1114 lines
42 KiB
JSON
1114 lines
42 KiB
JSON
{
|
|
"results": {
|
|
"agieval": {
|
|
"acc,none": 0.3664731494920174,
|
|
"acc_stderr,none": 0.005017892709566161,
|
|
"alias": "agieval"
|
|
},
|
|
"agieval_aqua_rat": {
|
|
"alias": " - agieval_aqua_rat",
|
|
"acc,none": 0.2440944881889764,
|
|
"acc_stderr,none": 0.027005516126961032,
|
|
"acc_norm,none": 0.2677165354330709,
|
|
"acc_norm_stderr,none": 0.027836648866445348
|
|
},
|
|
"agieval_gaokao_biology": {
|
|
"alias": " - agieval_gaokao_biology",
|
|
"acc,none": 0.2571428571428571,
|
|
"acc_stderr,none": 0.030231990420749873,
|
|
"acc_norm,none": 0.3238095238095238,
|
|
"acc_norm_stderr,none": 0.03236727895404352
|
|
},
|
|
"agieval_gaokao_chemistry": {
|
|
"alias": " - agieval_gaokao_chemistry",
|
|
"acc,none": 0.28019323671497587,
|
|
"acc_stderr,none": 0.031289827964521094,
|
|
"acc_norm,none": 0.25120772946859904,
|
|
"acc_norm_stderr,none": 0.030217850292985314
|
|
},
|
|
"agieval_gaokao_chinese": {
|
|
"alias": " - agieval_gaokao_chinese",
|
|
"acc,none": 0.21951219512195122,
|
|
"acc_stderr,none": 0.026444133743568285,
|
|
"acc_norm,none": 0.23170731707317074,
|
|
"acc_norm_stderr,none": 0.02695567308340271
|
|
},
|
|
"agieval_gaokao_english": {
|
|
"alias": " - agieval_gaokao_english",
|
|
"acc,none": 0.6503267973856209,
|
|
"acc_stderr,none": 0.027305308076274695,
|
|
"acc_norm,none": 0.6535947712418301,
|
|
"acc_norm_stderr,none": 0.027245613047215365
|
|
},
|
|
"agieval_gaokao_geography": {
|
|
"alias": " - agieval_gaokao_geography",
|
|
"acc,none": 0.36683417085427134,
|
|
"acc_stderr,none": 0.034250035902652465,
|
|
"acc_norm,none": 0.3417085427135678,
|
|
"acc_norm_stderr,none": 0.03370578394675525
|
|
},
|
|
"agieval_gaokao_history": {
|
|
"alias": " - agieval_gaokao_history",
|
|
"acc,none": 0.37872340425531914,
|
|
"acc_stderr,none": 0.031709956060406545,
|
|
"acc_norm,none": 0.34893617021276596,
|
|
"acc_norm_stderr,none": 0.031158522131357773
|
|
},
|
|
"agieval_gaokao_mathcloze": {
|
|
"alias": " - agieval_gaokao_mathcloze",
|
|
"acc,none": 0.01694915254237288,
|
|
"acc_stderr,none": 0.011933533435676647
|
|
},
|
|
"agieval_gaokao_mathqa": {
|
|
"alias": " - agieval_gaokao_mathqa",
|
|
"acc,none": 0.31054131054131057,
|
|
"acc_stderr,none": 0.024733170612334463,
|
|
"acc_norm,none": 0.3190883190883191,
|
|
"acc_norm_stderr,none": 0.024915340295242675
|
|
},
|
|
"agieval_gaokao_physics": {
|
|
"alias": " - agieval_gaokao_physics",
|
|
"acc,none": 0.31,
|
|
"acc_stderr,none": 0.0327852767543496,
|
|
"acc_norm,none": 0.33,
|
|
"acc_norm_stderr,none": 0.03333249580187338
|
|
},
|
|
"agieval_jec_qa_ca": {
|
|
"alias": " - agieval_jec_qa_ca",
|
|
"acc,none": 0.4724724724724725,
|
|
"acc_stderr,none": 0.015803218617280186,
|
|
"acc_norm,none": 0.44044044044044045,
|
|
"acc_norm_stderr,none": 0.015714533145117997
|
|
},
|
|
"agieval_jec_qa_kd": {
|
|
"alias": " - agieval_jec_qa_kd",
|
|
"acc,none": 0.516,
|
|
"acc_stderr,none": 0.015811198373114878,
|
|
"acc_norm,none": 0.493,
|
|
"acc_norm_stderr,none": 0.015817749561843567
|
|
},
|
|
"agieval_logiqa_en": {
|
|
"alias": " - agieval_logiqa_en",
|
|
"acc,none": 0.36098310291858676,
|
|
"acc_stderr,none": 0.018838352954538683,
|
|
"acc_norm,none": 0.3563748079877112,
|
|
"acc_norm_stderr,none": 0.018785092461820006
|
|
},
|
|
"agieval_logiqa_zh": {
|
|
"alias": " - agieval_logiqa_zh",
|
|
"acc,none": 0.28110599078341014,
|
|
"acc_stderr,none": 0.017632374626459998,
|
|
"acc_norm,none": 0.3271889400921659,
|
|
"acc_norm_stderr,none": 0.018403023897573558
|
|
},
|
|
"agieval_lsat_ar": {
|
|
"alias": " - agieval_lsat_ar",
|
|
"acc,none": 0.24782608695652175,
|
|
"acc_stderr,none": 0.028530862595410066,
|
|
"acc_norm,none": 0.24782608695652175,
|
|
"acc_norm_stderr,none": 0.028530862595410062
|
|
},
|
|
"agieval_lsat_lr": {
|
|
"alias": " - agieval_lsat_lr",
|
|
"acc,none": 0.492156862745098,
|
|
"acc_stderr,none": 0.0221593835954891,
|
|
"acc_norm,none": 0.4372549019607843,
|
|
"acc_norm_stderr,none": 0.021986915767668633
|
|
},
|
|
"agieval_lsat_rc": {
|
|
"alias": " - agieval_lsat_rc",
|
|
"acc,none": 0.5092936802973977,
|
|
"acc_stderr,none": 0.030537084593525398,
|
|
"acc_norm,none": 0.45724907063197023,
|
|
"acc_norm_stderr,none": 0.030430515298569164
|
|
},
|
|
"agieval_math": {
|
|
"alias": " - agieval_math",
|
|
"acc,none": 0.104,
|
|
"acc_stderr,none": 0.009658016218524298
|
|
},
|
|
"agieval_sat_en": {
|
|
"alias": " - agieval_sat_en",
|
|
"acc,none": 0.7038834951456311,
|
|
"acc_stderr,none": 0.03188634698327117,
|
|
"acc_norm,none": 0.587378640776699,
|
|
"acc_norm_stderr,none": 0.03438412659410015
|
|
},
|
|
"agieval_sat_en_without_passage": {
|
|
"alias": " - agieval_sat_en_without_passage",
|
|
"acc,none": 0.441747572815534,
|
|
"acc_stderr,none": 0.03468370354145869,
|
|
"acc_norm,none": 0.4029126213592233,
|
|
"acc_norm_stderr,none": 0.03425685196966478
|
|
},
|
|
"agieval_sat_math": {
|
|
"alias": " - agieval_sat_math",
|
|
"acc,none": 0.35,
|
|
"acc_stderr,none": 0.032230618755899304,
|
|
"acc_norm,none": 0.3181818181818182,
|
|
"acc_norm_stderr,none": 0.031473852941718845
|
|
}
|
|
},
|
|
"groups": {
|
|
"agieval": {
|
|
"acc,none": 0.3664731494920174,
|
|
"acc_stderr,none": 0.005017892709566161,
|
|
"alias": "agieval"
|
|
}
|
|
},
|
|
"group_subtasks": {
|
|
"agieval": [
|
|
"agieval_gaokao_biology",
|
|
"agieval_gaokao_chemistry",
|
|
"agieval_gaokao_chinese",
|
|
"agieval_gaokao_geography",
|
|
"agieval_gaokao_history",
|
|
"agieval_gaokao_mathcloze",
|
|
"agieval_gaokao_mathqa",
|
|
"agieval_gaokao_physics",
|
|
"agieval_jec_qa_ca",
|
|
"agieval_jec_qa_kd",
|
|
"agieval_logiqa_zh",
|
|
"agieval_aqua_rat",
|
|
"agieval_gaokao_english",
|
|
"agieval_logiqa_en",
|
|
"agieval_lsat_ar",
|
|
"agieval_lsat_lr",
|
|
"agieval_lsat_rc",
|
|
"agieval_math",
|
|
"agieval_sat_en_without_passage",
|
|
"agieval_sat_en",
|
|
"agieval_sat_math"
|
|
]
|
|
},
|
|
"configs": {
|
|
"agieval_aqua_rat": {
|
|
"task": "agieval_aqua_rat",
|
|
"dataset_path": "hails/agieval-aqua-rat",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_gaokao_biology": {
|
|
"task": "agieval_gaokao_biology",
|
|
"dataset_path": "hails/agieval-gaokao-biology",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_gaokao_chemistry": {
|
|
"task": "agieval_gaokao_chemistry",
|
|
"dataset_path": "hails/agieval-gaokao-chemistry",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_gaokao_chinese": {
|
|
"task": "agieval_gaokao_chinese",
|
|
"dataset_path": "hails/agieval-gaokao-chinese",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_gaokao_english": {
|
|
"task": "agieval_gaokao_english",
|
|
"dataset_path": "hails/agieval-gaokao-english",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_gaokao_geography": {
|
|
"task": "agieval_gaokao_geography",
|
|
"dataset_path": "hails/agieval-gaokao-geography",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_gaokao_history": {
|
|
"task": "agieval_gaokao_history",
|
|
"dataset_path": "hails/agieval-gaokao-history",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_gaokao_mathcloze": {
|
|
"task": "agieval_gaokao_mathcloze",
|
|
"dataset_path": "hails/agieval-gaokao-mathcloze",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{answer}}",
|
|
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidate = results[0]\n\n gold = doc[\"answer\"]\n\n if not gold:\n print(doc, candidate, gold)\n if is_equiv(candidate, gold):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"acc\": retval,\n }\n return results\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "generate_until",
|
|
"generation_kwargs": {
|
|
"max_gen_toks": 32,
|
|
"do_sample": false,
|
|
"temperature": 0.0,
|
|
"until": [
|
|
"Q:"
|
|
]
|
|
},
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_gaokao_mathqa": {
|
|
"task": "agieval_gaokao_mathqa",
|
|
"dataset_path": "hails/agieval-gaokao-mathqa",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_gaokao_physics": {
|
|
"task": "agieval_gaokao_physics",
|
|
"dataset_path": "hails/agieval-gaokao-physics",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_jec_qa_ca": {
|
|
"task": "agieval_jec_qa_ca",
|
|
"dataset_path": "hails/agieval-jec-qa-ca",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_jec_qa_kd": {
|
|
"task": "agieval_jec_qa_kd",
|
|
"dataset_path": "hails/agieval-jec-qa-kd",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_logiqa_en": {
|
|
"task": "agieval_logiqa_en",
|
|
"dataset_path": "hails/agieval-logiqa-en",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_logiqa_zh": {
|
|
"task": "agieval_logiqa_zh",
|
|
"dataset_path": "hails/agieval-logiqa-zh",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_lsat_ar": {
|
|
"task": "agieval_lsat_ar",
|
|
"dataset_path": "hails/agieval-lsat-ar",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_lsat_lr": {
|
|
"task": "agieval_lsat_lr",
|
|
"dataset_path": "hails/agieval-lsat-lr",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_lsat_rc": {
|
|
"task": "agieval_lsat_rc",
|
|
"dataset_path": "hails/agieval-lsat-rc",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_math": {
|
|
"task": "agieval_math",
|
|
"dataset_path": "hails/agieval-math",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{answer}}",
|
|
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidate = results[0]\n\n gold = doc[\"answer\"]\n\n if not gold:\n print(doc, candidate, gold)\n if is_equiv(candidate, gold):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"acc\": retval,\n }\n return results\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "generate_until",
|
|
"generation_kwargs": {
|
|
"max_gen_toks": 32,
|
|
"do_sample": false,
|
|
"temperature": 0.0,
|
|
"until": [
|
|
"Q:"
|
|
]
|
|
},
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_sat_en": {
|
|
"task": "agieval_sat_en",
|
|
"dataset_path": "hails/agieval-sat-en",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_sat_en_without_passage": {
|
|
"task": "agieval_sat_en_without_passage",
|
|
"dataset_path": "hails/agieval-sat-en-without-passage",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
},
|
|
"agieval_sat_math": {
|
|
"task": "agieval_sat_math",
|
|
"dataset_path": "hails/agieval-sat-math",
|
|
"test_split": "test",
|
|
"doc_to_text": "{{query}}",
|
|
"doc_to_target": "{{gold}}",
|
|
"doc_to_choice": "{{choices}}",
|
|
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"num_fewshot": 0,
|
|
"metric_list": [
|
|
{
|
|
"metric": "acc",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
},
|
|
{
|
|
"metric": "acc_norm",
|
|
"aggregation": "mean",
|
|
"higher_is_better": true
|
|
}
|
|
],
|
|
"output_type": "multiple_choice",
|
|
"repeats": 1,
|
|
"should_decontaminate": false,
|
|
"metadata": {
|
|
"version": 1.0
|
|
}
|
|
}
|
|
},
|
|
"versions": {
|
|
"agieval": 0.0,
|
|
"agieval_aqua_rat": 1.0,
|
|
"agieval_gaokao_biology": 1.0,
|
|
"agieval_gaokao_chemistry": 1.0,
|
|
"agieval_gaokao_chinese": 1.0,
|
|
"agieval_gaokao_english": 1.0,
|
|
"agieval_gaokao_geography": 1.0,
|
|
"agieval_gaokao_history": 1.0,
|
|
"agieval_gaokao_mathcloze": 1.0,
|
|
"agieval_gaokao_mathqa": 1.0,
|
|
"agieval_gaokao_physics": 1.0,
|
|
"agieval_jec_qa_ca": 1.0,
|
|
"agieval_jec_qa_kd": 1.0,
|
|
"agieval_logiqa_en": 1.0,
|
|
"agieval_logiqa_zh": 1.0,
|
|
"agieval_lsat_ar": 1.0,
|
|
"agieval_lsat_lr": 1.0,
|
|
"agieval_lsat_rc": 1.0,
|
|
"agieval_math": 1.0,
|
|
"agieval_sat_en": 1.0,
|
|
"agieval_sat_en_without_passage": 1.0,
|
|
"agieval_sat_math": 1.0
|
|
},
|
|
"n-shot": {
|
|
"agieval_aqua_rat": 0,
|
|
"agieval_gaokao_biology": 0,
|
|
"agieval_gaokao_chemistry": 0,
|
|
"agieval_gaokao_chinese": 0,
|
|
"agieval_gaokao_english": 0,
|
|
"agieval_gaokao_geography": 0,
|
|
"agieval_gaokao_history": 0,
|
|
"agieval_gaokao_mathcloze": 0,
|
|
"agieval_gaokao_mathqa": 0,
|
|
"agieval_gaokao_physics": 0,
|
|
"agieval_jec_qa_ca": 0,
|
|
"agieval_jec_qa_kd": 0,
|
|
"agieval_logiqa_en": 0,
|
|
"agieval_logiqa_zh": 0,
|
|
"agieval_lsat_ar": 0,
|
|
"agieval_lsat_lr": 0,
|
|
"agieval_lsat_rc": 0,
|
|
"agieval_math": 0,
|
|
"agieval_sat_en": 0,
|
|
"agieval_sat_en_without_passage": 0,
|
|
"agieval_sat_math": 0
|
|
},
|
|
"higher_is_better": {
|
|
"agieval": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_aqua_rat": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_gaokao_biology": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_gaokao_chemistry": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_gaokao_chinese": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_gaokao_english": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_gaokao_geography": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_gaokao_history": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_gaokao_mathcloze": {
|
|
"acc": true
|
|
},
|
|
"agieval_gaokao_mathqa": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_gaokao_physics": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_jec_qa_ca": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_jec_qa_kd": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_logiqa_en": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_logiqa_zh": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_lsat_ar": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_lsat_lr": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_lsat_rc": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_math": {
|
|
"acc": true
|
|
},
|
|
"agieval_sat_en": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_sat_en_without_passage": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
},
|
|
"agieval_sat_math": {
|
|
"acc": true,
|
|
"acc_norm": true
|
|
}
|
|
},
|
|
"n-samples": {
|
|
"agieval_gaokao_biology": {
|
|
"original": 210,
|
|
"effective": 210
|
|
},
|
|
"agieval_gaokao_chemistry": {
|
|
"original": 207,
|
|
"effective": 207
|
|
},
|
|
"agieval_gaokao_chinese": {
|
|
"original": 246,
|
|
"effective": 246
|
|
},
|
|
"agieval_gaokao_geography": {
|
|
"original": 199,
|
|
"effective": 199
|
|
},
|
|
"agieval_gaokao_history": {
|
|
"original": 235,
|
|
"effective": 235
|
|
},
|
|
"agieval_gaokao_mathcloze": {
|
|
"original": 118,
|
|
"effective": 118
|
|
},
|
|
"agieval_gaokao_mathqa": {
|
|
"original": 351,
|
|
"effective": 351
|
|
},
|
|
"agieval_gaokao_physics": {
|
|
"original": 200,
|
|
"effective": 200
|
|
},
|
|
"agieval_jec_qa_ca": {
|
|
"original": 999,
|
|
"effective": 999
|
|
},
|
|
"agieval_jec_qa_kd": {
|
|
"original": 1000,
|
|
"effective": 1000
|
|
},
|
|
"agieval_logiqa_zh": {
|
|
"original": 651,
|
|
"effective": 651
|
|
},
|
|
"agieval_aqua_rat": {
|
|
"original": 254,
|
|
"effective": 254
|
|
},
|
|
"agieval_gaokao_english": {
|
|
"original": 306,
|
|
"effective": 306
|
|
},
|
|
"agieval_logiqa_en": {
|
|
"original": 651,
|
|
"effective": 651
|
|
},
|
|
"agieval_lsat_ar": {
|
|
"original": 230,
|
|
"effective": 230
|
|
},
|
|
"agieval_lsat_lr": {
|
|
"original": 510,
|
|
"effective": 510
|
|
},
|
|
"agieval_lsat_rc": {
|
|
"original": 269,
|
|
"effective": 269
|
|
},
|
|
"agieval_math": {
|
|
"original": 1000,
|
|
"effective": 1000
|
|
},
|
|
"agieval_sat_en_without_passage": {
|
|
"original": 206,
|
|
"effective": 206
|
|
},
|
|
"agieval_sat_en": {
|
|
"original": 206,
|
|
"effective": 206
|
|
},
|
|
"agieval_sat_math": {
|
|
"original": 220,
|
|
"effective": 220
|
|
}
|
|
},
|
|
"config": {
|
|
"model": "hf",
|
|
"model_args": "pretrained=inceptionai/jais-family-30b-8k-chat,trust_remote_code=True,cache_dir=/tmp,parallelize=True",
|
|
"model_num_parameters": 30208489464,
|
|
"model_dtype": "torch.float32",
|
|
"model_revision": "main",
|
|
"model_sha": "dab185164dd3b79ec9201d7f4cf878ce91ae7e14",
|
|
"batch_size": "auto",
|
|
"batch_sizes": [
|
|
1
|
|
],
|
|
"device": null,
|
|
"use_cache": null,
|
|
"limit": null,
|
|
"bootstrap_iters": 100000,
|
|
"gen_kwargs": null,
|
|
"random_seed": 0,
|
|
"numpy_seed": 1234,
|
|
"torch_seed": 1234,
|
|
"fewshot_seed": 1234
|
|
},
|
|
"git_hash": "8e1bd48d",
|
|
"date": 1736969439.7554727,
|
|
"pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.27.9\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-1064-azure-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.3.107\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100 80GB PCIe\nGPU 1: NVIDIA A100 80GB PCIe\nGPU 2: NVIDIA A100 80GB PCIe\nGPU 3: NVIDIA A100 80GB PCIe\n\nNvidia driver version: 535.161.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V13 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 1\nBogoMIPS: 4890.87\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (12 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.15.0rc2\n[pip3] open_clip_torch==2.26.1\n[pip3] optree==0.10.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.2.0a0\n[pip3] torchdata==0.7.0a0\n[pip3] torchdiffeq==0.2.4\n[pip3] torchmetrics==1.4.1\n[pip3] torchsde==0.2.6\n[pip3] torchtext==0.17.0a0\n[pip3] torchvision==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
|
|
"transformers_version": "4.44.0",
|
|
"upper_git_hash": null,
|
|
"tokenizer_pad_token": [
|
|
"<|endoftext|>",
|
|
"0"
|
|
],
|
|
"tokenizer_eos_token": [
|
|
"<|endoftext|>",
|
|
"0"
|
|
],
|
|
"tokenizer_bos_token": [
|
|
"<|endoftext|>",
|
|
"0"
|
|
],
|
|
"eot_token_id": 0,
|
|
"max_length": 8192,
|
|
"task_hashes": {},
|
|
"model_source": "hf",
|
|
"model_name": "inceptionai/jais-family-30b-8k-chat",
|
|
"model_name_sanitized": "inceptionai__jais-family-30b-8k-chat",
|
|
"system_instruction": null,
|
|
"system_instruction_sha": null,
|
|
"fewshot_as_multiturn": false,
|
|
"chat_template": null,
|
|
"chat_template_sha": null,
|
|
"start_time": 825737.11323678,
|
|
"end_time": 833525.267133533,
|
|
"total_evaluation_time_seconds": "7788.153896752978"
|
|
} |