{ "results": { "agieval": { "acc,none": 0.42392356071601356, "acc_stderr,none": 0.004999593208027632, "alias": "agieval" }, "agieval_aqua_rat": { "alias": " - agieval_aqua_rat", "acc,none": 0.2952755905511811, "acc_stderr,none": 0.02867894492686086, "acc_norm,none": 0.25196850393700787, "acc_norm_stderr,none": 0.027294353392553598 }, "agieval_gaokao_biology": { "alias": " - agieval_gaokao_biology", "acc,none": 0.49047619047619045, "acc_stderr,none": 0.034579448570031264, "acc_norm,none": 0.48095238095238096, "acc_norm_stderr,none": 0.034560617865111484 }, "agieval_gaokao_chemistry": { "alias": " - agieval_gaokao_chemistry", "acc,none": 0.38164251207729466, "acc_stderr,none": 0.03384656305081144, "acc_norm,none": 0.30434782608695654, "acc_norm_stderr,none": 0.032058822365635266 }, "agieval_gaokao_chinese": { "alias": " - agieval_gaokao_chinese", "acc,none": 0.4349593495934959, "acc_stderr,none": 0.031672412111456834, "acc_norm,none": 0.43089430894308944, "acc_norm_stderr,none": 0.03163725545151277 }, "agieval_gaokao_english": { "alias": " - agieval_gaokao_english", "acc,none": 0.7516339869281046, "acc_stderr,none": 0.02473998135511359, "acc_norm,none": 0.7450980392156863, "acc_norm_stderr,none": 0.024954184324879912 }, "agieval_gaokao_geography": { "alias": " - agieval_gaokao_geography", "acc,none": 0.592964824120603, "acc_stderr,none": 0.03491385802519053, "acc_norm,none": 0.5678391959798995, "acc_norm_stderr,none": 0.035204872502584535 }, "agieval_gaokao_history": { "alias": " - agieval_gaokao_history", "acc,none": 0.5361702127659574, "acc_stderr,none": 0.03260038511835771, "acc_norm,none": 0.4808510638297872, "acc_norm_stderr,none": 0.032662042990646796 }, "agieval_gaokao_mathcloze": { "alias": " - agieval_gaokao_mathcloze", "acc,none": 0.03389830508474576, "acc_stderr,none": 0.016730444637044904 }, "agieval_gaokao_mathqa": { "alias": " - agieval_gaokao_mathqa", "acc,none": 0.301994301994302, "acc_stderr,none": 0.02454114583174699, "acc_norm,none": 0.2934472934472934, "acc_norm_stderr,none": 0.024339032696810918 }, "agieval_gaokao_physics": { "alias": " - agieval_gaokao_physics", "acc,none": 0.435, "acc_stderr,none": 0.03514328173714407, "acc_norm,none": 0.435, "acc_norm_stderr,none": 0.03514328173714407 }, "agieval_jec_qa_ca": { "alias": " - agieval_jec_qa_ca", "acc,none": 0.5445445445445446, "acc_stderr,none": 0.015764289047389874, "acc_norm,none": 0.4974974974974975, "acc_norm_stderr,none": 0.015827025208013587 }, "agieval_jec_qa_kd": { "alias": " - agieval_jec_qa_kd", "acc,none": 0.622, "acc_stderr,none": 0.015341165254026649, "acc_norm,none": 0.56, "acc_norm_stderr,none": 0.015704987954361784 }, "agieval_logiqa_en": { "alias": " - agieval_logiqa_en", "acc,none": 0.3686635944700461, "acc_stderr,none": 0.018922951005122538, "acc_norm,none": 0.3824884792626728, "acc_norm_stderr,none": 0.019062288283575927 }, "agieval_logiqa_zh": { "alias": " - agieval_logiqa_zh", "acc,none": 0.35176651305683565, "acc_stderr,none": 0.01872993627442735, "acc_norm,none": 0.3824884792626728, "acc_norm_stderr,none": 0.019062288283575913 }, "agieval_lsat_ar": { "alias": " - agieval_lsat_ar", "acc,none": 0.19130434782608696, "acc_stderr,none": 0.025991852462828483, "acc_norm,none": 0.20434782608695654, "acc_norm_stderr,none": 0.026645808150011344 }, "agieval_lsat_lr": { "alias": " - agieval_lsat_lr", "acc,none": 0.45294117647058824, "acc_stderr,none": 0.02206373457408461, "acc_norm,none": 0.4235294117647059, "acc_norm_stderr,none": 0.021901379648792144 }, "agieval_lsat_rc": { "alias": " - agieval_lsat_rc", "acc,none": 0.6356877323420075, "acc_stderr,none": 0.02939621506324139, "acc_norm,none": 0.5650557620817844, "acc_norm_stderr,none": 0.030282731632881126 }, "agieval_math": { "alias": " - agieval_math", "acc,none": 0.056, "acc_stderr,none": 0.007274401481697056 }, "agieval_sat_en": { "alias": " - agieval_sat_en", "acc,none": 0.7718446601941747, "acc_stderr,none": 0.02930915787324171, "acc_norm,none": 0.7135922330097088, "acc_norm_stderr,none": 0.031574793744217594 }, "agieval_sat_en_without_passage": { "alias": " - agieval_sat_en_without_passage", "acc,none": 0.39805825242718446, "acc_stderr,none": 0.03418799390613399, "acc_norm,none": 0.34951456310679613, "acc_norm_stderr,none": 0.03330232052876046 }, "agieval_sat_math": { "alias": " - agieval_sat_math", "acc,none": 0.41818181818181815, "acc_stderr,none": 0.03333144641627121, "acc_norm,none": 0.33636363636363636, "acc_norm_stderr,none": 0.03192622349349311 } }, "groups": { "agieval": { "acc,none": 0.42392356071601356, "acc_stderr,none": 0.004999593208027632, "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": "vllm", "model_args": "pretrained=meta-llama/Meta-Llama-3.1-8B-Instruct,tensor_parallel_size=1,data_parallel_size=8,gpu_memory_utilization=0.8,download_dir=/tmp,enforce_eager=True", "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": "788a3672", "date": 1737961150.0996048, "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": "4.48.1", "upper_git_hash": null, "tokenizer_pad_token": [ "<|eot_id|>", "128009" ], "tokenizer_eos_token": [ "<|eot_id|>", "128009" ], "tokenizer_bos_token": [ "<|begin_of_text|>", "128000" ], "eot_token_id": 128009, "max_length": 131072, "task_hashes": { "agieval_gaokao_biology": "48856850a9c3cb2bdd072c002e182cf4dc1270c513df1b196c07cd50c35ee312", "agieval_gaokao_chemistry": "298b30fddb559f13b752f13e9d5df9870ed193e55d393fa75daabc989f6d14a2", "agieval_gaokao_chinese": "dbde0aa44b028bf2ae28c3e3bd3eb4b5c76a1c9e335b93377719aeae0f385089", "agieval_gaokao_geography": "0f6315ed900034917ccc6a2a7e8af396ac5450984f5d2995966f4e6d944ddca7", "agieval_gaokao_history": "477fc7b6346abd5e6d7899fbdf17f9b6480fcee718412afe23efcf7d2b467c99", "agieval_gaokao_mathcloze": "e7d869494f25d82eb72aae9a978c044d2dd05456eb59288f5396caa2e976c37c", "agieval_gaokao_mathqa": "a990d2387b02674e639121eeaf4bf747d0b7950638c0cf305818e1e7307271cd", "agieval_gaokao_physics": "b35f0e58df73200a0b4bd485904fa2f31ddcbdb906d62166a21715a9fec13df6", "agieval_jec_qa_ca": "8ece590313c402549921441fee0b161996f57a073d2562f41dcab194adf3d6e1", "agieval_jec_qa_kd": "f968b31c5a4a5b2e2a309162cc1966ce2d859ae3db467b9bf77aec1dcf3da313", "agieval_logiqa_zh": "e7dfec6cca6c9d836bcf0090fa307a59af484030c0395793b9ef4890dd73dae7", "agieval_aqua_rat": "2186c15644e0585992df4e6090e4cbdc623f814a4725803c9fe053a3c6eee826", "agieval_gaokao_english": "1997a0d2b769dd5690676a55acba44f9655257b3ec335745d4f8b70045941028", "agieval_logiqa_en": "8cbc44ae4163ae2093f88be6eb95327bd0ac1c1aef48c40549bf0769b43aa0de", "agieval_lsat_ar": "d09b7b14ebb5f21bbd602143c8fc62a4edef6a64ab0f6eb87b9aafa7a4426c43", "agieval_lsat_lr": "a5cd32cd2a2759d428ef21fd2e8362276fe0b15dc1fff48fe30f6f39525d1336", "agieval_lsat_rc": "ce4856d4b9eaa4beb1ab1cb0e139f73d4097298e16e06025258b05b3d422b0eb", "agieval_math": "c4edf8986242f57ad6d5c1cb001b194b30d20a60bd6fb0909cb37b5e0d6d5c56", "agieval_sat_en_without_passage": "11bfc5e60248d5acab69f12abac189f630e0b3ad7dc8cdb9db8ccdc040516bb0", "agieval_sat_en": "3bb865c97a1fcec9154b1dbbae2bac428982fb809d8d42bb1ddb83199881c7ac", "agieval_sat_math": "63798581920be3a992f61dab8df71eb75cb455163fca9ea156540d204951c2c2" }, "model_source": "vllm", "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct", "model_name_sanitized": "meta-llama__Meta-Llama-3.1-8B-Instruct", "system_instruction": null, "system_instruction_sha": null, "fewshot_as_multiturn": false, "chat_template": null, "chat_template_sha": null, "start_time": 990290.065929208, "end_time": 990703.867264399, "total_evaluation_time_seconds": "413.8013351910049" }