{ "results": { "agieval": { "acc,none": 0.4075955491049831, "acc_stderr,none": 0.005091854332120318, "alias": "agieval" }, "agieval_aqua_rat": { "alias": " - agieval_aqua_rat", "acc,none": 0.27165354330708663, "acc_stderr,none": 0.027965103587140407, "acc_norm,none": 0.29133858267716534, "acc_norm_stderr,none": 0.02856657247427777 }, "agieval_gaokao_biology": { "alias": " - agieval_gaokao_biology", "acc,none": 0.3761904761904762, "acc_stderr,none": 0.033508636451125194, "acc_norm,none": 0.4, "acc_norm_stderr,none": 0.033886949683494226 }, "agieval_gaokao_chemistry": { "alias": " - agieval_gaokao_chemistry", "acc,none": 0.25120772946859904, "acc_stderr,none": 0.030217850292985352, "acc_norm,none": 0.2946859903381642, "acc_norm_stderr,none": 0.03176416108295296 }, "agieval_gaokao_chinese": { "alias": " - agieval_gaokao_chinese", "acc,none": 0.3821138211382114, "acc_stderr,none": 0.031043277811452864, "acc_norm,none": 0.36585365853658536, "acc_norm_stderr,none": 0.030772685945393178 }, "agieval_gaokao_english": { "alias": " - agieval_gaokao_english", "acc,none": 0.6993464052287581, "acc_stderr,none": 0.026256053835718964, "acc_norm,none": 0.6993464052287581, "acc_norm_stderr,none": 0.026256053835718968 }, "agieval_gaokao_geography": { "alias": " - agieval_gaokao_geography", "acc,none": 0.5628140703517588, "acc_stderr,none": 0.0352519354412315, "acc_norm,none": 0.5376884422110553, "acc_norm_stderr,none": 0.0354323641735603 }, "agieval_gaokao_history": { "alias": " - agieval_gaokao_history", "acc,none": 0.5319148936170213, "acc_stderr,none": 0.03261936918467382, "acc_norm,none": 0.4978723404255319, "acc_norm_stderr,none": 0.03268572658667492 }, "agieval_gaokao_mathcloze": { "alias": " - agieval_gaokao_mathcloze", "acc,none": 0.06779661016949153, "acc_stderr,none": 0.023241620090605725 }, "agieval_gaokao_mathqa": { "alias": " - agieval_gaokao_mathqa", "acc,none": 0.33903133903133903, "acc_stderr,none": 0.025303251636666108, "acc_norm,none": 0.3418803418803419, "acc_norm_stderr,none": 0.025354524742207396 }, "agieval_gaokao_physics": { "alias": " - agieval_gaokao_physics", "acc,none": 0.41, "acc_stderr,none": 0.034865138597849274, "acc_norm,none": 0.375, "acc_norm_stderr,none": 0.03431856376795913 }, "agieval_jec_qa_ca": { "alias": " - agieval_jec_qa_ca", "acc,none": 0.5125125125125125, "acc_stderr,none": 0.015822266755467843, "acc_norm,none": 0.4824824824824825, "acc_norm_stderr,none": 0.01581750687141562 }, "agieval_jec_qa_kd": { "alias": " - agieval_jec_qa_kd", "acc,none": 0.576, "acc_stderr,none": 0.015635487471405182, "acc_norm,none": 0.521, "acc_norm_stderr,none": 0.015805341148131296 }, "agieval_logiqa_en": { "alias": " - agieval_logiqa_en", "acc,none": 0.34408602150537637, "acc_stderr,none": 0.01863375065717621, "acc_norm,none": 0.34715821812596004, "acc_norm_stderr,none": 0.018672867593776815 }, "agieval_logiqa_zh": { "alias": " - agieval_logiqa_zh", "acc,none": 0.42857142857142855, "acc_stderr,none": 0.019410463442478737, "acc_norm,none": 0.4039938556067588, "acc_norm_stderr,none": 0.019246690834000664 }, "agieval_lsat_ar": { "alias": " - agieval_lsat_ar", "acc,none": 0.23043478260869565, "acc_stderr,none": 0.027827807522276156, "acc_norm,none": 0.24347826086956523, "acc_norm_stderr,none": 0.028361099300075063 }, "agieval_lsat_lr": { "alias": " - agieval_lsat_lr", "acc,none": 0.40588235294117647, "acc_stderr,none": 0.021765939601653905, "acc_norm,none": 0.4196078431372549, "acc_norm_stderr,none": 0.021873771696750578 }, "agieval_lsat_rc": { "alias": " - agieval_lsat_rc", "acc,none": 0.49814126394052044, "acc_stderr,none": 0.030542150046756422, "acc_norm,none": 0.49070631970260226, "acc_norm_stderr,none": 0.030537084593525398 }, "agieval_math": { "alias": " - agieval_math", "acc,none": 0.097, "acc_stderr,none": 0.009363689373248133 }, "agieval_sat_en": { "alias": " - agieval_sat_en", "acc,none": 0.7184466019417476, "acc_stderr,none": 0.03141236994965781, "acc_norm,none": 0.6941747572815534, "acc_norm_stderr,none": 0.032180600400244896 }, "agieval_sat_en_without_passage": { "alias": " - agieval_sat_en_without_passage", "acc,none": 0.46116504854368934, "acc_stderr,none": 0.03481602144131183, "acc_norm,none": 0.41262135922330095, "acc_norm_stderr,none": 0.03438412659410016 }, "agieval_sat_math": { "alias": " - agieval_sat_math", "acc,none": 0.41363636363636364, "acc_stderr,none": 0.033279041789669776, "acc_norm,none": 0.34545454545454546, "acc_norm_stderr,none": 0.03213241030708864 } }, "groups": { "agieval": { "acc,none": 0.4075955491049831, "acc_stderr,none": 0.005091854332120318, "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=mistralai/Mistral-Small-Instruct-2409,trust_remote_code=True,cache_dir=/tmp,parallelize=False", "model_num_parameters": 22247282688, "model_dtype": "torch.bfloat16", "model_revision": "main", "model_sha": "8012044390bdc1c6d8ab162f5416220f43bf517b", "batch_size": "auto", "batch_sizes": [ 16 ], "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": "5e10e017", "date": 1736973492.865733, "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 3.27.1\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [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: 12.2.128\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100 80GB PCIe\nGPU 1: 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.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\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): 48\nOn-line CPU(s) list: 0-47\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): 1\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: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 24 MiB (48 instances)\nL3 cache: 192 MiB (6 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\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==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.0.0.dev0\n[pip3] torchaudio==2.1.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchmetrics==1.2.0\n[pip3] torchvision==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect", "transformers_version": "4.48.0", "upper_git_hash": "2e5cd5395faf76fea1afc96dd0f7161a9d3aa145", "tokenizer_pad_token": [ "", "2" ], "tokenizer_eos_token": [ "", "2" ], "tokenizer_bos_token": [ "", "1" ], "eot_token_id": 2, "max_length": 32768, "task_hashes": {}, "model_source": "hf", "model_name": "mistralai/Mistral-Small-Instruct-2409", "model_name_sanitized": "mistralai__Mistral-Small-Instruct-2409", "system_instruction": null, "system_instruction_sha": null, "fewshot_as_multiturn": false, "chat_template": null, "chat_template_sha": null, "start_time": 14974.748424363, "end_time": 16899.11974055, "total_evaluation_time_seconds": "1924.3713161869982" }