{ "results": { "agieval": { "acc,none": 0.36453797774552493, "acc_stderr,none": 0.004942349596688666, "alias": "agieval" }, "agieval_aqua_rat": { "alias": " - agieval_aqua_rat", "acc,none": 0.2283464566929134, "acc_stderr,none": 0.026390526537822135, "acc_norm,none": 0.20866141732283464, "acc_norm_stderr,none": 0.02554712225493389 }, "agieval_gaokao_biology": { "alias": " - agieval_gaokao_biology", "acc,none": 0.29523809523809524, "acc_stderr,none": 0.03155253554505397, "acc_norm,none": 0.3476190476190476, "acc_norm_stderr,none": 0.032940430891650836 }, "agieval_gaokao_chemistry": { "alias": " - agieval_gaokao_chemistry", "acc,none": 0.2753623188405797, "acc_stderr,none": 0.031122831519058182, "acc_norm,none": 0.30434782608695654, "acc_norm_stderr,none": 0.03205882236563527 }, "agieval_gaokao_chinese": { "alias": " - agieval_gaokao_chinese", "acc,none": 0.3048780487804878, "acc_stderr,none": 0.02941105055075626, "acc_norm,none": 0.2886178861788618, "acc_norm_stderr,none": 0.028948765576340286 }, "agieval_gaokao_english": { "alias": " - agieval_gaokao_english", "acc,none": 0.6470588235294118, "acc_stderr,none": 0.027363593284684965, "acc_norm,none": 0.6797385620915033, "acc_norm_stderr,none": 0.026716118380156858 }, "agieval_gaokao_geography": { "alias": " - agieval_gaokao_geography", "acc,none": 0.3969849246231156, "acc_stderr,none": 0.03477110537378156, "acc_norm,none": 0.3768844221105528, "acc_norm_stderr,none": 0.034439417931776 }, "agieval_gaokao_history": { "alias": " - agieval_gaokao_history", "acc,none": 0.39574468085106385, "acc_stderr,none": 0.03196758697835363, "acc_norm,none": 0.37872340425531914, "acc_norm_stderr,none": 0.031709956060406545 }, "agieval_gaokao_mathcloze": { "alias": " - agieval_gaokao_mathcloze", "acc,none": 0.025423728813559324, "acc_stderr,none": 0.014552399522167078 }, "agieval_gaokao_mathqa": { "alias": " - agieval_gaokao_mathqa", "acc,none": 0.23931623931623933, "acc_stderr,none": 0.022806263357480903, "acc_norm,none": 0.25925925925925924, "acc_norm_stderr,none": 0.023424278964210166 }, "agieval_gaokao_physics": { "alias": " - agieval_gaokao_physics", "acc,none": 0.275, "acc_stderr,none": 0.031652557907861915, "acc_norm,none": 0.265, "acc_norm_stderr,none": 0.03128528159088722 }, "agieval_jec_qa_ca": { "alias": " - agieval_jec_qa_ca", "acc,none": 0.5065065065065065, "acc_stderr,none": 0.01582588330988679, "acc_norm,none": 0.4934934934934935, "acc_norm_stderr,none": 0.01582588330988679 }, "agieval_jec_qa_kd": { "alias": " - agieval_jec_qa_kd", "acc,none": 0.533, "acc_stderr,none": 0.015784807891138772, "acc_norm,none": 0.533, "acc_norm_stderr,none": 0.015784807891138775 }, "agieval_logiqa_en": { "alias": " - agieval_logiqa_en", "acc,none": 0.35176651305683565, "acc_stderr,none": 0.018729936274427355, "acc_norm,none": 0.3671274961597542, "acc_norm_stderr,none": 0.018906445694655587 }, "agieval_logiqa_zh": { "alias": " - agieval_logiqa_zh", "acc,none": 0.3425499231950845, "acc_stderr,none": 0.018613868829208027, "acc_norm,none": 0.35944700460829493, "acc_norm_stderr,none": 0.018820809084481267 }, "agieval_lsat_ar": { "alias": " - agieval_lsat_ar", "acc,none": 0.22608695652173913, "acc_stderr,none": 0.02764178570724134, "acc_norm,none": 0.2391304347826087, "acc_norm_stderr,none": 0.028187385293933942 }, "agieval_lsat_lr": { "alias": " - agieval_lsat_lr", "acc,none": 0.4117647058823529, "acc_stderr,none": 0.02181429628344194, "acc_norm,none": 0.4137254901960784, "acc_norm_stderr,none": 0.021829699356254582 }, "agieval_lsat_rc": { "alias": " - agieval_lsat_rc", "acc,none": 0.5092936802973977, "acc_stderr,none": 0.030537084593525405, "acc_norm,none": 0.5018587360594795, "acc_norm_stderr,none": 0.030542150046756422 }, "agieval_math": { "alias": " - agieval_math", "acc,none": 0.038, "acc_stderr,none": 0.006049181150584934 }, "agieval_sat_en": { "alias": " - agieval_sat_en", "acc,none": 0.7233009708737864, "acc_stderr,none": 0.03124542318927994, "acc_norm,none": 0.6990291262135923, "acc_norm_stderr,none": 0.03203560571847412 }, "agieval_sat_en_without_passage": { "alias": " - agieval_sat_en_without_passage", "acc,none": 0.47572815533980584, "acc_stderr,none": 0.034880344423561846, "acc_norm,none": 0.4368932038834951, "acc_norm_stderr,none": 0.03464225055241279 }, "agieval_sat_math": { "alias": " - agieval_sat_math", "acc,none": 0.3409090909090909, "acc_stderr,none": 0.03203095553573995, "acc_norm,none": 0.2818181818181818, "acc_norm_stderr,none": 0.030400424640665242 } }, "groups": { "agieval": { "acc,none": 0.36453797774552493, "acc_stderr,none": 0.004942349596688666, "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-7B-Instruct-v0.3,trust_remote_code=True,cache_dir=/tmp,parallelize=False", "model_num_parameters": 7248023552, "model_dtype": "torch.bfloat16", "model_revision": "main", "model_sha": "e0bc86c23ce5aae1db576c8cca6f06f1f73af2db", "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": "112b79143", "date": 1739246582.6735382, "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-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\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): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V12 64-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 0\nBogoMIPS: 4890.88\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 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 ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid\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 (24 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: Mitigation; untrained return thunk; SMT disabled\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\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": "4.48.3", "upper_git_hash": null, "tokenizer_pad_token": [ "", "0" ], "tokenizer_eos_token": [ "", "2" ], "tokenizer_bos_token": [ "", "1" ], "eot_token_id": 2, "max_length": 32768, "task_hashes": {}, "model_source": "hf", "model_name": "mistralai/Mistral-7B-Instruct-v0.3", "model_name_sanitized": "mistralai__Mistral-7B-Instruct-v0.3", "system_instruction": null, "system_instruction_sha": null, "fewshot_as_multiturn": false, "chat_template": null, "chat_template_sha": null, "start_time": 1357016.699727388, "end_time": 1359325.218546683, "total_evaluation_time_seconds": "2308.51881929487" }