diff --git a/dataset-index.yml b/dataset-index.yml index a3ce995f..57bd924e 100644 --- a/dataset-index.yml +++ b/dataset-index.yml @@ -122,6 +122,18 @@ paper: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10778138 configpath: opencompass/configs/datasets/MedBench/medbench_gen.py configpath_llmjudge: '' +- MedCalc_Bench: + name: MedCalc_Bench + category: Knowledge / Medicine + paper: https://arxiv.org/abs/2406.12036 + configpath: opencompass/configs/datasets/MedCalc_Bench/MedCalcBench_official_gen_a5155f.py + configpath_llmjudge: '' +- MedXpertQA: + name: MedQA + category: Knowledge / Medicine + paper: https://arxiv.org/abs/2009.13081 + configpath: opencompass/configs/datasets/MedQA/MedQA_gen.py + configpath_llmjudge: opencompass/configs/datasets/MedQA/MedQA_llmjudge_gen.py - MedXpertQA: name: MedXpertQA category: Knowledge / Medicine @@ -695,12 +707,6 @@ paper: https://arxiv.org/pdf/2009.03300 configpath: opencompass/configs/datasets/mmlu/mmlu_gen.py configpath_llmjudge: opencompass/configs/datasets/mmlu/mmlu_llm_judge_gen.py -- SciEval: - name: SciEval - category: Understanding - paper: https://arxiv.org/pdf/2308.13149 - configpath: opencompass/configs/datasets/SciEval_lifscience/SciEval_lifscience_gen.py - configpath_llmjudge: opencompass/configs/datasets/SciEval_lifscience/SciEval_lifscience_llm_judge_gen.py - mmlu_cf: name: MMLU-CF category: Understanding @@ -769,6 +775,12 @@ paper: https://arxiv.org/pdf/1911.11641v1 configpath: opencompass/configs/datasets/piqa/piqa_gen.py configpath_llmjudge: '' +- ProteinLMBench: + name: ProteinLMBench + category: Knowledge / Biology (Protein) + paper: https://arxiv.org/abs/2406.05540 + configpath: opencompass/configs/datasets/ProteinLMBench/ProteinLMBench_gen.py + configpath_llmjudge: opencompass/configs/datasets/ProteinLMBench/ProteinLMBench_llmjudge_gen.py - py150: name: py150 category: Code diff --git a/opencompass/configs/datasets/MedCalc_Bench/MedCalcBench_official_gen_a5155f.py b/opencompass/configs/datasets/MedCalc_Bench/MedCalcBench_official_gen_a5155f.py new file mode 100644 index 00000000..74fdff5e --- /dev/null +++ b/opencompass/configs/datasets/MedCalc_Bench/MedCalcBench_official_gen_a5155f.py @@ -0,0 +1,57 @@ +from opencompass.datasets import MedCalc_BenchDataset, MedCalcOfficial_Evaluator +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever + +ZERO_SHOT_PROMPT = 'You are a helpful assistant for calculating a score for a given patient note. Please think step-by-step to solve the question and then generate the required score. Your output should only contain a JSON dict formatted as {"step_by_step_thinking": str(your_step_by_step_thinking_procress_to_solve_the_question), "answer": str(short_and_direct_answer_of_the_question)}. \n Here is the patient note:\n{patient_note}\n\nHere is the task:\n{question}\n\nPlease directly output the JSON dict formatted as {"step_by_step_thinking": str(your_step_by_step_thinking_procress_to_solve_the_question), "answer": str(short_and_direct_answer_of_the_question)}:' +# Reader configuration +reader_cfg = dict( + input_columns=[ + 'row_number', + 'calculator_id', + 'calculator_name', + 'category', + 'note_id', + 'output_type', + 'note_type', + 'patient_note', + 'question', + 'relevant_entities', + 'ground_truth_answer', + 'lower_limit', + 'upper_limit', + 'ground_truth_explanation' + ], + output_column='ground_truth_answer', +) + + +# Inference configuration +infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict(role='HUMAN',prompt=ZERO_SHOT_PROMPT), + ]) + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), +) + +# Evaluation configuration +eval_cfg = dict( + evaluator=dict(type=MedCalcOfficial_Evaluator), + pred_role='BOT', +) +medcal_bench_dataset = dict( + type=MedCalc_BenchDataset, + abbr='medcal_bench_official_zero_shot_eval', + path='ncbi/MedCalc-Bench-v1.0', + prompt_mode='zero-shot', + reader_cfg=reader_cfg, + infer_cfg=infer_cfg, + eval_cfg=eval_cfg, +) + +medcal_bench_datasets = [medcal_bench_dataset] diff --git a/opencompass/configs/datasets/MedQA/MedQA_gen_3bf756.py b/opencompass/configs/datasets/MedQA/MedQA_gen_3bf756.py new file mode 100644 index 00000000..01306134 --- /dev/null +++ b/opencompass/configs/datasets/MedQA/MedQA_gen_3bf756.py @@ -0,0 +1,63 @@ +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.openicl.icl_evaluator import AccEvaluator +from opencompass.utils.text_postprocessors import first_option_postprocess +from opencompass.datasets.MedQA import MedQADataset + + +QUERY_TEMPLATE = """ +Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of Options(e.g. one of ABCDEFGHIJKLMNOP). Think step by step before answering. + +Question:\n +{question} + +Options:\n +{choices} + +""".strip() + + +MedQA_datasets = [] + +MedQA_reader_cfg = dict( + input_columns=['question', 'choices'], + output_column='label', +) + +MedQA_infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict(role='HUMAN', prompt=QUERY_TEMPLATE), + ], + ), + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), +) + +MedQA_subsets = { + 'US': 'xuxuxuxuxu/MedQA_US_test', + 'Mainland': 'xuxuxuxuxu/MedQA_Mainland_test', + 'Taiwan': 'xuxuxuxuxu/MedQA_Taiwan_test', +} + +for split in list(MedQA_subsets.keys()): + + MedQA_eval_cfg = dict( + evaluator=dict(type=AccEvaluator), + pred_postprocessor=dict(type=first_option_postprocess, options='ABCD') + ) + + MedQA_datasets.append( + dict( + abbr=f'MedQA_{split}', + type=MedQADataset, + path=MedQA_subsets[split], + reader_cfg=MedQA_reader_cfg, + infer_cfg=MedQA_infer_cfg, + eval_cfg=MedQA_eval_cfg, + ) + ) diff --git a/opencompass/configs/datasets/MedQA/MedQA_llmjudge_gen_3bf756.py b/opencompass/configs/datasets/MedQA/MedQA_llmjudge_gen_3bf756.py new file mode 100644 index 00000000..d6c19119 --- /dev/null +++ b/opencompass/configs/datasets/MedQA/MedQA_llmjudge_gen_3bf756.py @@ -0,0 +1,108 @@ +from mmengine.config import read_base +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.evaluator import GenericLLMEvaluator +from opencompass.datasets import generic_llmjudge_postprocess +from opencompass.datasets.MedQA import MedQADataset + + +QUERY_TEMPLATE = """ +Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of Options(e.g. one of ABCDEFGHIJKLMNOP). Think step by step before answering. + +Question:\n +{question} + +Options:\n +{choices} + +""".strip() + +GRADER_TEMPLATE = """ + Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly. + + Here are some evaluation criteria: + 1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's answer is consistent with the standard answer according to the form of the question. Don't try to answer the original question. You can assume that the standard answer is definitely correct. + 2. Because the candidate's answer may be different from the standard answer in the form of expression, before making a judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct, but be careful not to try to answer the original question. + 3. Some answers may contain multiple items, such as multiple-choice questions, multiple-select questions, fill-in-the-blank questions, etc. As long as the answer is the same as the standard answer, it is enough. For multiple-select questions and multiple-blank fill-in-the-blank questions, the candidate needs to answer all the corresponding options or blanks correctly to be considered correct. + 4. Some answers may be expressed in different ways, such as some answers may be a mathematical expression, some answers may be a textual description, as long as the meaning expressed is the same. And some formulas are expressed in different ways, but they are equivalent and correct. + + Please judge whether the following answers are consistent with the standard answer based on the above criteria. Grade the predicted answer of this new question as one of: + A: CORRECT + B: INCORRECT + Just return the letters "A" or "B", with no text around it. + + Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer. + + : {question}\n {choices} \n\n\n + : \n{label}\n\n\n + : \n{prediction}\n\n\n + Judging the correctness of candidates' answers: +""".strip() + +MedQA_datasets = [] + +MedQA_reader_cfg = dict( + input_columns=['question', 'choices'], + output_column='label', +) + +MedQA_infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict(role='HUMAN', prompt=QUERY_TEMPLATE), + ], + ), + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), +) + +MedQA_subsets = { + 'US': 'xuxuxuxuxu/MedQA_US_test', + 'Mainland': 'xuxuxuxuxu/MedQA_Mainland_test', + 'Taiwan': 'xuxuxuxuxu/MedQA_Taiwan_test', +} + +for split in list(MedQA_subsets.keys()): + + MedQA_eval_cfg = dict( + evaluator=dict( + type=GenericLLMEvaluator, + prompt_template=dict( + type=PromptTemplate, + template=dict( + begin=[ + dict( + role='SYSTEM', + fallback_role='HUMAN', + prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.", + ) + ], + round=[ + dict(role='HUMAN', prompt=GRADER_TEMPLATE), + ], + ), + ), + dataset_cfg=dict( + type=MedQADataset, + path=MedQA_subsets[split], + reader_cfg=MedQA_reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_postprocess), + ), + ) + + MedQA_datasets.append( + dict( + abbr=f'MedQA_{split}', + type=MedQADataset, + path=MedQA_subsets[split], + reader_cfg=MedQA_reader_cfg, + infer_cfg=MedQA_infer_cfg, + eval_cfg=MedQA_eval_cfg, + ) + ) diff --git a/opencompass/configs/datasets/ProteinLMBench/ProteinLMBench_gen_a67965.py b/opencompass/configs/datasets/ProteinLMBench/ProteinLMBench_gen_a67965.py new file mode 100644 index 00000000..2cf2f220 --- /dev/null +++ b/opencompass/configs/datasets/ProteinLMBench/ProteinLMBench_gen_a67965.py @@ -0,0 +1,46 @@ +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.openicl.icl_evaluator import AccEvaluator +from opencompass.datasets.ProteinLMBench import ProteinLMBenchDataset, ProteinLMBenchEvaluator + +QUERY_TEMPLATE = "Answer the following multiple choice question. There is only one correct answer. The last line of your response should be in the format 'Answer: $LETTER' (without quotes), where LETTER is the letter among {start} through {end}.\n{question}" + + +# Reader configuration +reader_cfg = dict( + input_columns=['question', 'start', 'end', 'options'], + output_column='label', +) + +# Inference configuration +infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict( + role='HUMAN', + prompt=QUERY_TEMPLATE + ) + ], ), + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), +) + +# Evaluation configuration +eval_cfg = dict( + evaluator=dict(type=ProteinLMBenchEvaluator), +) + +proteinlmbench_dataset = dict( + abbr='ProteinLMBench', + type=ProteinLMBenchDataset, + path='tsynbio/ProteinLMBench', + reader_cfg=reader_cfg, + infer_cfg=infer_cfg, + eval_cfg=eval_cfg +) + +proteinlmbench_datasets = [proteinlmbench_dataset] diff --git a/opencompass/configs/datasets/ProteinLMBench/ProteinLMBench_llmjudge_gen_a67965.py b/opencompass/configs/datasets/ProteinLMBench/ProteinLMBench_llmjudge_gen_a67965.py new file mode 100644 index 00000000..5254677e --- /dev/null +++ b/opencompass/configs/datasets/ProteinLMBench/ProteinLMBench_llmjudge_gen_a67965.py @@ -0,0 +1,89 @@ +from mmengine.config import read_base +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.evaluator import GenericLLMEvaluator +from opencompass.datasets import generic_llmjudge_postprocess +from opencompass.datasets.ProteinLMBench import ProteinLMBenchDataset + +QUERY_TEMPLATE = "Answer the following multiple choice question. There is only one correct answer. The last line of your response should be in the format 'Answer: $LETTER' (without quotes), where LETTER is the letter among {start} through {end}.\n{question}" + +GRADER_TEMPLATE = """ + Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly. + + Here are some evaluation criteria: + 1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's answer is consistent with the standard answer according to the form of the question. Don't try to answer the original question. You can assume that the standard answer is definitely correct. + 2. Because the candidate's answer may be different from the standard answer in the form of expression, before making a judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct, but be careful not to try to answer the original question. + 3. Some answers may contain multiple items, such as multiple-choice questions, multiple-select questions, fill-in-the-blank questions, etc. As long as the answer is the same as the standard answer, it is enough. For multiple-select questions and multiple-blank fill-in-the-blank questions, the candidate needs to answer all the corresponding options or blanks correctly to be considered correct. + 4. Some answers may be expressed in different ways, such as some answers may be a mathematical expression, some answers may be a textual description, as long as the meaning expressed is the same. And some formulas are expressed in different ways, but they are equivalent and correct. + + Please judge whether the following answers are consistent with the standard answer based on the above criteria. Grade the predicted answer of this new question as one of: + A: CORRECT + B: INCORRECT + Just return the letters "A" or "B", with no text around it. + + Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer. + + : {question}\n\n\n + : \n{label}\n\n\n + : \n{prediction}\n\n\n + Judging the correctness of candidates' answers: +""".strip() + + +reader_cfg = dict( + input_columns=['question', 'start', 'end', 'options'], + output_column='label', +) + +infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict(role='HUMAN', prompt=QUERY_TEMPLATE), + ], + ), + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), +) + +eval_cfg = dict( + evaluator=dict( + type=GenericLLMEvaluator, + prompt_template=dict( + type=PromptTemplate, + template=dict( + begin=[ + dict( + role='SYSTEM', + fallback_role='HUMAN', + prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.", + ) + ], + round=[ + dict(role='HUMAN', prompt=GRADER_TEMPLATE), + ], + ), + ), + dataset_cfg=dict( + type=ProteinLMBenchDataset, + path='tsynbio/ProteinLMBench', + reader_cfg=reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_postprocess), + ), +) + +proteinlmbench_dataset = dict( + abbr='ProteinLMBench', + type=ProteinLMBenchDataset, + path='tsynbio/ProteinLMBench', + reader_cfg=reader_cfg, + infer_cfg=infer_cfg, + eval_cfg=eval_cfg +) + +proteinlmbench_datasets = [proteinlmbench_dataset] diff --git a/opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_0shot_gen_4043d4.py b/opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_0shot_gen_4043d4.py deleted file mode 100644 index 5381abcf..00000000 --- a/opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_0shot_gen_4043d4.py +++ /dev/null @@ -1,61 +0,0 @@ -from opencompass.openicl.icl_prompt_template import PromptTemplate -from opencompass.openicl.icl_retriever import FixKRetriever -from opencompass.openicl.icl_inferencer import GenInferencer -from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator -from opencompass.utils.text_postprocessors import first_option_postprocess -from opencompass.datasets import SciEvalDataset # 你自己实现的类 - -# 只评测 biology + multiple-choice 的 test split -_hint = ('Given a question and four options, please select the right answer. ' - "Your answer should be 'A', 'B', 'C' or 'D'.") - -scieval_reader_cfg = dict( - input_columns=['input', 'A', 'B', 'C', 'D'], - output_column='target', - train_split='test', -) - -scieval_infer_cfg = dict( - ice_template=dict( - type=PromptTemplate, - template=dict(round=[ - dict( - role='HUMAN', - prompt=f'{_hint}\nQuestion: {{input}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nAnswer: ' - ), - dict(role='BOT', prompt='{target}\n') - ]), - ), - prompt_template=dict( - type=PromptTemplate, - template=dict( - begin='', - round=[ - dict( - role='HUMAN', - prompt=f'{_hint}\nQuestion: {{input}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nAnswer: ' - ), - ], - ), - ice_token='', - ), - retriever=dict(type=FixKRetriever, fix_id_list=[0, 1, 2, 3, 4]), - inferencer=dict(type=GenInferencer), -) - -scieval_eval_cfg = dict( - evaluator=dict(type=AccwithDetailsEvaluator), - pred_postprocessor=dict(type=first_option_postprocess, options='ABCD'), -) - -scieval_datasets = [ - dict( - abbr='scieval_biology', - type=SciEvalDataset, - path='OpenDFM/SciEval', - name='default', - reader_cfg=scieval_reader_cfg, - infer_cfg=scieval_infer_cfg, - eval_cfg=scieval_eval_cfg, - ) -] diff --git a/opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_0shot_llmjudge_gen_012dd1.py b/opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_0shot_llmjudge_gen_012dd1.py deleted file mode 100644 index 26af5cd3..00000000 --- a/opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_0shot_llmjudge_gen_012dd1.py +++ /dev/null @@ -1,125 +0,0 @@ -# SciEval_lifescience_llmjudge_gen.py - -from mmengine.config import read_base -from opencompass.openicl.icl_prompt_template import PromptTemplate -from opencompass.openicl.icl_retriever import ZeroRetriever -from opencompass.openicl.icl_inferencer import GenInferencer -from opencompass.utils.text_postprocessors import match_answer_pattern -from opencompass.evaluator import GenericLLMEvaluator -from opencompass.datasets import generic_llmjudge_postprocess -from opencompass.datasets import SciEvalDataset - -with read_base(): - from .SciEval_lifescience_sets import SciEval_lifescience_subsets - -QUERY_TEMPLATE = """ -Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of ABCD. - -{input} - -A) {A} -B) {B} -C) {C} -D) {D} -""".strip() - -GRADER_TEMPLATE = """ -Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly. - -Here are some evaluation criteria: -1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's answer is consistent with the standard answer according to the form of the question. Don't try to answer the original question. You can assume that the standard answer is definitely correct. -2. Because the candidate's answer may be different from the standard answer in the form of expression, before making a judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct, but be careful not to try to answer the original question. -3. Some answers may contain multiple items, such as multiple-choice questions, multiple-select questions, fill-in-the-blank questions, etc. As long as the answer is the same as the standard answer, it is enough. For multiple-select questions and multiple-blank fill-in-the-blank questions, the candidate needs to answer all the corresponding options or blanks correctly to be considered correct. -4. Some answers may be expressed in different ways, such as some answers may be a mathematical expression, some answers may be a textual description, as long as the meaning expressed is the same. And some formulas are expressed in different ways, but they are equivalent and correct. - -Please judge whether the following answers are consistent with the standard answer based on the above criteria. Grade the predicted answer of this new question as one of: -A: CORRECT -B: INCORRECT -Just return the letters "A" or "B", with no text around it. - -Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer. - -: {input} -A) {A} -B) {B} -C) {C} -D) {D} - - -: -{target} - - -: -{prediction} - - -Judging the correctness of candidates' answers: -""".strip() - -scieval_reader_cfg = dict( - input_columns=['input', 'A', 'B', 'C', 'D'], - output_column='target', - train_split='test', -) - -scieval_datasets = [] -for name in SciEval_lifescience_subsets: - scieval_infer_cfg = dict( - prompt_template=dict( - type=PromptTemplate, - template=dict( - round=[ - dict(role='HUMAN', prompt=QUERY_TEMPLATE), - ] - ) - ), - retriever=dict(type=ZeroRetriever), - inferencer=dict(type=GenInferencer), - ) - - scieval_eval_cfg = dict( - evaluator=dict( - type=GenericLLMEvaluator, - prompt_template=dict( - type=PromptTemplate, - template=dict( - begin=[ - dict( - role='SYSTEM', - fallback_role='HUMAN', - prompt=( - 'You are a helpful assistant who evaluates the correctness ' - "and quality of models' outputs." - ), - ) - ], - round=[ - dict(role='HUMAN', prompt=GRADER_TEMPLATE), - ], - ), - ), - dataset_cfg=dict( - type=SciEvalDataset, - path='OpenDFM/SciEval', - name='default', - reader_cfg=scieval_reader_cfg, - ), - judge_cfg=dict(), - dict_postprocessor=dict(type=generic_llmjudge_postprocess), - ), - pred_role='BOT', - ) - - scieval_datasets.append( - dict( - abbr=f'scieval_lifescience_{name}_llmjudge', - type=SciEvalDataset, - path='OpenDFM/SciEval', - name='default', - reader_cfg=scieval_reader_cfg, - infer_cfg=scieval_infer_cfg, - eval_cfg=scieval_eval_cfg, - mode='singlescore', - ) - ) diff --git a/opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_sets.py b/opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_sets.py deleted file mode 100644 index 8d0a0a83..00000000 --- a/opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_sets.py +++ /dev/null @@ -1,3 +0,0 @@ -SciEval_lifescience_subsets = [ - 'biology', # 大学生物学 -] diff --git a/opencompass/configs/datasets/mmlu_pro/mmlu_pro_biomed_0shot_cot_gen_057927.py b/opencompass/configs/datasets/mmlu_pro/mmlu_pro_biomed_0shot_cot_gen_057927.py new file mode 100644 index 00000000..02766491 --- /dev/null +++ b/opencompass/configs/datasets/mmlu_pro/mmlu_pro_biomed_0shot_cot_gen_057927.py @@ -0,0 +1,60 @@ +from mmengine.config import read_base +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.openicl.icl_evaluator import AccEvaluator +from opencompass.datasets import MMLUProDataset +from opencompass.utils.text_postprocessors import match_answer_pattern + +categories = [ + 'health', +] + +QUERY_TEMPLATE = """ +Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of Options(e.g. one of ABCDEFGHIJKLMNOP). Think step by step before answering. +Question:\n +{question} +Options:\n +{options_str} +""".strip() + +mmlu_pro_datasets = [] + +for category in categories: + mmlu_pro_reader_cfg = dict( + input_columns=['question', 'cot_content', 'options_str'], + output_column='answer', + train_split='validation', + test_split='test', + ) + mmlu_pro_infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict(role='HUMAN', + prompt=QUERY_TEMPLATE), + ], + ), + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), + ) + + mmlu_pro_eval_cfg = dict( + evaluator=dict(type=AccEvaluator), + pred_postprocessor=dict( + type=match_answer_pattern, + answer_pattern=r'(?i)ANSWER\s*:\s*([A-P])') + ) + + mmlu_pro_datasets.append( + dict( + abbr=f'mmlu_pro_{category.replace(" ", "_")}', + type=MMLUProDataset, + path='opencompass/mmlu_pro', + category=category, + reader_cfg=mmlu_pro_reader_cfg, + infer_cfg=mmlu_pro_infer_cfg, + eval_cfg=mmlu_pro_eval_cfg, + )) \ No newline at end of file diff --git a/opencompass/configs/datasets/mmlu_pro/mmlu_pro_biomed_0shot_nocot_genericllmeval_gen_057927.py b/opencompass/configs/datasets/mmlu_pro/mmlu_pro_biomed_0shot_nocot_genericllmeval_gen_057927.py new file mode 100644 index 00000000..58cd20b1 --- /dev/null +++ b/opencompass/configs/datasets/mmlu_pro/mmlu_pro_biomed_0shot_nocot_genericllmeval_gen_057927.py @@ -0,0 +1,101 @@ +from mmengine.config import read_base +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.evaluator import GenericLLMEvaluator +from opencompass.datasets import MMLUProDataset, generic_llmjudge_postprocess + +categories = [ + 'health', +] + + +QUERY_TEMPLATE = """ +Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: $LETTER' (without quotes) where LETTER is one of Options(e.g. one of ABCDEFGHIJKLMNOP). Think step by step before answering. +Question:\n +{question} +Options:\n +{options_str} +""".strip() + +GRADER_TEMPLATE = """ + Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly. + + Here are some evaluation criteria: + 1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's answer is consistent with the standard answer according to the form of the question. Don't try to answer the original question. You can assume that the standard answer is definitely correct. + 2. Because the candidate's answer may be different from the standard answer in the form of expression, before making a judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct, but be careful not to try to answer the original question. + 3. Some answers may contain multiple items, such as multiple-choice questions, multiple-select questions, fill-in-the-blank questions, etc. As long as the answer is the same as the standard answer, it is enough. For multiple-select questions and multiple-blank fill-in-the-blank questions, the candidate needs to answer all the corresponding options or blanks correctly to be considered correct. + 4. Some answers may be expressed in different ways, such as some answers may be a mathematical expression, some answers may be a textual description, as long as the meaning expressed is the same. And some formulas are expressed in different ways, but they are equivalent and correct. + Please judge whether the following answers are consistent with the standard answer based on the above criteria. Grade the predicted answer of this new question as one of: + A: CORRECT + B: INCORRECT + Just return the letters "A" or "B", with no text around it. + Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer. + : {question}\n {options_str} \n\n\n + : \n{answer}\n\n\n + : \n{prediction}\n\n\n + Judging the correctness of candidates' answers: +""".strip() + +mmlu_pro_datasets = [] + +for category in categories: + mmlu_pro_reader_cfg = dict( + input_columns=['question', 'cot_content', 'options_str'], + output_column='answer', + train_split='validation', + test_split='test', + ) + mmlu_pro_infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict(role='HUMAN', prompt=QUERY_TEMPLATE), + ], + ), + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), + ) + + mmlu_pro_eval_cfg = dict( + evaluator=dict( + type=GenericLLMEvaluator, + prompt_template=dict( + type=PromptTemplate, + template=dict( + begin=[ + dict( + role='SYSTEM', + fallback_role='HUMAN', + prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.", + ) + ], + round=[ + dict(role='HUMAN', prompt=GRADER_TEMPLATE), + ], + ), + ), + dataset_cfg=dict( + type=MMLUProDataset, + path='opencompass/mmlu_pro', + category=category, + reader_cfg=mmlu_pro_reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_postprocess), + ), + ) + + mmlu_pro_datasets.append( + dict( + abbr=f'mmlu_pro_{category.replace(" ", "_")}', + type=MMLUProDataset, + path='opencompass/mmlu_pro', + category=category, + reader_cfg=mmlu_pro_reader_cfg, + infer_cfg=mmlu_pro_infer_cfg, + eval_cfg=mmlu_pro_eval_cfg, + ) + ) \ No newline at end of file diff --git a/opencompass/configs/models/baichuan/hf_baichuan_m1_14b_base.py b/opencompass/configs/models/baichuan/hf_baichuan_m1_14b_base.py new file mode 100644 index 00000000..e5b59bfb --- /dev/null +++ b/opencompass/configs/models/baichuan/hf_baichuan_m1_14b_base.py @@ -0,0 +1,14 @@ +import torch +from opencompass.models import HuggingFaceBaseModel + +models = [ + dict( + type=HuggingFaceBaseModel, + abbr='baichuan-m1-14b-base-hf', + path='baichuan-inc/Baichuan-M1-14B-Base', + max_out_len=1024, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True, torch_dtype=torch.bfloat16), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/baichuan/hf_baichuan_m1_14b_instruct.py b/opencompass/configs/models/baichuan/hf_baichuan_m1_14b_instruct.py new file mode 100644 index 00000000..b90f39fb --- /dev/null +++ b/opencompass/configs/models/baichuan/hf_baichuan_m1_14b_instruct.py @@ -0,0 +1,14 @@ +import torch +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='baichuan-m1-14b-instruct-hf', + path='baichuan-inc/Baichuan-M1-14B-Instruct', + max_out_len=2048, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True, torch_dtype=torch.bfloat16), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/huatuogpt/hf_huatuogpt2_13b.py b/opencompass/configs/models/huatuogpt/hf_huatuogpt2_13b.py new file mode 100644 index 00000000..d5ffbf6e --- /dev/null +++ b/opencompass/configs/models/huatuogpt/hf_huatuogpt2_13b.py @@ -0,0 +1,17 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='huatuogpt2-13b-hf', + path='FreedomIntelligence/HuatuoGPT2-13B', + tokenizer_kwargs=dict(padding_side='left', + truncation_side='left', + trust_remote_code=True, + use_fast=True,), + max_out_len=1024, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/huatuogpt/hf_huatuogpt2_7b.py b/opencompass/configs/models/huatuogpt/hf_huatuogpt2_7b.py new file mode 100644 index 00000000..98d29ad2 --- /dev/null +++ b/opencompass/configs/models/huatuogpt/hf_huatuogpt2_7b.py @@ -0,0 +1,13 @@ +from opencompass.models import HuggingFacewithChatTemplate + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='huatuogpt2-7b-hf', + path='FreedomIntelligence/HuatuoGPT2-7B', + max_out_len=1024, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1), + ) +] diff --git a/opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_7b.py b/opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_7b.py new file mode 100644 index 00000000..db1130e1 --- /dev/null +++ b/opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_7b.py @@ -0,0 +1,15 @@ +from opencompass.models import HuggingFacewithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='huatuogpt-o1-7b-hf', + path='FreedomIntelligence/HuatuoGPT-o1-7B', + max_out_len=2048, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1), + pred_postprocessor=dict(type=extract_non_reasoning_content, think_start_token='## Thinking', think_end_token='## Final Response'), + ) +] diff --git a/opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_8b.py b/opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_8b.py new file mode 100644 index 00000000..ba2e2c1d --- /dev/null +++ b/opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_8b.py @@ -0,0 +1,15 @@ +from opencompass.models import HuggingFacewithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=HuggingFacewithChatTemplate, + abbr='huatuogpt-o1-8b-hf', + path='FreedomIntelligence/HuatuoGPT-o1-8B', + max_out_len=2048, + batch_size=8, + model_kwargs=dict(device_map='auto', trust_remote_code=True), + run_cfg=dict(num_gpus=1), + pred_postprocessor=dict(type=extract_non_reasoning_content, think_start_token='## Thinking', think_end_token='## Final Response'), + ) +] diff --git a/opencompass/datasets/MedCalc_Bench.py b/opencompass/datasets/MedCalc_Bench.py new file mode 100644 index 00000000..66855d5c --- /dev/null +++ b/opencompass/datasets/MedCalc_Bench.py @@ -0,0 +1,323 @@ +import math +import re +from datetime import datetime + +import numpy as np +from datasets import load_dataset + +from opencompass.openicl import BaseEvaluator +from opencompass.registry import LOAD_DATASET + +from .base import BaseDataset + + +def check_correctness(answer: str, ground_truth, calid, upper_limit, + lower_limit): + """""" + calid = int(calid) + + if calid in [13, 68]: + # Output Type: date + + if datetime.strptime( + answer, + '%m/%d/%Y').strftime('%-m/%-d/%Y') == datetime.strptime( + ground_truth, '%m/%d/%Y').strftime('%-m/%-d/%Y'): + correctness = 1 + else: + correctness = 0 + elif calid in [69]: + # Output Type: integer (A, B) + match = re.search( + r"\(?[\"\']?(\d+)\s*(weeks?)?[\"\']?,?" + r"\s*[\"\']?(\d+)\s*(days?)?[\"\']?\s*\)?", ground_truth) + ground_truth = f'({match.group(1)}, {match.group(3)})' + match = re.search( + r"\(?[\"\']?(\d+)\s*(weeks?)?[\"\']?,?" + r"\s*[\"\']?(\d+)\s*(days?)?[\"\']?\s*\)?", answer) + if match: + weeks = match.group(1) + days = match.group(3) + answer = f'({weeks}, {days})' + if eval(answer) == eval(ground_truth): + correctness = 1 + else: + correctness = 0 + else: + correctness = 0 + elif calid in [ + 4, 15, 16, 17, 18, 20, 21, 25, 27, 28, 29, 32, 33, 36, 43, 45, 48, + 51, 69 + ]: + # Output Type: integer A + answer = round(eval(answer)) + if answer == eval(ground_truth): + correctness = 1 + else: + correctness = 0 + elif calid in [ + 2, 3, 5, 6, 7, 8, 9, 10, 11, 19, 22, 23, 24, 26, 30, 31, 38, 39, + 40, 44, 46, 49, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67 + ]: + # Output Type: decimal + answer = eval(answer) + if answer >= eval(lower_limit) and answer <= eval(upper_limit): + correctness = 1 + else: + correctness = 0 + else: + raise ValueError(f'Unknown calculator ID: {calid}') + return correctness + + +def extract_answer(answer, calid): + + calid = int(calid) + extracted_answer = re.findall(r'[Aa]nswer":\s*(.*?)\}', answer) + matches = re.findall( + r'"step_by_step_thinking":\s*"' + r'([^"]+)"\s*,\s*"[Aa]nswer"', answer) + + if matches: + # Select the last match + last_match = matches[-1] + explanation = last_match + else: + explanation = 'No Explanation' + + if len(extracted_answer) == 0: + extracted_answer = 'Not Found' + else: + extracted_answer = extracted_answer[-1].strip().strip('"') + if extracted_answer == 'str(short_and_direct\ + _answer_of_the_question)': + extracted_answer = 'Not Found' + if extracted_answer == 'str(value which is\ + the answer to the question)': + extracted_answer = 'Not Found' + if extracted_answer == 'X.XX': + extracted_answer = 'Not Found' + + if calid in [13, 68]: + # Output Type: date + match = re.search( + r'^(0?[1-9]|1[0-2])\/(0?[1-9]' + r'|[12][0-9]|3[01])\/(\d{4})', extracted_answer) + if match: + month = int(match.group(1)) + day = int(match.group(2)) + year = match.group(3) + answer = f'{month:02}/{day:02}/{year}' + else: + answer = 'N/A' + + elif calid in [69]: + # Output Type: integer (A, B) + match = re.search( + r"\(?[\"\']?(\d+)\s*(weeks?)?[\"\']?," + r"\?\s*[\"\']?(\d+)\s*(days?)?[\"\']?\s*\)?", extracted_answer) + extracted_answer = extracted_answer.replace('[', '(').replace( + ']', ')').replace("'", '').replace('"', '') + match = re.search( + r"\(?[\"\']?(\d+)\s*(weeks?)?[\"\']?," + r"?\s*[\"\']?(\d+)\s*(days?)?[\"\']?\s*\)?", extracted_answer) + if match: + weeks = match.group(1) + days = match.group(3) + answer = f'({weeks}, {days})' + else: + answer = 'N/A' + elif calid in [ + 4, 15, 16, 17, 18, 20, 21, 25, 27, 28, 29, 32, 33, 36, 43, 45, 48, + 51, 69 + ]: + # Output Type: integer A + match = re.search(r'(\d+) out of', extracted_answer) + if match: # cases like "3 out of 5" + answer = match.group(1) + else: + match = re.search(r'-?\d+(, ?-?\d+)+', extracted_answer) + if match: # cases like "3, 4, 5" + answer = str(len(match.group(0).split(','))) + else: + # match = re.findall(r"(? 0: # find the last integer + answer = match[-1][0] + # answer = match[-1].lstrip("0") + else: + answer = 'N/A' + elif calid in [ + 2, 3, 5, 6, 7, 8, 9, 10, 11, 19, 22, 23, 24, 26, 30, 31, 38, 39, + 40, 44, 46, 49, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67 + ]: + # Output Type: decimal + match = re.search(r'str\((.*)\)', extracted_answer) + if match: + expression = match.group(1).replace('^', '**').replace( + 'is odd', '% 2 == 1').replace('is even', '% 2 == 0').replace( + 'sqrt', 'math.sqrt').replace('.math', '').replace( + 'weight', + '').replace('height', '').replace('mg/dl', '').replace( + 'g/dl', '').replace('mmol/L', '').replace( + 'kg', '').replace('g', + '').replace('mEq/L', '') + expression = expression.split('#')[0] + if expression.count('(') > expression.count(')'): # add missing ') + expression += ')' * (expression.count('(') - + expression.count(')')) + elif expression.count(')') > expression.count( + '('): # add missing ( + expression = '(' * (expression.count(')') - + expression.count('(')) + expression + try: + answer = eval(expression, {'__builtins__': None}, { + 'min': min, + 'pow': pow, + 'round': round, + 'abs': abs, + 'int': int, + 'float': float, + 'math': math, + 'np': np, + 'numpy': np + }) + except Exception: + print(f'Error in evaluating expression: {expression}') + answer = 'N/A' + else: + match = re.search(r'(-?\d+(\.\d+)?)\s*mL/min/1.73', + extracted_answer) + if match: # cases like "8.1 mL/min/1.73 m\u00b2" + answer = eval(match.group(1)) + else: + match = re.findall(r'(-?\d+(\.\d+)?)\%', extracted_answer) + if len(match) > 0: # cases like "53.1%" + answer = eval(match[-1][0]) / 100 + else: + match = re.findall(r'(-?\d+(\.\d+)?)', extracted_answer) + if len( + match + ) > 0: # cases like "8.1 mL/min/1.73 m\u00b2" or "11.1" + answer = eval(match[-1][0]) + else: + answer = 'N/A' + if answer != 'N/A': + answer = str(answer) + + return answer, explanation + + +def _parse(item, prompt_mode): + item['row_number'] = item['Row Number'] + item['calculator_id'] = item['Calculator ID'] + item['calculator_name'] = item['Calculator Name'] + item['category'] = item['Category'] + item['output_type'] = item['Output Type'] + item['note_id'] = item['Note ID'] + item['note_type'] = item['Note Type'] + item['patient_note'] = item['Patient Note'] + item['question'] = item['Question'] + item['relevant_entities'] = item['Relevant Entities'] + item['ground_truth_answer'] = item['Ground Truth Answer'] + item['lower_limit'] = item['Lower Limit'] + item['upper_limit'] = item['Upper Limit'] + item['ground_truth_explanation'] = item['Ground Truth Explanation'] + return item + + +@LOAD_DATASET.register_module() +class MedCalc_BenchDataset(BaseDataset): + + @staticmethod + def load(path: str, prompt_mode: str, **kwargs): + data_files = { + 'test': 'data/test-00000-of-00001.parquet', + 'train': 'data/train-00000-of-00001.parquet' + } + dataset = load_dataset(path, data_files=data_files, split='test') + # dataset = dataset.select(range(2)) + if prompt_mode == 'zero-shot': + dataset = dataset.map(lambda item: _parse(item, prompt_mode), + load_from_cache_file=False) + elif prompt_mode == 'few-shot': + pass # TODO: Implement few-shot prompt + return dataset + + +class MedCalcOfficial_Evaluator(BaseEvaluator): + + def score(self, predictions, references, test_set): + + if len(predictions) != len(references): + return {'error': 'preds and refrs have different length'} + + correct = 0 + count = 0 + details = [] + for idx, (i, j) in enumerate(zip(predictions, references)): + calculator_id = test_set['calculator_id'][idx] + lower_limit = test_set['lower_limit'][idx] + upper_limit = test_set['upper_limit'][idx] + row_number = test_set['row_number'][idx] + note_id = test_set['note_id'][idx] + category = test_set['category'][idx] + question = test_set['question'][idx] + calculator_name = test_set['calculator_name'][idx] + patient_note = test_set['patient_note'][idx] + ground_truth_explanation = test_set['ground_truth_explanation'][ + idx] + ground_truth_answer = test_set['ground_truth_answer'][idx] + try: + answer_value, explanation = extract_answer( + i, int(calculator_id)) + + print(answer_value) + print(explanation) + + correctness = check_correctness(answer_value, + ground_truth_answer, + calculator_id, upper_limit, + lower_limit) + + status = 'Correct' if correctness else 'Incorrect' + + outputs = { + 'Row Number': int(row_number), + 'Calculator Name': calculator_name, + 'Calculator ID': calculator_id, + 'Category': category, + 'Note ID': note_id, + 'Patient Note': patient_note, + 'Question': question, + 'LLM Answer': answer_value, + 'LLM Explanation': explanation, + 'Ground Truth Answer': ground_truth_answer, + 'Ground Truth Explanation': ground_truth_explanation, + 'Result': status + } + + except Exception as e: + outputs = { + 'Row Number': int(row_number), + 'Calculator Name': calculator_name, + 'Calculator ID': calculator_id, + 'Category': category, + 'Note ID': note_id, + 'Patient Note': patient_note, + 'Question': question, + 'LLM Answer': str(e), + 'LLM Explanation': str(e), + 'Ground Truth Answer': ground_truth_answer, + 'Ground Truth Explanation': ground_truth_explanation, + 'Result': 'Incorrect' + } + status = 'Incorrect' + count += 1 + if status == 'Correct': + correct += 1 + details.append(outputs) + + result = {'accuracy': 100 * correct / count, 'details': details} + return result diff --git a/opencompass/datasets/MedQA.py b/opencompass/datasets/MedQA.py new file mode 100644 index 00000000..256f9910 --- /dev/null +++ b/opencompass/datasets/MedQA.py @@ -0,0 +1,29 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET + +from .base import BaseDataset + + +@LOAD_DATASET.register_module() +class MedQADataset(BaseDataset): + + @staticmethod + def load_single(path): + dataset = [] + ds = load_dataset(path) + for data in ds['train']: + data['label'] = data['answer_idx'] + choices = '' + for option in data['options']: + choices += option + '. ' + data['options'][option] + '\n' + data['choices'] = choices + + dataset.append(data) + + return Dataset.from_list(dataset) + + @staticmethod + def load(path): + dataset = MedQADataset.load_single(path) + return dataset diff --git a/opencompass/datasets/ProteinLMBench.py b/opencompass/datasets/ProteinLMBench.py new file mode 100644 index 00000000..bebaadfd --- /dev/null +++ b/opencompass/datasets/ProteinLMBench.py @@ -0,0 +1,58 @@ +from datasets import load_dataset + +from opencompass.openicl import BaseEvaluator +from opencompass.registry import LOAD_DATASET +from opencompass.utils.text_postprocessors import first_option_postprocess + +from .base import BaseDataset + + +def _parse(item): + item['start'] = chr(65) + item['end'] = chr(65 + len(item.get('options', [])) - 1) + new_options = [] + choices = '' + for i in range(len(item['options'])): + new_options.append(item['options'][i].split(': ')[-1]) + choices += chr(65 + + i) + '. ' + item['options'][i].split(': ')[-1] + '\n' + item['question'] = (f'\nQuestion: {item["question"]}\n' + f'Answer Choices: \n{choices}') + item['options'] = new_options + item['label'] = chr(65 + int(item['answer'].split(' ')[-1]) - + 1) # Index from 1 in answer + return item + + +@LOAD_DATASET.register_module() +class ProteinLMBenchDataset(BaseDataset): + + @staticmethod + def load(path: str, **kwargs): + dataset = load_dataset(path, 'evaluation', split='train') + dataset = dataset.map(lambda item: _parse(item)) + + return dataset + + +class ProteinLMBenchEvaluator(BaseEvaluator): + + def score(self, predictions, references, test_set): + if len(predictions) != len(references): + return {'error': 'preds and refrs have different length'} + correct = 0 + count = 0 + details = [] + for idx, (prediction, + reference) in enumerate(zip(predictions, references)): + options = ''.join( + [chr(65 + i) for i in range(len(test_set['options'][idx]))]) + predict = first_option_postprocess(prediction, options) + detail = {'pred': predict, 'answer': reference, 'correct': False} + count += 1 + if predict == reference: + correct += 1 + detail['correct'] = True + details.append(detail) + result = {'accuracy': 100 * correct / count, 'details': details} + return result diff --git a/opencompass/datasets/SciEval_lifescience.py b/opencompass/datasets/SciEval_lifescience.py deleted file mode 100644 index af93e496..00000000 --- a/opencompass/datasets/SciEval_lifescience.py +++ /dev/null @@ -1,62 +0,0 @@ -import re -from typing import List - -from datasets import Dataset, DatasetDict, load_dataset - -from opencompass.datasets.base import BaseDataset -from opencompass.registry import LOAD_DATASET - -# 预编译的多选题正则，按 PEP-8 每行 < 79 字符 -_PATTERN_MC = ( - r'^(?P.*?)' # 题干 - r'(?:A\.)\s*(?P.*?)\s*' # 选项 A - r'B\.\s*(?P.*?)\s*' # 选项 B - r'C\.\s*(?P.*?)\s*' # 选项 C - r'D\.\s*(?P.*?)' # 选项 D - r'Answer:' # 答案分隔符 -) - - -@LOAD_DATASET.register_module() -class SciEvalDataset(BaseDataset): - """Biology multiple-choice subset of SciEval.""" - - @staticmethod - def load(path: str, name: str, **kwargs) -> DatasetDict: - dataset = DatasetDict() - - for split in ('test', ): - raw_iter = load_dataset( - path, - name=name, - split=split, - streaming=True, - ) - - examples: List[dict] = [] - for ex in raw_iter: - if (ex.get('category') != 'biology' - or ex.get('type') != 'multiple-choice'): - continue - - ans_list = ex.get('answer') or ex.get('answers') or [] - if not ans_list: - continue - target = ans_list[0] - - match = re.search(_PATTERN_MC, ex.get('question', ''), re.S) - if not match: - continue - - examples.append({ - 'input': match.group('stem').strip(), - 'A': match.group('A').strip(), - 'B': match.group('B').strip(), - 'C': match.group('C').strip(), - 'D': match.group('D').strip(), - 'target': target, - }) - - dataset[split] = Dataset.from_list(examples) - - return dataset diff --git a/opencompass/datasets/__init__.py b/opencompass/datasets/__init__.py index ef3f9199..c441a2d8 100644 --- a/opencompass/datasets/__init__.py +++ b/opencompass/datasets/__init__.py @@ -97,6 +97,9 @@ from .math_intern import * # noqa: F401, F403 from .mathbench import * # noqa: F401, F403 from .mbpp import * # noqa: F401, F403 from .medbench import * # noqa: F401, F403 +from .MedCalc_Bench import MedCalc_BenchDataset # noqa: F401 +from .MedCalc_Bench import MedCalcOfficial_Evaluator # noqa: F401 +from .MedQA import * # noqa: F401, F403 from .MedXpertQA import * # noqa: F401, F403 from .mgsm import * # noqa: F401, F403 from .mmlu import * # noqa: F401, F403 @@ -118,6 +121,7 @@ from .OlympiadBench import * # noqa: F401, F403 from .OpenFinData import * # noqa: F401, F403 from .physics import * # noqa: F401, F403 from .piqa import * # noqa: F401, F403 +from .ProteinLMBench import * # noqa: F401, F403 from .py150 import * # noqa: F401, F403 from .qasper import * # noqa: F401, F403 from .qaspercut import * # noqa: F401, F403 @@ -130,9 +134,7 @@ from .ruler import * # noqa: F401, F403 from .safety import * # noqa: F401, F403 from .scibench import ScibenchDataset, scibench_postprocess # noqa: F401, F403 from .scicode import * # noqa: F401, F403 -from .SciKnowEval import SciKnowEvalDataset # noqa: F401, F403 -from .SciKnowEval import SciKnowEvalEvaluator # noqa: F401, F403 -from .SciEval_lifescience import SciEvalDataset # noqa: F401 +from .SciKnowEval import * # noqa: F401, F403 from .simpleqa import * # noqa: F401, F403 from .siqa import * # noqa: F401, F403 from .smolinstruct import * # noqa: F401, F403 diff --git a/opencompass/datasets/base.py b/opencompass/datasets/base.py index ac6c4570..1ccbe9fd 100644 --- a/opencompass/datasets/base.py +++ b/opencompass/datasets/base.py @@ -23,7 +23,8 @@ class BaseDataset: 'idx': idx }, with_indices=True, - writer_batch_size=16) + writer_batch_size=16, + load_from_cache_file=False) dataset = concatenate_datasets([dataset] * n) self.dataset = dataset else: @@ -34,7 +35,8 @@ class BaseDataset: 'idx': idx }, with_indices=True, - writer_batch_size=16) + writer_batch_size=16, + load_from_cache_file=False) dataset[key] = concatenate_datasets([dataset[key]] * n) self.dataset[key] = dataset[key] self._init_reader(**reader_cfg)