907012423268113
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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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||
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"acc,none": 0.27635327635327633,
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"acc_stderr,none": 0.023903505003127226,
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"acc_norm,none": 0.25925925925925924,
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"acc_norm_stderr,none": 0.023424278964210177
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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": 0.305,
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"acc_stderr,none": 0.03263741725420572,
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"acc_norm,none": 0.305,
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"acc_norm_stderr,none": 0.03263741725420572
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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": 0.4624624624624625,
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"acc_stderr,none": 0.015782557191362036,
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"acc_norm,none": 0.46846846846846846,
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"acc_norm_stderr,none": 0.015795720055236592
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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": 0.495,
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"acc_stderr,none": 0.01581850894443665,
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"acc_norm,none": 0.492,
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"acc_norm_stderr,none": 0.015817274929209004
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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": 0.30414746543778803,
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"acc_stderr,none": 0.01804446579150677,
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"acc_norm,none": 0.32565284178187404,
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"acc_norm_stderr,none": 0.018380720184319525
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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": 0.2642089093701997,
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"acc_stderr,none": 0.017293954549744518,
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"acc_norm,none": 0.32565284178187404,
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"acc_norm_stderr,none": 0.018380720184319525
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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": 0.22608695652173913,
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"acc_stderr,none": 0.027641785707241334,
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"acc_norm,none": 0.2217391304347826,
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"acc_norm_stderr,none": 0.02745149660405891
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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": 0.32941176470588235,
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"acc_stderr,none": 0.020832367421292224,
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"acc_norm,none": 0.30196078431372547,
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"acc_norm_stderr,none": 0.02034961945311915
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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": 0.3940520446096654,
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||
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"acc_stderr,none": 0.029848812493479992,
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||
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"acc_norm,none": 0.31970260223048325,
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"acc_norm_stderr,none": 0.028487549542669435
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},
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"agieval_math": {
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"alias": " - agieval_math",
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"acc,none": 0.051,
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"acc_stderr,none": 0.006960420062571407
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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": 0.6067961165048543,
|
||
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"acc_stderr,none": 0.03411562759702561,
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||
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"acc_norm,none": 0.470873786407767,
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"acc_norm_stderr,none": 0.034862214060202984
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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": 0.3786407766990291,
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||
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"acc_stderr,none": 0.033877248925062636,
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||
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"acc_norm,none": 0.30097087378640774,
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"acc_norm_stderr,none": 0.03203560571847414
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},
|
||
|
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"agieval_sat_math": {
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||
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"alias": " - agieval_sat_math",
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"acc,none": 0.29545454545454547,
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||
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"acc_stderr,none": 0.03083030272837515,
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||
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"acc_norm,none": 0.2681818181818182,
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||
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"acc_norm_stderr,none": 0.029936030014892836
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||
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}
|
||
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},
|
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|
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"groups": {
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"agieval": {
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"acc,none": 0.3289791969037252,
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||
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"acc_stderr,none": 0.004884128051037663,
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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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||
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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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||
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"agieval_aqua_rat",
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||
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"agieval_gaokao_english",
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||
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"agieval_logiqa_en",
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||
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"agieval_lsat_ar",
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||
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"agieval_lsat_lr",
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||
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"agieval_lsat_rc",
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||
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"agieval_math",
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||
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"agieval_sat_en_without_passage",
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||
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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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||
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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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"doc_to_text": "{{query}}",
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||
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"doc_to_target": "{{gold}}",
|
||
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"doc_to_choice": "{{choices}}",
|
||
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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": "",
|
||
|
|
"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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"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
},
|
||
|
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{
|
||
|
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"metric": "acc_norm",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
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}
|
||
|
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],
|
||
|
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"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 1.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"agieval_gaokao_biology": {
|
||
|
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"task": "agieval_gaokao_biology",
|
||
|
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"dataset_path": "hails/agieval-gaokao-biology",
|
||
|
|
"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_chemistry": {
|
||
|
|
"task": "agieval_gaokao_chemistry",
|
||
|
|
"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,
|
||
|
|
"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_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,
|
||
|
|
"effective": 207
|
||
|
|
},
|
||
|
|
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|
||
|
|
"original": 246,
|
||
|
|
"effective": 246
|
||
|
|
},
|
||
|
|
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|
||
|
|
"original": 199,
|
||
|
|
"effective": 199
|
||
|
|
},
|
||
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|
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|
||
|
|
"original": 235,
|
||
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|
"effective": 235
|
||
|
|
},
|
||
|
|
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|
||
|
|
"original": 118,
|
||
|
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"effective": 118
|
||
|
|
},
|
||
|
|
"agieval_gaokao_mathqa": {
|
||
|
|
"original": 351,
|
||
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|
"effective": 351
|
||
|
|
},
|
||
|
|
"agieval_gaokao_physics": {
|
||
|
|
"original": 200,
|
||
|
|
"effective": 200
|
||
|
|
},
|
||
|
|
"agieval_jec_qa_ca": {
|
||
|
|
"original": 999,
|
||
|
|
"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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|
||
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},
|
||
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|
||
|
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"original": 306,
|
||
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"effective": 306
|
||
|
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},
|
||
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"agieval_logiqa_en": {
|
||
|
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"original": 651,
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||
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"effective": 651
|
||
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},
|
||
|
|
"agieval_lsat_ar": {
|
||
|
|
"original": 230,
|
||
|
|
"effective": 230
|
||
|
|
},
|
||
|
|
"agieval_lsat_lr": {
|
||
|
|
"original": 510,
|
||
|
|
"effective": 510
|
||
|
|
},
|
||
|
|
"agieval_lsat_rc": {
|
||
|
|
"original": 269,
|
||
|
|
"effective": 269
|
||
|
|
},
|
||
|
|
"agieval_math": {
|
||
|
|
"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,
|
||
|
|
"effective": 220
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"config": {
|
||
|
|
"model": "vllm",
|
||
|
|
"model_args": "pretrained=inceptionai/jais-adapted-7b-chat,tensor_parallel_size=1,data_parallel_size=2,gpu_memory_utilization=0.8,download_dir=/tmp",
|
||
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|
"batch_size": 1,
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"batch_sizes": [],
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"device": null,
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"limit": null,
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"bootstrap_iters": 100000,
|
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"gen_kwargs": null,
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"random_seed": 0,
|
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||
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"torch_seed": 1234,
|
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|
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"fewshot_seed": 1234
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||
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},
|
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|
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"git_hash": "8e1bd48d",
|
||
|
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"date": 1735738495.5311651,
|
||
|
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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: 4890.88\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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"transformers_version": "4.47.1",
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"upper_git_hash": "f64fe2f2a86055aaecced603b56097fd79201711",
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"tokenizer_pad_token": [
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"0"
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],
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"tokenizer_eos_token": [
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"</s>",
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"2"
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],
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"tokenizer_bos_token": [
|
||
|
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"<s>",
|
||
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"1"
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||
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],
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"eot_token_id": 2,
|
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"task_hashes": {},
|
||
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"model_source": "vllm",
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||
|
|
"model_name": "inceptionai/jais-adapted-7b-chat",
|
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|
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"model_name_sanitized": "inceptionai__jais-adapted-7b-chat",
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|
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"system_instruction": null,
|
||
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|
"system_instruction_sha": null,
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"fewshot_as_multiturn": false,
|
||
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|
"chat_template": null,
|
||
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"chat_template_sha": null,
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||
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"start_time": 4080.287854209,
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||
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|
"end_time": 4827.744314483,
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"total_evaluation_time_seconds": "747.4564602740002"
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|
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}
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