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": 0.36490082244799227,
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"acc_stderr,none": 0.004969377963121314,
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||
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"alias": "agieval"
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||
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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": 0.25984251968503935,
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"acc_stderr,none": 0.027571279139610997,
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"acc_norm,none": 0.2795275590551181,
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"acc_norm_stderr,none": 0.02821374533845074
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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": 0.3,
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||
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"acc_stderr,none": 0.03169833889962086,
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"acc_norm,none": 0.3333333333333333,
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"acc_norm_stderr,none": 0.03260773253630123
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},
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"agieval_gaokao_chemistry": {
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||
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"alias": " - agieval_gaokao_chemistry",
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"acc,none": 0.2463768115942029,
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"acc_stderr,none": 0.030022263446335153,
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"acc_norm,none": 0.28019323671497587,
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"acc_norm_stderr,none": 0.031289827964521094
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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": 0.2601626016260163,
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"acc_stderr,none": 0.028028995361669362,
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"acc_norm,none": 0.2601626016260163,
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"acc_norm_stderr,none": 0.028028995361669366
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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": 0.7091503267973857,
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"acc_stderr,none": 0.02600480036395213,
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"acc_norm,none": 0.7124183006535948,
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"acc_norm_stderr,none": 0.02591780611714716
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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": 0.3768844221105528,
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"acc_stderr,none": 0.03443941793177599,
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"acc_norm,none": 0.36180904522613067,
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"acc_norm_stderr,none": 0.034149349640988196
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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": 0.4425531914893617,
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"acc_stderr,none": 0.03246956919789958,
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"acc_norm,none": 0.3702127659574468,
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"acc_norm_stderr,none": 0.03156564682236784
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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": 0.06779661016949153,
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"acc_stderr,none": 0.023241620090605725
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},
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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": 0.2564102564102564,
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"acc_stderr,none": 0.02333997409827682,
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"acc_norm,none": 0.28205128205128205,
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"acc_norm_stderr,none": 0.024053414152940693
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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": 0.385,
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"acc_stderr,none": 0.03449382728261699,
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"acc_norm,none": 0.36,
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"acc_norm_stderr,none": 0.03402629784040014
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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": 0.48848848848848847,
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"acc_stderr,none": 0.015823028204038865,
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"acc_norm,none": 0.4444444444444444,
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"acc_norm_stderr,none": 0.01572922111997255
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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": 0.536,
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||
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"acc_stderr,none": 0.01577824302490459,
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||
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"acc_norm,none": 0.511,
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||
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"acc_norm_stderr,none": 0.01581547119529269
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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": 0.3640552995391705,
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"acc_stderr,none": 0.018872814735104125,
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"acc_norm,none": 0.36251920122887865,
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"acc_norm_stderr,none": 0.018855687979585062
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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": 0.30414746543778803,
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"acc_norm_stderr,none": 0.01804446579150677
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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": 0.25217391304347825,
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"acc_stderr,none": 0.02869674529449335,
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"acc_norm,none": 0.22608695652173913,
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"acc_norm_stderr,none": 0.027641785707241327
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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": 0.46078431372549017,
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"acc_stderr,none": 0.022093840314950028,
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"acc_norm,none": 0.38823529411764707,
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"acc_norm_stderr,none": 0.021601346576260526
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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": 0.5092936802973977,
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||
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"acc_stderr,none": 0.03053708459352539,
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||
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"acc_norm,none": 0.40148698884758366,
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"acc_norm_stderr,none": 0.029943677641911325
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},
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"agieval_math": {
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"alias": " - agieval_math",
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"acc,none": 0.075,
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"acc_stderr,none": 0.008333333333333337
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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": 0.6601941747572816,
|
||
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"acc_stderr,none": 0.03308067200587321,
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||
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"acc_norm,none": 0.6019417475728155,
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"acc_norm_stderr,none": 0.03418799390613399
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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": 0.38349514563106796,
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||
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"acc_stderr,none": 0.0339602794458664,
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||
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"acc_norm,none": 0.32038834951456313,
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"acc_norm_stderr,none": 0.03259056088171643
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},
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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": 0.2636363636363636,
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||
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"acc_stderr,none": 0.02977328576472751,
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||
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"acc_norm,none": 0.24545454545454545,
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||
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"acc_norm_stderr,none": 0.029080789024287262
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}
|
||
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},
|
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"groups": {
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"agieval": {
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"acc,none": 0.36490082244799227,
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||
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"acc_stderr,none": 0.004969377963121314,
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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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||
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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": "",
|
||
|
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"target_delimiter": " ",
|
||
|
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"fewshot_delimiter": "\n\n",
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||
|
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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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"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
|
||
|
|
},
|
||
|
|
"agieval_gaokao_chinese": {
|
||
|
|
"original": 246,
|
||
|
|
"effective": 246
|
||
|
|
},
|
||
|
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|
||
|
|
"original": 199,
|
||
|
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"effective": 199
|
||
|
|
},
|
||
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||
|
|
"original": 235,
|
||
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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
|
||
|
|
},
|
||
|
|
"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": {
|
||
|
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"original": 651,
|
||
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"effective": 651
|
||
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},
|
||
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|
||
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"original": 254,
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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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|
||
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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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||
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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
|
||
|
|
},
|
||
|
|
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|
||
|
|
"original": 220,
|
||
|
|
"effective": 220
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"config": {
|
||
|
|
"model": "vllm",
|
||
|
|
"model_args": "pretrained=inceptionai/jais-adapted-13b-chat,tensor_parallel_size=1,data_parallel_size=2,gpu_memory_utilization=0.8,download_dir=/tmp",
|
||
|
|
"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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"numpy_seed": 1234,
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"torch_seed": 1234,
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"fewshot_seed": 1234
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},
|
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"git_hash": "8e1bd48d",
|
||
|
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"date": 1735742027.800715,
|
||
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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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"</s>",
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"2"
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],
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|
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"<s>",
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],
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|
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"task_hashes": {},
|
||
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"model_source": "vllm",
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||
|
|
"model_name": "inceptionai/jais-adapted-13b-chat",
|
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|
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"model_name_sanitized": "inceptionai__jais-adapted-13b-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": 18842.426191837,
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"end_time": 19966.545417353,
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"total_evaluation_time_seconds": "1124.1192255160022"
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||
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}
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