1108 lines
41 KiB
JSON
1108 lines
41 KiB
JSON
{
|
|
"results": {
|
|
"agieval": {
|
|
"acc,none": 0.36490082244799227,
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"acc_stderr,none": 0.004969377963121314,
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|
"alias": "agieval"
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|
},
|
|
"agieval_aqua_rat": {
|
|
"alias": " - agieval_aqua_rat",
|
|
"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": {
|
|
"alias": " - agieval_gaokao_biology",
|
|
"acc,none": 0.3,
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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": {
|
|
"alias": " - agieval_gaokao_chemistry",
|
|
"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": {
|
|
"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": {
|
|
"alias": " - agieval_gaokao_english",
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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": {
|
|
"alias": " - agieval_gaokao_geography",
|
|
"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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"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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|
"acc_stderr,none": 0.01577824302490459,
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|
"acc_norm,none": 0.511,
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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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|
},
|
|
"agieval_lsat_ar": {
|
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"alias": " - agieval_lsat_ar",
|
|
"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,
|
|
"acc_stderr,none": 0.022093840314950028,
|
|
"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,
|
|
"acc_stderr,none": 0.03053708459352539,
|
|
"acc_norm,none": 0.40148698884758366,
|
|
"acc_norm_stderr,none": 0.029943677641911325
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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",
|
|
"acc,none": 0.6601941747572816,
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|
"acc_stderr,none": 0.03308067200587321,
|
|
"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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|
"acc_stderr,none": 0.0339602794458664,
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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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"agieval_sat_math": {
|
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"alias": " - agieval_sat_math",
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"acc,none": 0.2636363636363636,
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"acc_stderr,none": 0.02977328576472751,
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|
"acc_norm,none": 0.24545454545454545,
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"acc_norm_stderr,none": 0.029080789024287262
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}
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|
},
|
|
"groups": {
|
|
"agieval": {
|
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"acc,none": 0.36490082244799227,
|
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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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"agieval_gaokao_mathqa",
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"agieval_gaokao_physics",
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"agieval_jec_qa_ca",
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"agieval_jec_qa_kd",
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"agieval_logiqa_zh",
|
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"agieval_aqua_rat",
|
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"agieval_gaokao_english",
|
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"agieval_logiqa_en",
|
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"agieval_lsat_ar",
|
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"agieval_lsat_lr",
|
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"agieval_lsat_rc",
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"agieval_math",
|
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"agieval_sat_en_without_passage",
|
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"agieval_sat_en",
|
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"agieval_sat_math"
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]
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},
|
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"configs": {
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"agieval_aqua_rat": {
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"task": "agieval_aqua_rat",
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"dataset_path": "hails/agieval-aqua-rat",
|
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"test_split": "test",
|
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"doc_to_text": "{{query}}",
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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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"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",
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"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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],
|
|
"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",
|
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"description": "",
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"target_delimiter": " ",
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|
"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
|
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"metric_list": [
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{
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"metric": "acc",
|
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"aggregation": "mean",
|
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"higher_is_better": true
|
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},
|
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{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
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"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",
|
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"description": "",
|
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
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"metric_list": [
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{
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"metric": "acc",
|
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"aggregation": "mean",
|
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"higher_is_better": true
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},
|
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{
|
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"metric": "acc_norm",
|
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"aggregation": "mean",
|
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"higher_is_better": true
|
|
}
|
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],
|
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"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
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"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",
|
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"description": "",
|
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
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"metric_list": [
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{
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"metric": "acc",
|
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"aggregation": "mean",
|
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"higher_is_better": true
|
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},
|
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{
|
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"metric": "acc_norm",
|
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"aggregation": "mean",
|
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"higher_is_better": true
|
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}
|
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],
|
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"output_type": "multiple_choice",
|
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"repeats": 1,
|
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"should_decontaminate": false,
|
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"metadata": {
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"version": 1.0
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}
|
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},
|
|
"agieval_gaokao_english": {
|
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"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",
|
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"description": "",
|
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
|
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"metric_list": [
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{
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"metric": "acc",
|
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"aggregation": "mean",
|
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"higher_is_better": true
|
|
},
|
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{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
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"higher_is_better": true
|
|
}
|
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],
|
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"output_type": "multiple_choice",
|
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"repeats": 1,
|
|
"should_decontaminate": false,
|
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"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}}",
|
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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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"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",
|
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"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
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"higher_is_better": true
|
|
}
|
|
],
|
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"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",
|
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"description": "",
|
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"target_delimiter": " ",
|
|
"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",
|
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"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
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"metadata": {
|
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"version": 1.0
|
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}
|
|
},
|
|
"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",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
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"metric_list": [
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{
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"metric": "acc",
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Q:"
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]
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"metadata": {
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"version": 1.0
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}
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},
|
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"agieval_gaokao_mathqa": {
|
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"task": "agieval_gaokao_mathqa",
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"dataset_path": "hails/agieval-gaokao-mathqa",
|
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"test_split": "test",
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"doc_to_text": "{{query}}",
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"doc_to_target": "{{gold}}",
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"doc_to_choice": "{{choices}}",
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"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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"metric": "acc",
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"higher_is_better": true
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},
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{
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"metric": "acc_norm",
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"higher_is_better": true
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}
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],
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"output_type": "multiple_choice",
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"metadata": {
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"version": 1.0
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}
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},
|
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"agieval_gaokao_physics": {
|
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"task": "agieval_gaokao_physics",
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"dataset_path": "hails/agieval-gaokao-physics",
|
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"test_split": "test",
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"doc_to_text": "{{query}}",
|
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"doc_to_target": "{{gold}}",
|
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"doc_to_choice": "{{choices}}",
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"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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"metric": "acc",
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{
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"metric": "acc_norm",
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"higher_is_better": true
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],
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"output_type": "multiple_choice",
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"should_decontaminate": false,
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"metadata": {
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"version": 1.0
|
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}
|
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},
|
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"agieval_jec_qa_ca": {
|
|
"task": "agieval_jec_qa_ca",
|
|
"dataset_path": "hails/agieval-jec-qa-ca",
|
|
"test_split": "test",
|
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"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"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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"num_fewshot": 0,
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"metric_list": [
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{
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"metric": "acc",
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"higher_is_better": true
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},
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{
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"metric": "acc_norm",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "multiple_choice",
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
|
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"version": 1.0
|
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}
|
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},
|
|
"agieval_jec_qa_kd": {
|
|
"task": "agieval_jec_qa_kd",
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|
"dataset_path": "hails/agieval-jec-qa-kd",
|
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"test_split": "test",
|
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"doc_to_text": "{{query}}",
|
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"doc_to_target": "{{gold}}",
|
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"doc_to_choice": "{{choices}}",
|
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"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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"metric": "acc",
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{
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"metric": "acc_norm",
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"higher_is_better": true
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}
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],
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"output_type": "multiple_choice",
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 1.0
|
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}
|
|
},
|
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"agieval_logiqa_en": {
|
|
"task": "agieval_logiqa_en",
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"dataset_path": "hails/agieval-logiqa-en",
|
|
"test_split": "test",
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"doc_to_text": "{{query}}",
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"doc_to_target": "{{gold}}",
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"doc_to_choice": "{{choices}}",
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"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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{
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"metric": "acc",
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},
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{
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"metric": "acc_norm",
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"higher_is_better": true
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}
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],
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"output_type": "multiple_choice",
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"repeats": 1,
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"should_decontaminate": false,
|
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"metadata": {
|
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"version": 1.0
|
|
}
|
|
},
|
|
"agieval_logiqa_zh": {
|
|
"task": "agieval_logiqa_zh",
|
|
"dataset_path": "hails/agieval-logiqa-zh",
|
|
"test_split": "test",
|
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"doc_to_text": "{{query}}",
|
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"doc_to_target": "{{gold}}",
|
|
"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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{
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},
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{
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"higher_is_better": true
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}
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],
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"output_type": "multiple_choice",
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"repeats": 1,
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"should_decontaminate": false,
|
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"metadata": {
|
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"version": 1.0
|
|
}
|
|
},
|
|
"agieval_lsat_ar": {
|
|
"task": "agieval_lsat_ar",
|
|
"dataset_path": "hails/agieval-lsat-ar",
|
|
"test_split": "test",
|
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"doc_to_text": "{{query}}",
|
|
"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",
|
|
"description": "",
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{
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"metric": "acc",
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"higher_is_better": true
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},
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{
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"metric": "acc_norm",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
|
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"output_type": "multiple_choice",
|
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"repeats": 1,
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"should_decontaminate": false,
|
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"metadata": {
|
|
"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}}",
|
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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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"metric": "acc",
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"higher_is_better": true
|
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},
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{
|
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"metric": "acc_norm",
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"aggregation": "mean",
|
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"higher_is_better": true
|
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}
|
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],
|
|
"output_type": "multiple_choice",
|
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"repeats": 1,
|
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"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}}",
|
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"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
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"description": "",
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{
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"metric": "acc",
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"higher_is_better": true
|
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},
|
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{
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"metric": "acc_norm",
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"aggregation": "mean",
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"higher_is_better": true
|
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}
|
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],
|
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"output_type": "multiple_choice",
|
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"repeats": 1,
|
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"should_decontaminate": false,
|
|
"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": "",
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"target_delimiter": " ",
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"metric_list": [
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{
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"metric": "acc",
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"higher_is_better": true
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}
|
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],
|
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"output_type": "generate_until",
|
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"generation_kwargs": {
|
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"max_gen_toks": 32,
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"do_sample": false,
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"temperature": 0.0,
|
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"until": [
|
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"Q:"
|
|
]
|
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},
|
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"repeats": 1,
|
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"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": " ",
|
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"fewshot_delimiter": "\n\n",
|
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"num_fewshot": 0,
|
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"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
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"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,
|
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"metric_list": [
|
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{
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"metric": "acc",
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"aggregation": "mean",
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"higher_is_better": true
|
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},
|
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{
|
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"metric": "acc_norm",
|
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"aggregation": "mean",
|
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"higher_is_better": true
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}
|
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],
|
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"output_type": "multiple_choice",
|
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"repeats": 1,
|
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"should_decontaminate": false,
|
|
"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}}",
|
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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",
|
|
"description": "",
|
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
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"metric_list": [
|
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{
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"metric": "acc",
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"aggregation": "mean",
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} |