| { | |
| "results": { | |
| "agieval": { | |
| "acc,none": 0.5544267053701016, | |
| "acc_stderr,none": 0.004859843455357734, | |
| "alias": "agieval" | |
| }, | |
| "agieval_aqua_rat": { | |
| "alias": " - agieval_aqua_rat", | |
| "acc,none": 0.3700787401574803, | |
| "acc_stderr,none": 0.03035497929089593, | |
| "acc_norm,none": 0.38188976377952755, | |
| "acc_norm_stderr,none": 0.03054511159403859 | |
| }, | |
| "agieval_gaokao_biology": { | |
| "alias": " - agieval_gaokao_biology", | |
| "acc,none": 0.7380952380952381, | |
| "acc_stderr,none": 0.030412684459928757, | |
| "acc_norm,none": 0.7047619047619048, | |
| "acc_norm_stderr,none": 0.03155253554505398 | |
| }, | |
| "agieval_gaokao_chemistry": { | |
| "alias": " - agieval_gaokao_chemistry", | |
| "acc,none": 0.4444444444444444, | |
| "acc_stderr,none": 0.034620941824986436, | |
| "acc_norm,none": 0.36231884057971014, | |
| "acc_norm_stderr,none": 0.033489883876211865 | |
| }, | |
| "agieval_gaokao_chinese": { | |
| "alias": " - agieval_gaokao_chinese", | |
| "acc,none": 0.5528455284552846, | |
| "acc_stderr,none": 0.031764911338391044, | |
| "acc_norm,none": 0.5447154471544715, | |
| "acc_norm_stderr,none": 0.03181583027784235 | |
| }, | |
| "agieval_gaokao_english": { | |
| "alias": " - agieval_gaokao_english", | |
| "acc,none": 0.8464052287581699, | |
| "acc_stderr,none": 0.020645597910418787, | |
| "acc_norm,none": 0.8431372549019608, | |
| "acc_norm_stderr,none": 0.020823758837580905 | |
| }, | |
| "agieval_gaokao_geography": { | |
| "alias": " - agieval_gaokao_geography", | |
| "acc,none": 0.7688442211055276, | |
| "acc_stderr,none": 0.029959803439140443, | |
| "acc_norm,none": 0.7638190954773869, | |
| "acc_norm_stderr,none": 0.030184574030479208 | |
| }, | |
| "agieval_gaokao_history": { | |
| "alias": " - agieval_gaokao_history", | |
| "acc,none": 0.7489361702127659, | |
| "acc_stderr,none": 0.028346963777162452, | |
| "acc_norm,none": 0.7361702127659574, | |
| "acc_norm_stderr,none": 0.02880998985410295 | |
| }, | |
| "agieval_gaokao_mathcloze": { | |
| "alias": " - agieval_gaokao_mathcloze", | |
| "acc,none": 0.025423728813559324, | |
| "acc_stderr,none": 0.01455239952216708 | |
| }, | |
| "agieval_gaokao_mathqa": { | |
| "alias": " - agieval_gaokao_mathqa", | |
| "acc,none": 0.4188034188034188, | |
| "acc_stderr,none": 0.026371365163318804, | |
| "acc_norm,none": 0.37606837606837606, | |
| "acc_norm_stderr,none": 0.0258921362904796 | |
| }, | |
| "agieval_gaokao_physics": { | |
| "alias": " - agieval_gaokao_physics", | |
| "acc,none": 0.59, | |
| "acc_stderr,none": 0.034865138597849274, | |
| "acc_norm,none": 0.56, | |
| "acc_norm_stderr,none": 0.03518793763172071 | |
| }, | |
| "agieval_jec_qa_ca": { | |
| "alias": " - agieval_jec_qa_ca", | |
| "acc,none": 0.6466466466466466, | |
| "acc_stderr,none": 0.015131181922110867, | |
| "acc_norm,none": 0.5565565565565566, | |
| "acc_norm_stderr,none": 0.01572564618087532 | |
| }, | |
| "agieval_jec_qa_kd": { | |
| "alias": " - agieval_jec_qa_kd", | |
| "acc,none": 0.703, | |
| "acc_stderr,none": 0.0144568322948011, | |
| "acc_norm,none": 0.629, | |
| "acc_norm_stderr,none": 0.015283736211823187 | |
| }, | |
| "agieval_logiqa_en": { | |
| "alias": " - agieval_logiqa_en", | |
| "acc,none": 0.5944700460829493, | |
| "acc_stderr,none": 0.019258381208154284, | |
| "acc_norm,none": 0.533026113671275, | |
| "acc_norm_stderr,none": 0.01956878502638526 | |
| }, | |
| "agieval_logiqa_zh": { | |
| "alias": " - agieval_logiqa_zh", | |
| "acc,none": 0.5775729646697388, | |
| "acc_stderr,none": 0.01937414753071922, | |
| "acc_norm,none": 0.5253456221198156, | |
| "acc_norm_stderr,none": 0.019586400283373922 | |
| }, | |
| "agieval_lsat_ar": { | |
| "alias": " - agieval_lsat_ar", | |
| "acc,none": 0.33043478260869563, | |
| "acc_stderr,none": 0.031082903446842964, | |
| "acc_norm,none": 0.33043478260869563, | |
| "acc_norm_stderr,none": 0.031082903446842964 | |
| }, | |
| "agieval_lsat_lr": { | |
| "alias": " - agieval_lsat_lr", | |
| "acc,none": 0.7235294117647059, | |
| "acc_stderr,none": 0.019824108780753007, | |
| "acc_norm,none": 0.6313725490196078, | |
| "acc_norm_stderr,none": 0.021383450873181317 | |
| }, | |
| "agieval_lsat_rc": { | |
| "alias": " - agieval_lsat_rc", | |
| "acc,none": 0.7992565055762082, | |
| "acc_stderr,none": 0.024467885125224527, | |
| "acc_norm,none": 0.6728624535315985, | |
| "acc_norm_stderr,none": 0.02865899432669078 | |
| }, | |
| "agieval_math": { | |
| "alias": " - agieval_math", | |
| "acc,none": 0.069, | |
| "acc_stderr,none": 0.008018934050315138 | |
| }, | |
| "agieval_sat_en": { | |
| "alias": " - agieval_sat_en", | |
| "acc,none": 0.8640776699029126, | |
| "acc_stderr,none": 0.023935630169275284, | |
| "acc_norm,none": 0.7669902912621359, | |
| "acc_norm_stderr,none": 0.029526026912337827 | |
| }, | |
| "agieval_sat_en_without_passage": { | |
| "alias": " - agieval_sat_en_without_passage", | |
| "acc,none": 0.5145631067961165, | |
| "acc_stderr,none": 0.034906699050989067, | |
| "acc_norm,none": 0.4320388349514563, | |
| "acc_norm_stderr,none": 0.0345974255383149 | |
| }, | |
| "agieval_sat_math": { | |
| "alias": " - agieval_sat_math", | |
| "acc,none": 0.5727272727272728, | |
| "acc_stderr,none": 0.03342754338309286, | |
| "acc_norm,none": 0.5227272727272727, | |
| "acc_norm_stderr,none": 0.03375194708230163 | |
| } | |
| }, | |
| "groups": { | |
| "agieval": { | |
| "acc,none": 0.5544267053701016, | |
| "acc_stderr,none": 0.004859843455357734, | |
| "alias": "agieval" | |
| } | |
| }, | |
| "group_subtasks": { | |
| "agieval": [ | |
| "agieval_gaokao_biology", | |
| "agieval_gaokao_chemistry", | |
| "agieval_gaokao_chinese", | |
| "agieval_gaokao_geography", | |
| "agieval_gaokao_history", | |
| "agieval_gaokao_mathcloze", | |
| "agieval_gaokao_mathqa", | |
| "agieval_gaokao_physics", | |
| "agieval_jec_qa_ca", | |
| "agieval_jec_qa_kd", | |
| "agieval_logiqa_zh", | |
| "agieval_aqua_rat", | |
| "agieval_gaokao_english", | |
| "agieval_logiqa_en", | |
| "agieval_lsat_ar", | |
| "agieval_lsat_lr", | |
| "agieval_lsat_rc", | |
| "agieval_math", | |
| "agieval_sat_en_without_passage", | |
| "agieval_sat_en", | |
| "agieval_sat_math" | |
| ] | |
| }, | |
| "configs": { | |
| "agieval_aqua_rat": { | |
| "task": "agieval_aqua_rat", | |
| "dataset_path": "hails/agieval-aqua-rat", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_gaokao_biology": { | |
| "task": "agieval_gaokao_biology", | |
| "dataset_path": "hails/agieval-gaokao-biology", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_gaokao_chemistry": { | |
| "task": "agieval_gaokao_chemistry", | |
| "dataset_path": "hails/agieval-gaokao-chemistry", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_gaokao_chinese": { | |
| "task": "agieval_gaokao_chinese", | |
| "dataset_path": "hails/agieval-gaokao-chinese", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_gaokao_english": { | |
| "task": "agieval_gaokao_english", | |
| "dataset_path": "hails/agieval-gaokao-english", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_gaokao_geography": { | |
| "task": "agieval_gaokao_geography", | |
| "dataset_path": "hails/agieval-gaokao-geography", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_gaokao_history": { | |
| "task": "agieval_gaokao_history", | |
| "dataset_path": "hails/agieval-gaokao-history", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_gaokao_mathcloze": { | |
| "task": "agieval_gaokao_mathcloze", | |
| "dataset_path": "hails/agieval-gaokao-mathcloze", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{answer}}", | |
| "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidate = results[0]\n\n gold = doc[\"answer\"]\n\n if not gold:\n print(doc, candidate, gold)\n if is_equiv(candidate, gold):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"acc\": retval,\n }\n return results\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "generate_until", | |
| "generation_kwargs": { | |
| "max_gen_toks": 32, | |
| "do_sample": false, | |
| "temperature": 0.0, | |
| "until": [ | |
| "Q:" | |
| ] | |
| }, | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_gaokao_mathqa": { | |
| "task": "agieval_gaokao_mathqa", | |
| "dataset_path": "hails/agieval-gaokao-mathqa", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_gaokao_physics": { | |
| "task": "agieval_gaokao_physics", | |
| "dataset_path": "hails/agieval-gaokao-physics", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_jec_qa_ca": { | |
| "task": "agieval_jec_qa_ca", | |
| "dataset_path": "hails/agieval-jec-qa-ca", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_jec_qa_kd": { | |
| "task": "agieval_jec_qa_kd", | |
| "dataset_path": "hails/agieval-jec-qa-kd", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_logiqa_en": { | |
| "task": "agieval_logiqa_en", | |
| "dataset_path": "hails/agieval-logiqa-en", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_logiqa_zh": { | |
| "task": "agieval_logiqa_zh", | |
| "dataset_path": "hails/agieval-logiqa-zh", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_lsat_ar": { | |
| "task": "agieval_lsat_ar", | |
| "dataset_path": "hails/agieval-lsat-ar", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_lsat_lr": { | |
| "task": "agieval_lsat_lr", | |
| "dataset_path": "hails/agieval-lsat-lr", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_lsat_rc": { | |
| "task": "agieval_lsat_rc", | |
| "dataset_path": "hails/agieval-lsat-rc", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_math": { | |
| "task": "agieval_math", | |
| "dataset_path": "hails/agieval-math", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{answer}}", | |
| "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidate = results[0]\n\n gold = doc[\"answer\"]\n\n if not gold:\n print(doc, candidate, gold)\n if is_equiv(candidate, gold):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"acc\": retval,\n }\n return results\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "generate_until", | |
| "generation_kwargs": { | |
| "max_gen_toks": 32, | |
| "do_sample": false, | |
| "temperature": 0.0, | |
| "until": [ | |
| "Q:" | |
| ] | |
| }, | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_sat_en": { | |
| "task": "agieval_sat_en", | |
| "dataset_path": "hails/agieval-sat-en", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_sat_en_without_passage": { | |
| "task": "agieval_sat_en_without_passage", | |
| "dataset_path": "hails/agieval-sat-en-without-passage", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
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| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
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| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "agieval_sat_math": { | |
| "task": "agieval_sat_math", | |
| "dataset_path": "hails/agieval-sat-math", | |
| "test_split": "test", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{gold}}", | |
| "doc_to_choice": "{{choices}}", | |
| "process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
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| } | |
| ], | |
| "output_type": "multiple_choice", | |
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| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| } | |
| }, | |
| "versions": { | |
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| "agieval_sat_en_without_passage": 1.0, | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
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| "effective": 999 | |
| }, | |
| "agieval_jec_qa_kd": { | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
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| }, | |
| "agieval_lsat_lr": { | |
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| "effective": 510 | |
| }, | |
| "agieval_lsat_rc": { | |
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| "effective": 269 | |
| }, | |
| "agieval_math": { | |
| "original": 1000, | |
| "effective": 1000 | |
| }, | |
| "agieval_sat_en_without_passage": { | |
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| "effective": 206 | |
| }, | |
| "agieval_sat_en": { | |
| "original": 206, | |
| "effective": 206 | |
| }, | |
| "agieval_sat_math": { | |
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| "effective": 220 | |
| } | |
| }, | |
| "config": { | |
| "model": "vllm", | |
| "model_args": "pretrained=meta-llama/Llama-3.3-70B-Instruct,tensor_parallel_size=4,data_parallel_size=2,gpu_memory_utilization=0.9,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": "150ae04f", | |
| "date": 1737578738.814069, | |
| "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: 4890.87\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 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": "086919bd66f4e15fdcd4b792a7b27a698c1ba091", | |
| "tokenizer_pad_token": [ | |
| "<|finetune_right_pad_id|>", | |
| "128004" | |
| ], | |
| "tokenizer_eos_token": [ | |
| "<|eot_id|>", | |
| "128009" | |
| ], | |
| "tokenizer_bos_token": [ | |
| "<|begin_of_text|>", | |
| "128000" | |
| ], | |
| "eot_token_id": 128009, | |
| "max_length": 131072, | |
| "task_hashes": {}, | |
| "model_source": "vllm", | |
| "model_name": "meta-llama/Llama-3.3-70B-Instruct", | |
| "model_name_sanitized": "meta-llama__Llama-3.3-70B-Instruct", | |
| "system_instruction": null, | |
| "system_instruction_sha": null, | |
| "fewshot_as_multiturn": false, | |
| "chat_template": null, | |
| "chat_template_sha": null, | |
| "start_time": 120759.780132137, | |
| "end_time": 122538.423654986, | |
| "total_evaluation_time_seconds": "1778.6435228490009" | |
| } |