| { | |
| "results": { | |
| "agieval": { | |
| "acc,none": 0.7109337203676827, | |
| "acc_stderr,none": 0.00411658454162476, | |
| "alias": "agieval" | |
| }, | |
| "agieval_aqua_rat": { | |
| "alias": " - agieval_aqua_rat", | |
| "acc,none": 0.5905511811023622, | |
| "acc_stderr,none": 0.03091493387931976, | |
| "acc_norm,none": 0.5787401574803149, | |
| "acc_norm_stderr,none": 0.031042492081410127 | |
| }, | |
| "agieval_gaokao_biology": { | |
| "alias": " - agieval_gaokao_biology", | |
| "acc,none": 0.9285714285714286, | |
| "acc_stderr,none": 0.017814371196065843, | |
| "acc_norm,none": 0.9285714285714286, | |
| "acc_norm_stderr,none": 0.017814371196065843 | |
| }, | |
| "agieval_gaokao_chemistry": { | |
| "alias": " - agieval_gaokao_chemistry", | |
| "acc,none": 0.8405797101449275, | |
| "acc_stderr,none": 0.02550513569429598, | |
| "acc_norm,none": 0.7777777777777778, | |
| "acc_norm_stderr,none": 0.028965958105927822 | |
| }, | |
| "agieval_gaokao_chinese": { | |
| "alias": " - agieval_gaokao_chinese", | |
| "acc,none": 0.8902439024390244, | |
| "acc_stderr,none": 0.019970355234713685, | |
| "acc_norm,none": 0.8739837398373984, | |
| "acc_norm_stderr,none": 0.021202248854272642 | |
| }, | |
| "agieval_gaokao_english": { | |
| "alias": " - agieval_gaokao_english", | |
| "acc,none": 0.8104575163398693, | |
| "acc_stderr,none": 0.022442358263336182, | |
| "acc_norm,none": 0.8398692810457516, | |
| "acc_norm_stderr,none": 0.020998740930362303 | |
| }, | |
| "agieval_gaokao_geography": { | |
| "alias": " - agieval_gaokao_geography", | |
| "acc,none": 0.8994974874371859, | |
| "acc_stderr,none": 0.02136760475548775, | |
| "acc_norm,none": 0.8994974874371859, | |
| "acc_norm_stderr,none": 0.02136760475548775 | |
| }, | |
| "agieval_gaokao_history": { | |
| "alias": " - agieval_gaokao_history", | |
| "acc,none": 0.9319148936170213, | |
| "acc_stderr,none": 0.0164666880348399, | |
| "acc_norm,none": 0.9659574468085106, | |
| "acc_norm_stderr,none": 0.01185446970478215 | |
| }, | |
| "agieval_gaokao_mathcloze": { | |
| "alias": " - agieval_gaokao_mathcloze", | |
| "acc,none": 0.11016949152542373, | |
| "acc_stderr,none": 0.02894618860440566 | |
| }, | |
| "agieval_gaokao_mathqa": { | |
| "alias": " - agieval_gaokao_mathqa", | |
| "acc,none": 0.6609686609686609, | |
| "acc_stderr,none": 0.025303251636666108, | |
| "acc_norm,none": 0.6410256410256411, | |
| "acc_norm_stderr,none": 0.025641025641025647 | |
| }, | |
| "agieval_gaokao_physics": { | |
| "alias": " - agieval_gaokao_physics", | |
| "acc,none": 0.92, | |
| "acc_stderr,none": 0.01923146500480799, | |
| "acc_norm,none": 0.905, | |
| "acc_norm_stderr,none": 0.02078545587374491 | |
| }, | |
| "agieval_jec_qa_ca": { | |
| "alias": " - agieval_jec_qa_ca", | |
| "acc,none": 0.8758758758758759, | |
| "acc_stderr,none": 0.01043720251442883, | |
| "acc_norm,none": 0.8548548548548549, | |
| "acc_norm_stderr,none": 0.011150187682575276 | |
| }, | |
| "agieval_jec_qa_kd": { | |
| "alias": " - agieval_jec_qa_kd", | |
| "acc,none": 0.92, | |
| "acc_stderr,none": 0.008583336977753651, | |
| "acc_norm,none": 0.887, | |
| "acc_norm_stderr,none": 0.010016552866696856 | |
| }, | |
| "agieval_logiqa_en": { | |
| "alias": " - agieval_logiqa_en", | |
| "acc,none": 0.6267281105990783, | |
| "acc_stderr,none": 0.01897123271547206, | |
| "acc_norm,none": 0.6129032258064516, | |
| "acc_norm_stderr,none": 0.01910508839198029 | |
| }, | |
| "agieval_logiqa_zh": { | |
| "alias": " - agieval_logiqa_zh", | |
| "acc,none": 0.7096774193548387, | |
| "acc_stderr,none": 0.01780386214853801, | |
| "acc_norm,none": 0.6927803379416283, | |
| "acc_norm_stderr,none": 0.018095292260828216 | |
| }, | |
| "agieval_lsat_ar": { | |
| "alias": " - agieval_lsat_ar", | |
| "acc,none": 0.30869565217391304, | |
| "acc_stderr,none": 0.03052686171290101, | |
| "acc_norm,none": 0.2956521739130435, | |
| "acc_norm_stderr,none": 0.030155489768916202 | |
| }, | |
| "agieval_lsat_lr": { | |
| "alias": " - agieval_lsat_lr", | |
| "acc,none": 0.8509803921568627, | |
| "acc_stderr,none": 0.015784200670552844, | |
| "acc_norm,none": 0.8450980392156863, | |
| "acc_norm_stderr,none": 0.016036999418614126 | |
| }, | |
| "agieval_lsat_rc": { | |
| "alias": " - agieval_lsat_rc", | |
| "acc,none": 0.8475836431226765, | |
| "acc_stderr,none": 0.021955315121071486, | |
| "acc_norm,none": 0.8327137546468402, | |
| "acc_norm_stderr,none": 0.022798726518245306 | |
| }, | |
| "agieval_math": { | |
| "alias": " - agieval_math", | |
| "acc,none": 0.161, | |
| "acc_stderr,none": 0.011628164696727181 | |
| }, | |
| "agieval_sat_en": { | |
| "alias": " - agieval_sat_en", | |
| "acc,none": 0.9368932038834952, | |
| "acc_stderr,none": 0.016982678176624688, | |
| "acc_norm,none": 0.9223300970873787, | |
| "acc_norm_stderr,none": 0.018693586887038226 | |
| }, | |
| "agieval_sat_en_without_passage": { | |
| "alias": " - agieval_sat_en_without_passage", | |
| "acc,none": 0.6359223300970874, | |
| "acc_stderr,none": 0.03360641055142778, | |
| "acc_norm,none": 0.6067961165048543, | |
| "acc_norm_stderr,none": 0.034115627597025605 | |
| }, | |
| "agieval_sat_math": { | |
| "alias": " - agieval_sat_math", | |
| "acc,none": 0.8272727272727273, | |
| "acc_stderr,none": 0.025543638189954865, | |
| "acc_norm,none": 0.7954545454545454, | |
| "acc_norm_stderr,none": 0.027257156202504098 | |
| } | |
| }, | |
| "groups": { | |
| "agieval": { | |
| "acc,none": 0.7109337203676827, | |
| "acc_stderr,none": 0.00411658454162476, | |
| "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, | |
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| { | |
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| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
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| "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": " ", | |
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| } | |
| ], | |
| "output_type": "multiple_choice", | |
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| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| } | |
| }, | |
| "versions": { | |
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| "agieval_gaokao_mathqa": 1.0, | |
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| "agieval_sat_en": 1.0, | |
| "agieval_sat_en_without_passage": 1.0, | |
| "agieval_sat_math": 1.0 | |
| }, | |
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| }, | |
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| }, | |
| "agieval_gaokao_english": { | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
| "agieval_gaokao_mathqa": { | |
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| }, | |
| "agieval_gaokao_physics": { | |
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| "effective": 200 | |
| }, | |
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| "effective": 999 | |
| }, | |
| "agieval_jec_qa_kd": { | |
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| }, | |
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| }, | |
| "agieval_aqua_rat": { | |
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| }, | |
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| }, | |
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| "effective": 510 | |
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| "agieval_lsat_rc": { | |
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| "effective": 269 | |
| }, | |
| "agieval_math": { | |
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| "effective": 1000 | |
| }, | |
| "agieval_sat_en_without_passage": { | |
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| "effective": 206 | |
| }, | |
| "agieval_sat_en": { | |
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| "agieval_sat_math": { | |
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| "effective": 220 | |
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| "config": { | |
| "model": "hf", | |
| "model_args": "pretrained=Qwen/Qwen2.5-72B-Instruct,trust_remote_code=True,cache_dir=/tmp,parallelize=True", | |
| "model_num_parameters": 72706203648, | |
| "model_dtype": "torch.bfloat16", | |
| "model_revision": "main", | |
| "model_sha": "d3d951150c1e5848237cd6a7ad11df4836aee842", | |
| "batch_size": 1, | |
| "batch_sizes": [], | |
| "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": 1736540156.5705156, | |
| "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|>", | |
| "151643" | |
| ], | |
| "tokenizer_eos_token": [ | |
| "<|im_end|>", | |
| "151645" | |
| ], | |
| "tokenizer_bos_token": [ | |
| null, | |
| "None" | |
| ], | |
| "eot_token_id": 151645, | |
| "max_length": 32768, | |
| "task_hashes": {}, | |
| "model_source": "hf", | |
| "model_name": "Qwen/Qwen2.5-72B-Instruct", | |
| "model_name_sanitized": "Qwen__Qwen2.5-72B-Instruct", | |
| "system_instruction": null, | |
| "system_instruction_sha": null, | |
| "fewshot_as_multiturn": false, | |
| "chat_template": null, | |
| "chat_template_sha": null, | |
| "start_time": 396454.035052041, | |
| "end_time": 402466.480592644, | |
| "total_evaluation_time_seconds": "6012.445540603017" | |
| } |