1114 lines
41 KiB
JSON
1114 lines
41 KiB
JSON
{
|
|
"results": {
|
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"agieval": {
|
|
"acc,none": 0.39646831156265117,
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"acc_stderr,none": 0.005025874456441722,
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"alias": "agieval"
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|
},
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"agieval_aqua_rat": {
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|
"alias": " - agieval_aqua_rat",
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|
"acc,none": 0.28346456692913385,
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"acc_stderr,none": 0.02833400492130763,
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"acc_norm,none": 0.25984251968503935,
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"acc_norm_stderr,none": 0.027571279139611004
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},
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"agieval_gaokao_biology": {
|
|
"alias": " - agieval_gaokao_biology",
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"acc,none": 0.43333333333333335,
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"acc_stderr,none": 0.0342769159111587,
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"acc_norm,none": 0.45714285714285713,
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"acc_norm_stderr,none": 0.03445843938031584
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},
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"agieval_gaokao_chemistry": {
|
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"alias": " - agieval_gaokao_chemistry",
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"acc,none": 0.30434782608695654,
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"acc_stderr,none": 0.032058822365635266,
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"acc_norm,none": 0.28019323671497587,
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"acc_norm_stderr,none": 0.031289827964521094
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},
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"agieval_gaokao_chinese": {
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"alias": " - agieval_gaokao_chinese",
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"acc,none": 0.3089430894308943,
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"acc_stderr,none": 0.02951977938940492,
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"acc_norm,none": 0.2967479674796748,
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"acc_norm_stderr,none": 0.029185445861037915
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},
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"agieval_gaokao_english": {
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|
"alias": " - agieval_gaokao_english",
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"acc,none": 0.6372549019607843,
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"acc_stderr,none": 0.027530078447110307,
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"acc_norm,none": 0.6568627450980392,
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"acc_norm_stderr,none": 0.027184498909941613
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},
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"agieval_gaokao_geography": {
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"alias": " - agieval_gaokao_geography",
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"acc,none": 0.6180904522613065,
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"acc_stderr,none": 0.03452817946540989,
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"acc_norm,none": 0.6231155778894473,
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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": 0.6042553191489362,
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"acc_stderr,none": 0.03196758697835361,
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"acc_norm,none": 0.5404255319148936,
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"acc_norm_stderr,none": 0.03257901482099834
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},
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"agieval_gaokao_mathcloze": {
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"alias": " - agieval_gaokao_mathcloze",
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"acc,none": 0.03389830508474576,
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"acc_stderr,none": 0.0167304446370449
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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": 0.30484330484330485,
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"acc_stderr,none": 0.024606263101409013,
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"acc_norm,none": 0.31054131054131057,
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"acc_norm_stderr,none": 0.02473317061233447
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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": 0.47,
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"acc_stderr,none": 0.03538020341900045,
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"acc_norm,none": 0.445,
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"acc_norm_stderr,none": 0.03522897106090459
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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": 0.5205205205205206,
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"acc_stderr,none": 0.015813888401348383,
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"acc_norm,none": 0.4914914914914915,
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"acc_norm_stderr,none": 0.015824931665172324
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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": 0.607,
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"acc_stderr,none": 0.015452824654081496,
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"acc_norm,none": 0.535,
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"acc_norm_stderr,none": 0.01578049505003016
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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": 0.34408602150537637,
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"acc_stderr,none": 0.01863375065717621,
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"acc_norm,none": 0.34101382488479265,
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"acc_norm_stderr,none": 0.01859377050860097
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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": 0.3533026113671275,
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"acc_stderr,none": 0.018748533323899717,
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"acc_norm,none": 0.38402457757296465,
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"acc_norm_stderr,none": 0.019076755948732337
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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": 0.23478260869565218,
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"acc_stderr,none": 0.028009647070930118,
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"acc_norm,none": 0.23043478260869565,
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"acc_norm_stderr,none": 0.027827807522276156
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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": 0.3568627450980392,
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|
"acc_stderr,none": 0.02123457379560983,
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"acc_norm,none": 0.3411764705882353,
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"acc_norm_stderr,none": 0.021014312949349186
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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": 0.49814126394052044,
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"acc_stderr,none": 0.030542150046756422,
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|
"acc_norm,none": 0.43866171003717475,
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"acc_norm_stderr,none": 0.03031166554071835
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},
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|
"agieval_math": {
|
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"alias": " - agieval_math",
|
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"acc,none": 0.077,
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"acc_stderr,none": 0.00843458014024063
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},
|
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"agieval_sat_en": {
|
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"alias": " - agieval_sat_en",
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"acc,none": 0.6650485436893204,
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"acc_stderr,none": 0.032964058640862416,
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"acc_norm,none": 0.616504854368932,
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"acc_norm_stderr,none": 0.0339602794458664
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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": 0.39805825242718446,
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"acc_stderr,none": 0.03418799390613398,
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"acc_norm,none": 0.3592233009708738,
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"acc_norm_stderr,none": 0.03350878450608781
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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": 0.2909090909090909,
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"acc_stderr,none": 0.03069075327671109,
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"acc_norm,none": 0.2772727272727273,
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"acc_norm_stderr,none": 0.03024953767588669
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}
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},
|
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"groups": {
|
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"agieval": {
|
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"acc,none": 0.39646831156265117,
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"acc_stderr,none": 0.005025874456441722,
|
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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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{
|
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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_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}}",
|
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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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{
|
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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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],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
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|
}
|
|
},
|
|
"agieval_gaokao_chemistry": {
|
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"task": "agieval_gaokao_chemistry",
|
|
"dataset_path": "hails/agieval-gaokao-chemistry",
|
|
"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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},
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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}}",
|
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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",
|
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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",
|
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"dataset_path": "hails/agieval-gaokao-english",
|
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"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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"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",
|
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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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}
|
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},
|
|
"agieval_gaokao_geography": {
|
|
"task": "agieval_gaokao_geography",
|
|
"dataset_path": "hails/agieval-gaokao-geography",
|
|
"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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},
|
|
{
|
|
"metric": "acc_norm",
|
|
"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_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",
|
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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": {
|
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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}}",
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|
"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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