1112 lines
42 KiB
JSON
1112 lines
42 KiB
JSON
{
|
|
"results": {
|
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"agieval": {
|
|
"acc,none": 0.36453797774552493,
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"acc_stderr,none": 0.004942349596688666,
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"alias": "agieval"
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|
},
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|
"agieval_aqua_rat": {
|
|
"alias": " - agieval_aqua_rat",
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|
"acc,none": 0.2283464566929134,
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|
"acc_stderr,none": 0.026390526537822135,
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"acc_norm,none": 0.20866141732283464,
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"acc_norm_stderr,none": 0.02554712225493389
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},
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|
"agieval_gaokao_biology": {
|
|
"alias": " - agieval_gaokao_biology",
|
|
"acc,none": 0.29523809523809524,
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"acc_stderr,none": 0.03155253554505397,
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"acc_norm,none": 0.3476190476190476,
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"acc_norm_stderr,none": 0.032940430891650836
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},
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"agieval_gaokao_chemistry": {
|
|
"alias": " - agieval_gaokao_chemistry",
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"acc,none": 0.2753623188405797,
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"acc_stderr,none": 0.031122831519058182,
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"acc_norm,none": 0.30434782608695654,
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"acc_norm_stderr,none": 0.03205882236563527
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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": 0.3048780487804878,
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"acc_stderr,none": 0.02941105055075626,
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"acc_norm,none": 0.2886178861788618,
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"acc_norm_stderr,none": 0.028948765576340286
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},
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|
"agieval_gaokao_english": {
|
|
"alias": " - agieval_gaokao_english",
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|
"acc,none": 0.6470588235294118,
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"acc_stderr,none": 0.027363593284684965,
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"acc_norm,none": 0.6797385620915033,
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"acc_norm_stderr,none": 0.026716118380156858
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},
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"agieval_gaokao_geography": {
|
|
"alias": " - agieval_gaokao_geography",
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|
"acc,none": 0.3969849246231156,
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"acc_stderr,none": 0.03477110537378156,
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"acc_norm,none": 0.3768844221105528,
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"acc_norm_stderr,none": 0.034439417931776
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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": 0.39574468085106385,
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"acc_stderr,none": 0.03196758697835363,
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"acc_norm,none": 0.37872340425531914,
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"acc_norm_stderr,none": 0.031709956060406545
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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": 0.025423728813559324,
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"acc_stderr,none": 0.014552399522167078
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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": 0.23931623931623933,
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"acc_stderr,none": 0.022806263357480903,
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"acc_norm,none": 0.25925925925925924,
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"acc_norm_stderr,none": 0.023424278964210166
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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": 0.275,
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"acc_stderr,none": 0.031652557907861915,
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"acc_norm,none": 0.265,
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|
"acc_norm_stderr,none": 0.03128528159088722
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},
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|
"agieval_jec_qa_ca": {
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|
"alias": " - agieval_jec_qa_ca",
|
|
"acc,none": 0.5065065065065065,
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"acc_stderr,none": 0.01582588330988679,
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"acc_norm,none": 0.4934934934934935,
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"acc_norm_stderr,none": 0.01582588330988679
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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": 0.533,
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|
"acc_stderr,none": 0.015784807891138772,
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"acc_norm,none": 0.533,
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"acc_norm_stderr,none": 0.015784807891138775
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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": 0.35176651305683565,
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"acc_stderr,none": 0.018729936274427355,
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"acc_norm,none": 0.3671274961597542,
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"acc_norm_stderr,none": 0.018906445694655587
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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": 0.3425499231950845,
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"acc_stderr,none": 0.018613868829208027,
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"acc_norm,none": 0.35944700460829493,
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"acc_norm_stderr,none": 0.018820809084481267
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},
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"agieval_lsat_ar": {
|
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"alias": " - agieval_lsat_ar",
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|
"acc,none": 0.22608695652173913,
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|
"acc_stderr,none": 0.02764178570724134,
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|
"acc_norm,none": 0.2391304347826087,
|
|
"acc_norm_stderr,none": 0.028187385293933942
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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": 0.4117647058823529,
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|
"acc_stderr,none": 0.02181429628344194,
|
|
"acc_norm,none": 0.4137254901960784,
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|
"acc_norm_stderr,none": 0.021829699356254582
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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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"acc_stderr,none": 0.030537084593525405,
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|
"acc_norm,none": 0.5018587360594795,
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|
"acc_norm_stderr,none": 0.030542150046756422
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},
|
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"agieval_math": {
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"alias": " - agieval_math",
|
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"acc,none": 0.038,
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"acc_stderr,none": 0.006049181150584934
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},
|
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"agieval_sat_en": {
|
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"alias": " - agieval_sat_en",
|
|
"acc,none": 0.7233009708737864,
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"acc_stderr,none": 0.03124542318927994,
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"acc_norm,none": 0.6990291262135923,
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"acc_norm_stderr,none": 0.03203560571847412
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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": 0.47572815533980584,
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"acc_stderr,none": 0.034880344423561846,
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"acc_norm,none": 0.4368932038834951,
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"acc_norm_stderr,none": 0.03464225055241279
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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": 0.3409090909090909,
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"acc_stderr,none": 0.03203095553573995,
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"acc_norm,none": 0.2818181818181818,
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"acc_norm_stderr,none": 0.030400424640665242
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}
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},
|
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"groups": {
|
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"agieval": {
|
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"acc,none": 0.36453797774552493,
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"acc_stderr,none": 0.004942349596688666,
|
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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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],
|
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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
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|
}
|
|
},
|
|
"agieval_gaokao_biology": {
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"task": "agieval_gaokao_biology",
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"dataset_path": "hails/agieval-gaokao-biology",
|
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"test_split": "test",
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|
"doc_to_text": "{{query}}",
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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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"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",
|
|
"aggregation": "mean",
|
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"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
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|
}
|
|
},
|
|
"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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],
|
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"output_type": "multiple_choice",
|
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"repeats": 1,
|
|
"should_decontaminate": false,
|
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"metadata": {
|
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"version": 1.0
|
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}
|
|
},
|
|
"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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{
|
|
"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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},
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"agieval_gaokao_english": {
|
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"task": "agieval_gaokao_english",
|
|
"dataset_path": "hails/agieval-gaokao-english",
|
|
"test_split": "test",
|
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"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
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"description": "",
|
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"target_delimiter": " ",
|
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"fewshot_delimiter": "\n\n",
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"num_fewshot": 0,
|
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"metric_list": [
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{
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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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"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
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"metadata": {
|
|
"version": 1.0
|
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}
|
|
},
|
|
"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",
|
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"repeats": 1,
|
|
"should_decontaminate": false,
|
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"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": " ",
|
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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",
|
|
"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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} |