From 9ec23c145b4c0ac441b51d6e326e2074975b2400 Mon Sep 17 00:00:00 2001 From: Jiahao Xu <48542487+xuxuxuxuxuxjh@users.noreply.github.com> Date: Thu, 8 May 2025 16:25:43 +0800 Subject: [PATCH 01/10] [Datasets] Add ClinicBench, PubMedQA and ScienceQA (#2061) * Add ClinicBench * Add PubMedQA & ScienceQA & ClinicBench * Add PubMedQA & ScienceQA & ClinicBench * Update datasets_info & hf_path * Update hf_path --- dataset-index.yml | 18 ++++ .../ClinicBench/ClinicBench_llmjudge_gen.py | 4 + .../ClinicBench_llmjudge_gen_d09668.py | 100 ++++++++++++++++++ .../PubMedQA/PubMedQA_llmjudge_gen.py | 4 + .../PubMedQA/PubMedQA_llmjudge_gen_f00302.py | 94 ++++++++++++++++ .../ScienceQA/ScienceQA_llmjudge_gen.py | 4 + .../ScienceQA_llmjudge_gen_f00302.py | 94 ++++++++++++++++ opencompass/datasets/ClinicBench.py | 19 ++++ opencompass/datasets/PubMedQA.py | 34 ++++++ opencompass/datasets/ScienceQA.py | 32 ++++++ 10 files changed, 403 insertions(+) create mode 100644 opencompass/configs/datasets/ClinicBench/ClinicBench_llmjudge_gen.py create mode 100644 opencompass/configs/datasets/ClinicBench/ClinicBench_llmjudge_gen_d09668.py create mode 100644 opencompass/configs/datasets/PubMedQA/PubMedQA_llmjudge_gen.py create mode 100644 opencompass/configs/datasets/PubMedQA/PubMedQA_llmjudge_gen_f00302.py create mode 100644 opencompass/configs/datasets/ScienceQA/ScienceQA_llmjudge_gen.py create mode 100644 opencompass/configs/datasets/ScienceQA/ScienceQA_llmjudge_gen_f00302.py create mode 100644 opencompass/datasets/ClinicBench.py create mode 100644 opencompass/datasets/PubMedQA.py create mode 100644 opencompass/datasets/ScienceQA.py diff --git a/dataset-index.yml b/dataset-index.yml index 4a920071..94e88200 100644 --- a/dataset-index.yml +++ b/dataset-index.yml @@ -128,6 +128,24 @@ paper: https://arxiv.org/abs/2501.18362 configpath: opencompass/configs/datasets/MedXpertQA/MedXpertQA_gen.py configpath_llmjudge: opencompass/configs/datasets/MedXpertQA/MedXpertQA_llmjudge_gen.py +- ClinicBench: + name: ClinicBench + category: Knowledge / Medicine + paper: https://arxiv.org/abs/2405.00716 + configpath: '' + configpath_llmjudge: opencompass/configs/datasets/ClinicBench/ClinicBench_llmjudge_gen.py +- ScienceQA: + name: ScienceQA + category: Knowledge / Medicine + paper: https://arxiv.org/abs/2209.09513 + configpath: '' + configpath_llmjudge: opencompass/configs/datasets/ScienceQA/ScienceQA_llmjudge_gen.py +- PubMedQA: + name: PubMedQA + category: Knowledge / Medicine + paper: https://arxiv.org/abs/1909.06146 + configpath: '' + configpath_llmjudge: opencompass/configs/datasets/PubMedQA/PubMedQA_llmjudge_gen.py - musr: name: MuSR category: Reasoning diff --git a/opencompass/configs/datasets/ClinicBench/ClinicBench_llmjudge_gen.py b/opencompass/configs/datasets/ClinicBench/ClinicBench_llmjudge_gen.py new file mode 100644 index 00000000..febfce11 --- /dev/null +++ b/opencompass/configs/datasets/ClinicBench/ClinicBench_llmjudge_gen.py @@ -0,0 +1,4 @@ +from mmengine.config import read_base + +with read_base(): + from .ClinicBench_llmjudge_gen_d09668 import ClinicBench_datasets \ No newline at end of file diff --git a/opencompass/configs/datasets/ClinicBench/ClinicBench_llmjudge_gen_d09668.py b/opencompass/configs/datasets/ClinicBench/ClinicBench_llmjudge_gen_d09668.py new file mode 100644 index 00000000..358a91f5 --- /dev/null +++ b/opencompass/configs/datasets/ClinicBench/ClinicBench_llmjudge_gen_d09668.py @@ -0,0 +1,100 @@ +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.ClinicBench import ClinicBenchDataset + + +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() + +ClinicBench_datasets = [] + +ClinicBench_reader_cfg = dict( + input_columns=['question', 'choices'], + output_column='label', +) + +ClinicBench_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), +) + +ClinicBench_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=ClinicBenchDataset, + path='xuxuxuxuxu/Pharmacology-QA', + reader_cfg=ClinicBench_reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_postprocess), + ), +) + +ClinicBench_datasets.append( + dict( + abbr=f'ClinicBench', + type=ClinicBenchDataset, + path='xuxuxuxuxu/Pharmacology-QA', + reader_cfg=ClinicBench_reader_cfg, + infer_cfg=ClinicBench_infer_cfg, + eval_cfg=ClinicBench_eval_cfg, + ) +) diff --git a/opencompass/configs/datasets/PubMedQA/PubMedQA_llmjudge_gen.py b/opencompass/configs/datasets/PubMedQA/PubMedQA_llmjudge_gen.py new file mode 100644 index 00000000..4055d0f5 --- /dev/null +++ b/opencompass/configs/datasets/PubMedQA/PubMedQA_llmjudge_gen.py @@ -0,0 +1,4 @@ +from mmengine.config import read_base + +with read_base(): + from .PubMedQA_llmjudge_gen_f00302 import PubMedQA_datasets \ No newline at end of file diff --git a/opencompass/configs/datasets/PubMedQA/PubMedQA_llmjudge_gen_f00302.py b/opencompass/configs/datasets/PubMedQA/PubMedQA_llmjudge_gen_f00302.py new file mode 100644 index 00000000..b38a8fe5 --- /dev/null +++ b/opencompass/configs/datasets/PubMedQA/PubMedQA_llmjudge_gen_f00302.py @@ -0,0 +1,94 @@ +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.PubMedQA import PubMedQADataset + + +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() + +PubMedQA_datasets = [] + +PubMedQA_reader_cfg = dict( + input_columns=['question', 'choices'], + output_column='label', +) + +PubMedQA_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), +) + +PubMedQA_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=PubMedQADataset, + path='qiaojin/PubMedQA', + reader_cfg=PubMedQA_reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_postprocess), + ), +) + +PubMedQA_datasets.append( + dict( + abbr=f'PubMedQA', + type=PubMedQADataset, + path='qiaojin/PubMedQA', + reader_cfg=PubMedQA_reader_cfg, + infer_cfg=PubMedQA_infer_cfg, + eval_cfg=PubMedQA_eval_cfg, + ) +) \ No newline at end of file diff --git a/opencompass/configs/datasets/ScienceQA/ScienceQA_llmjudge_gen.py b/opencompass/configs/datasets/ScienceQA/ScienceQA_llmjudge_gen.py new file mode 100644 index 00000000..32305456 --- /dev/null +++ b/opencompass/configs/datasets/ScienceQA/ScienceQA_llmjudge_gen.py @@ -0,0 +1,4 @@ +from mmengine.config import read_base + +with read_base(): + from .ScienceQA_llmjudge_gen_f00302 import ScienceQA_datasets \ No newline at end of file diff --git a/opencompass/configs/datasets/ScienceQA/ScienceQA_llmjudge_gen_f00302.py b/opencompass/configs/datasets/ScienceQA/ScienceQA_llmjudge_gen_f00302.py new file mode 100644 index 00000000..e128c2a0 --- /dev/null +++ b/opencompass/configs/datasets/ScienceQA/ScienceQA_llmjudge_gen_f00302.py @@ -0,0 +1,94 @@ +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.ScienceQA import ScienceQADataset + + +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() + +ScienceQA_datasets = [] + +ScienceQA_reader_cfg = dict( + input_columns=['question', 'choices'], + output_column='label', +) + +ScienceQA_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), +) + +ScienceQA_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=ScienceQADataset, + path='derek-thomas/ScienceQA', + reader_cfg=ScienceQA_reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_postprocess), + ), +) + +ScienceQA_datasets.append( + dict( + abbr=f'ScienceQA', + type=ScienceQADataset, + path='derek-thomas/ScienceQA', + reader_cfg=ScienceQA_reader_cfg, + infer_cfg=ScienceQA_infer_cfg, + eval_cfg=ScienceQA_eval_cfg, + ) +) \ No newline at end of file diff --git a/opencompass/datasets/ClinicBench.py b/opencompass/datasets/ClinicBench.py new file mode 100644 index 00000000..86ef5082 --- /dev/null +++ b/opencompass/datasets/ClinicBench.py @@ -0,0 +1,19 @@ +from datasets import load_dataset + +from opencompass.registry import LOAD_DATASET + +from .base import BaseDataset + + +@LOAD_DATASET.register_module() +class ClinicBenchDataset(BaseDataset): + + @staticmethod + def load_single(path): + dataset = load_dataset(path)['train'] + return dataset + + @staticmethod + def load(path): + dataset = ClinicBenchDataset.load_single(path) + return dataset diff --git a/opencompass/datasets/PubMedQA.py b/opencompass/datasets/PubMedQA.py new file mode 100644 index 00000000..b0db32e3 --- /dev/null +++ b/opencompass/datasets/PubMedQA.py @@ -0,0 +1,34 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET + +from .base import BaseDataset + + +@LOAD_DATASET.register_module() +class PubMedQADataset(BaseDataset): + + @staticmethod + def load_single(path): + dataset = [] + ds = load_dataset(path, 'pqa_labeled') + for data in ds['train']: + data['question'] = (f"CONTEXTS: {data['context']}\n" + f"QUESTION: {data['question']}") + choices = 'A. yes\nB. no\nC. maybe' + data['choices'] = choices + if data['final_decision'] == 'yes': + data['label'] = 'A. yes' + elif data['final_decision'] == 'no': + data['label'] = 'B. no' + else: + data['label'] = 'C. maybe' + + dataset.append(data) + + return Dataset.from_list(dataset) + + @staticmethod + def load(path): + dataset = PubMedQADataset.load_single(path) + return dataset diff --git a/opencompass/datasets/ScienceQA.py b/opencompass/datasets/ScienceQA.py new file mode 100644 index 00000000..1bc9c952 --- /dev/null +++ b/opencompass/datasets/ScienceQA.py @@ -0,0 +1,32 @@ +from datasets import Dataset, load_dataset + +from opencompass.registry import LOAD_DATASET + +from .base import BaseDataset + + +@LOAD_DATASET.register_module() +class ScienceQADataset(BaseDataset): + + @staticmethod + def load_single(path): + dataset = [] + ds = load_dataset(path) + for data in ds['test']: + if data['image'] is None: + data['label'] = chr(65 + data['answer'] + ) + '. ' + data['choices'][data['answer']] + choices = '' + for i in range(len(data['choices'])): + choices += chr(65 + i) + '. ' + data['choices'][i] + '\n' + data['choices'] = choices + # print(data) + + dataset.append(data) + + return Dataset.from_list(dataset) + + @staticmethod + def load(path): + dataset = ScienceQADataset.load_single(path) + return dataset From a685ed7daffcab6b7cfb27ba1332242e403d138f Mon Sep 17 00:00:00 2001 From: Wei Li <1253865871@qq.com> Date: Thu, 8 May 2025 16:44:05 +0800 Subject: [PATCH 02/10] [Dataset] Add nejm ai benchmark (#2063) * support nejm ai benchmark * add dataset files * revise gen name * revise gen name * revise class name & remove csv file & add dataset-index.yml info * update * update --------- Co-authored-by: MaiziXiao --- dataset-index.yml | 8 +- .../nejm_ai_benchmark/nejmaibench_gen.py | 4 + .../nejmaibench_gen_60c8f5.py | 59 ++++++++ .../nejmaibench_llmjudge_gen.py | 4 + .../nejmaibench_llmjudge_gen_60c8f5.py | 108 ++++++++++++++ opencompass/datasets/__init__.py | 1 + opencompass/datasets/nejmaibench.py | 139 ++++++++++++++++++ opencompass/utils/datasets_info.py | 10 ++ 8 files changed, 332 insertions(+), 1 deletion(-) create mode 100644 opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_gen.py create mode 100644 opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_gen_60c8f5.py create mode 100644 opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_llmjudge_gen.py create mode 100644 opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_llmjudge_gen_60c8f5.py create mode 100644 opencompass/datasets/nejmaibench.py diff --git a/dataset-index.yml b/dataset-index.yml index 94e88200..1bfbdbbc 100644 --- a/dataset-index.yml +++ b/dataset-index.yml @@ -1046,4 +1046,10 @@ category: Reasoning/Code/Agent paper: '' configpath: opencompass/configs/datasets/internsandbox/internsandbox_gen_44b982.py - configpath_llmjudge: '' \ No newline at end of file + configpath_llmjudge: '' +- nejmaibench: + name: nejmaibench + category: Science /Medicine + paper: https://arxiv.org/pdf/2308.04709 + configpath: opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_gen.py + configpath_llmjudge: opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_llmjudge_gen.py diff --git a/opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_gen.py b/opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_gen.py new file mode 100644 index 00000000..2116726c --- /dev/null +++ b/opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_gen.py @@ -0,0 +1,4 @@ +from mmengine.config import read_base + +with read_base(): + from .nejmaibench_gen_60c8f5 import nejmaibench_datasets # noqa: F401, F403 \ No newline at end of file diff --git a/opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_gen_60c8f5.py b/opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_gen_60c8f5.py new file mode 100644 index 00000000..ec817c57 --- /dev/null +++ b/opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_gen_60c8f5.py @@ -0,0 +1,59 @@ +from opencompass.datasets import NejmaibenchDataset, NejmaibenchEvaluator +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever + +import os + +SYSTEM_PROMPT = 'You are a helpful medical assistant.\n\n' # Where to put this? +ZERO_SHOT_PROMPT = 'Q: {question}\n Please select the correct answer from the options above and output only the corresponding letter (A, B, C, D, or E) without any explanation or additional text.\n' + +# Reader configuration +reader_cfg = dict( + input_columns=[ + 'question', + 'options', + 'Subject', + 'prompt_mode', + + ], + output_column='label', +) + +# Inference configuration +infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + begin=[ + dict(role='SYSTEM', fallback_role='HUMAN', prompt=SYSTEM_PROMPT), + ], + round=[ + dict( + role='HUMAN', + prompt=ZERO_SHOT_PROMPT, # prompt mode: zero-shot + ), + ], + ), + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), +) + +# Evaluation configuration +eval_cfg = dict( + evaluator=dict(type=NejmaibenchEvaluator), + pred_role='BOT', +) +nejmaibench_dataset = dict( + type=NejmaibenchDataset, + abbr='nejmaibench', + path='opencompass/nejmaibench', + prompt_mode='zero-shot', + reader_cfg=reader_cfg, + infer_cfg=infer_cfg, + eval_cfg=eval_cfg, + +) + +nejmaibench_datasets = [nejmaibench_dataset] diff --git a/opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_llmjudge_gen.py b/opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_llmjudge_gen.py new file mode 100644 index 00000000..de683ccc --- /dev/null +++ b/opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_llmjudge_gen.py @@ -0,0 +1,4 @@ +from mmengine.config import read_base + +with read_base(): + from .nejmaibench_llmjudge_gen_60c8f5 import nejmaibench_datasets # noqa: F401, F403 \ No newline at end of file diff --git a/opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_llmjudge_gen_60c8f5.py b/opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_llmjudge_gen_60c8f5.py new file mode 100644 index 00000000..31be8049 --- /dev/null +++ b/opencompass/configs/datasets/nejm_ai_benchmark/nejmaibench_llmjudge_gen_60c8f5.py @@ -0,0 +1,108 @@ +from opencompass.datasets import NejmaibenchDataset +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.datasets import generic_llmjudge_postprocess +from opencompass.evaluator import GenericLLMEvaluator +import os + +SYSTEM_PROMPT = 'You are a helpful medical assistant.\n\n' # Where to put this? +ZERO_SHOT_PROMPT = 'Q: {question}\n Please select the correct answer from the options above and output only the corresponding letter (A, B, C, D, or E) without any explanation or additional text.\n' +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. + + : Q: {question}\nPlease select the correct answer from the options above and output only the corresponding letter (A, B, C, D, or E) without any explanation or additional text.\n\n\n\n + : \n{label}\n\n\n + : \n{prediction}\n\n\n + Judging the correctness of candidates' answers: +""".strip() + +# Reader configuration +reader_cfg = dict( + input_columns=[ + 'question', + 'options', + 'Subject', + 'prompt_mode', + + ], + output_column='label', +) + + +# Inference configuration +infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + begin=[ + dict(role='SYSTEM', fallback_role='HUMAN', prompt=SYSTEM_PROMPT), + ], + round=[ + dict( + role='HUMAN', + prompt=ZERO_SHOT_PROMPT, # prompt mode: zero-shot + ), + ], + ), + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), +) + +# Evaluation configuration +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=NejmaibenchDataset, + path='opencompass/nejmaibench', + prompt_mode='zero-shot', + reader_cfg=reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_postprocess), + ), +) + + +nejmaibench_dataset = dict( + type=NejmaibenchDataset, + abbr='nejmaibench', + path='opencompass/nejmaibench', + prompt_mode='zero-shot', + reader_cfg=reader_cfg, + infer_cfg=infer_cfg, + eval_cfg=eval_cfg, + +) + +nejmaibench_datasets = [nejmaibench_dataset] diff --git a/opencompass/datasets/__init__.py b/opencompass/datasets/__init__.py index a7c037cf..220ce030 100644 --- a/opencompass/datasets/__init__.py +++ b/opencompass/datasets/__init__.py @@ -109,6 +109,7 @@ from .musr import * # noqa: F401, F403 from .narrativeqa import * # noqa: F401, F403 from .natural_question import * # noqa: F401, F403 from .natural_question_cn import * # noqa: F401, F403 +from .nejmaibench import * # noqa: F401, F403 from .NPHardEval import * # noqa: F401, F403 from .obqa import * # noqa: F401, F403 from .olymmath import * # noqa: F401, F403 diff --git a/opencompass/datasets/nejmaibench.py b/opencompass/datasets/nejmaibench.py new file mode 100644 index 00000000..768f4688 --- /dev/null +++ b/opencompass/datasets/nejmaibench.py @@ -0,0 +1,139 @@ +import re + +import pandas as pd +from datasets import Dataset + +from opencompass.openicl import BaseEvaluator +from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS +from opencompass.utils import get_data_path + +from .base import BaseDataset + + +def _parse(item, prompt_mode): + # 1. 从 Choices 字符串里按行拆分出每个选项 + raw_choices = item.get('Choices', '') + # 去掉首尾空白并按行分割，过滤掉空行 + lines = [ + line.strip() for line in raw_choices.strip().splitlines() + if line.strip() + ] + + # 2. 用正则去掉行首的 "A. "/"B. " 等前缀，只保留选项内容 + options_list = [re.sub(r'^[A-Z]\.\s*', '', line) for line in lines] + + # 3. 写回 item + item['options'] = options_list + + # 4. 重建带标号的选项字符串 + options_str = '\n'.join(f'{chr(65 + i)}. {opt}' + for i, opt in enumerate(options_list)) + + # 5. 构造 question、label、prompt_mode、start、end + item['question'] = f"{item['Question']}\n{options_str}" + item['label'] = item['Answer'] + item['prompt_mode'] = prompt_mode + item['start'] = chr(65) + item['end'] = chr(65 + len(options_list) - 1) + return item + + +@LOAD_DATASET.register_module() +class NejmaibenchDataset(BaseDataset): + + @staticmethod + def load(path: str, prompt_mode: str = 'zero-shot', **kwargs): + # 读取 CSV 文件为 DataFrame，并将 NaN 转为空字符串 + path = get_data_path(path) + df = pd.read_csv(path, encoding='utf-8') + df = df.fillna('') + + # 转换为字典列表 + data_list = df.to_dict(orient='records') + + # 将数据列表包装为 Dataset + dataset = Dataset.from_list(data_list) + + # 根据提示模式进行解析 + if prompt_mode == 'zero-shot': + dataset = dataset.map(lambda item: _parse(item, prompt_mode)) + elif prompt_mode == 'few-shot': + pass # TODO: Implement few-shot prompt handling + return dataset + + +class NejmaibenchEvaluator(BaseEvaluator): + + def score(self, predictions, references, test_set): + method = test_set['prompt_mode'][0] + + 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)): + i = answer_cleansing(method, i, test_set['options'][idx], + test_set['label'][idx]) + detail = { + 'pred': i, + 'answer': j, + 'correct': False, + 'Subject': test_set['Subject'][idx], + } + count += 1 + if i == j: + correct += 1 + detail['correct'] = True + details.append(detail) + result = {'accuracy': 100 * correct / count, 'details': details} + return result + + +@TEXT_POSTPROCESSORS.register_module() +def answer_cleansing( + method: str, + prediction: str, + options: list, + label: str, +) -> str: + + # Clean up unwanted phrases in the prediction + for unwanted_phrase in [ + 'I understand', + 'A through J', + 'A through E', + 'A through D', + ]: + prediction = prediction.replace(unwanted_phrase, '') + + options_num = len(options) + options = [chr(65 + i) for i in range(options_num)] + options_str = r'\b(' + '|'.join(options) + r')\b' + prediction = re.findall(options_str, prediction) + + if len(prediction) == 0: + prediction = [] + return prediction + else: + # If there is a "label" and its length is 1, + # process prediction accordingly + if len(label) == 1: + if method == 'few-shot': + answer_flag = True if len(prediction) > 1 else False + # choose the first or last element based on the answer_flag + if answer_flag: + prediction = [prediction[0]] + else: + prediction = [prediction[-1]] + elif method == 'zero-shot': + # choose the first element in list + prediction = [prediction[0]] + else: + raise ValueError('Method is not properly defined ...') + + # Remove trailing period if it exists + if prediction[0] and prediction[0].endswith('.'): + prediction[0] = prediction[0][:-1] + + return prediction[0] diff --git a/opencompass/utils/datasets_info.py b/opencompass/utils/datasets_info.py index 5048a496..10ca4436 100644 --- a/opencompass/utils/datasets_info.py +++ b/opencompass/utils/datasets_info.py @@ -446,6 +446,11 @@ DATASETS_MAPPING = { "hf_id": "", "local": "./data/ChemBench4K", }, + "opencompass/nejmaibench": { + "ms_id": "", + "hf_id": "", + "local": "./data/nejmaibench/NEJM_All_Questions_And_Answers.csv", + }, } @@ -798,6 +803,11 @@ DATASETS_URL = { "url": "http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/ChemBench4K.zip", "md5": "fc23fd21b2566a5dbbebfa4601d7779c" + }, + "nejmaibench": { + "url": + "http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/nejmaibench.zip", + "md5": "e6082cae3596b3ebea73e23ba445b99e" } } From ff3275edf023f2ce49c3fbfed90ff675ca27c1c9 Mon Sep 17 00:00:00 2001 From: Mo Li <2568818204@qq.com> Date: Thu, 8 May 2025 19:06:56 +0800 Subject: [PATCH 03/10] [Update] Add Long-Context configs for Gemma, OREAL, and Qwen2.5 models (#2048) * [Update] Update Gemma, Oreal, Qwen Config * fix lint --- .../models/gemma/vllm_gemma_3_12b_it.py | 16 ++++++++++++++ .../models/gemma/vllm_gemma_3_27b_it.py | 16 ++++++++++++++ .../models/gemma/vllm_gemma_3_4b_it.py | 17 +++++++++++++++ .../lmdeploy_internlm3_8b_instruct_128k.py | 19 +++++++++++++++++ .../models/hf_internlm/lmdeploy_oreal_32b.py | 20 ++++++++++++++++++ .../qwen2_5/vllm_qwen2_5_14b_instruct_128k.py | 21 +++++++++++++++++++ .../qwen2_5/vllm_qwen2_5_32b_instruct_128k.py | 21 +++++++++++++++++++ .../qwen2_5/vllm_qwen2_5_72b_instruct_128k.py | 21 +++++++++++++++++++ .../qwen2_5/vllm_qwen2_5_7b_instruct_128k.py | 21 +++++++++++++++++++ 9 files changed, 172 insertions(+) create mode 100644 opencompass/configs/models/gemma/vllm_gemma_3_12b_it.py create mode 100644 opencompass/configs/models/gemma/vllm_gemma_3_27b_it.py create mode 100644 opencompass/configs/models/gemma/vllm_gemma_3_4b_it.py create mode 100644 opencompass/configs/models/hf_internlm/lmdeploy_internlm3_8b_instruct_128k.py create mode 100644 opencompass/configs/models/hf_internlm/lmdeploy_oreal_32b.py create mode 100644 opencompass/configs/models/qwen2_5/vllm_qwen2_5_14b_instruct_128k.py create mode 100644 opencompass/configs/models/qwen2_5/vllm_qwen2_5_32b_instruct_128k.py create mode 100644 opencompass/configs/models/qwen2_5/vllm_qwen2_5_72b_instruct_128k.py create mode 100644 opencompass/configs/models/qwen2_5/vllm_qwen2_5_7b_instruct_128k.py diff --git a/opencompass/configs/models/gemma/vllm_gemma_3_12b_it.py b/opencompass/configs/models/gemma/vllm_gemma_3_12b_it.py new file mode 100644 index 00000000..2914640f --- /dev/null +++ b/opencompass/configs/models/gemma/vllm_gemma_3_12b_it.py @@ -0,0 +1,16 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='gemma-3-12b-it-vllm', + path='google/gemma-3-12b-it', + model_kwargs=dict(tensor_parallel_size=4, + # for long context + rope_scaling={'factor': 8.0, 'rope_type': 'linear'}), + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/gemma/vllm_gemma_3_27b_it.py b/opencompass/configs/models/gemma/vllm_gemma_3_27b_it.py new file mode 100644 index 00000000..b6f4b93b --- /dev/null +++ b/opencompass/configs/models/gemma/vllm_gemma_3_27b_it.py @@ -0,0 +1,16 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='gemma-3-27b-it-vllm', + path='google/gemma-3-27b-it', + model_kwargs=dict(tensor_parallel_size=4, + # for long context + rope_scaling={'factor': 8.0, 'rope_type': 'linear'}), + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/gemma/vllm_gemma_3_4b_it.py b/opencompass/configs/models/gemma/vllm_gemma_3_4b_it.py new file mode 100644 index 00000000..22516ff7 --- /dev/null +++ b/opencompass/configs/models/gemma/vllm_gemma_3_4b_it.py @@ -0,0 +1,17 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='gemma-3-4b-it-vllm', + path='google/gemma-3-4b-it', + model_kwargs=dict(tensor_parallel_size=2, + # for long context + rope_scaling={'factor': 8.0, 'rope_type': 'linear'}), + max_seq_len=140000, + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_internlm3_8b_instruct_128k.py b/opencompass/configs/models/hf_internlm/lmdeploy_internlm3_8b_instruct_128k.py new file mode 100644 index 00000000..1cc4e251 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_internlm3_8b_instruct_128k.py @@ -0,0 +1,19 @@ +from opencompass.models import TurboMindModelwithChatTemplate + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='internlm3-8b-instruct-turbomind', + path='internlm/internlm3-8b-instruct', + engine_config=dict(session_len=142000, max_batch_size=1, tp=2, + # for long context + rope_scaling_factor=6.0), + gen_config=dict( + top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=8192 + ), + max_seq_len=142000, + max_out_len=8192, + batch_size=1, + run_cfg=dict(num_gpus=2), + ) +] diff --git a/opencompass/configs/models/hf_internlm/lmdeploy_oreal_32b.py b/opencompass/configs/models/hf_internlm/lmdeploy_oreal_32b.py new file mode 100644 index 00000000..1d10bd94 --- /dev/null +++ b/opencompass/configs/models/hf_internlm/lmdeploy_oreal_32b.py @@ -0,0 +1,20 @@ +from opencompass.models import TurboMindModelwithChatTemplate +from opencompass.utils.text_postprocessors import extract_non_reasoning_content + +models = [ + dict( + type=TurboMindModelwithChatTemplate, + abbr='OREAL-32B', + path='internlm/OREAL-32B', + engine_config=dict(session_len=32768, max_batch_size=16, tp=4), + gen_config=dict(top_k=1, + temperature=1e-6, + top_p=0.9, + max_new_tokens=32768), + max_seq_len=32768, + max_out_len=32768, + batch_size=16, + run_cfg=dict(num_gpus=4), + pred_postprocessor=dict(type=extract_non_reasoning_content) + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_14b_instruct_128k.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_14b_instruct_128k.py new file mode 100644 index 00000000..6dec3743 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_14b_instruct_128k.py @@ -0,0 +1,21 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2.5-14b-instruct-vllm', + path='Qwen/Qwen2.5-14B-Instruct', + model_kwargs=dict( + tensor_parallel_size=4, + rope_scaling={ + 'factor': 4.0, + 'original_max_position_embeddings': 32768, + 'rope_type': 'yarn' + }, + ), + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_32b_instruct_128k.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_32b_instruct_128k.py new file mode 100644 index 00000000..5c326734 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_32b_instruct_128k.py @@ -0,0 +1,21 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2.5-32b-instruct-vllm', + path='Qwen/Qwen2.5-32B-Instruct', + model_kwargs=dict( + tensor_parallel_size=8, + rope_scaling={ + 'factor': 4.0, + 'original_max_position_embeddings': 32768, + 'rope_type': 'yarn' + }, + ), + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_72b_instruct_128k.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_72b_instruct_128k.py new file mode 100644 index 00000000..2a4a52fa --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_72b_instruct_128k.py @@ -0,0 +1,21 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2_5-72b-instruct-vllm', + path='Qwen/Qwen2.5-72B-Instruct', + model_kwargs=dict( + tensor_parallel_size=8, + rope_scaling={ + 'factor': 4.0, + 'original_max_position_embeddings': 32768, + 'rope_type': 'yarn' + }, + ), + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=8), + ) +] diff --git a/opencompass/configs/models/qwen2_5/vllm_qwen2_5_7b_instruct_128k.py b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_7b_instruct_128k.py new file mode 100644 index 00000000..db21f730 --- /dev/null +++ b/opencompass/configs/models/qwen2_5/vllm_qwen2_5_7b_instruct_128k.py @@ -0,0 +1,21 @@ +from opencompass.models import VLLMwithChatTemplate + +models = [ + dict( + type=VLLMwithChatTemplate, + abbr='qwen2.5-7b-instruct-vllm', + path='Qwen/Qwen2.5-7B-Instruct', + model_kwargs=dict( + tensor_parallel_size=4, + rope_scaling={ + 'factor': 4.0, + 'original_max_position_embeddings': 32768, + 'rope_type': 'yarn' + }, + ), + max_out_len=4096, + batch_size=1, + generation_kwargs=dict(temperature=0), + run_cfg=dict(num_gpus=4), + ) +] From a7f3ac20b259339b851e4664991686161fa0d866 Mon Sep 17 00:00:00 2001 From: huihui1999 <107675879+bio-mlhui@users.noreply.github.com> Date: Thu, 8 May 2025 19:44:46 +0800 Subject: [PATCH 04/10] [Dataset] Add CARDBiomedBench (#2071) * CARDBiomedBench * fix hash * fix dataset-index * use official llmjudge postprocess * use official llmjudge_postprocess * fix lint * fix init --- dataset-index.yml | 6 ++ .../CARDBiomedBench_llmjudge_gen_99a231.py | 101 ++++++++++++++++++ opencompass/datasets/CARDBiomedBench.py | 30 ++++++ opencompass/datasets/__init__.py | 1 + 4 files changed, 138 insertions(+) create mode 100644 opencompass/configs/datasets/CARDBiomedBench/CARDBiomedBench_llmjudge_gen_99a231.py create mode 100644 opencompass/datasets/CARDBiomedBench.py diff --git a/dataset-index.yml b/dataset-index.yml index 1bfbdbbc..f0960740 100644 --- a/dataset-index.yml +++ b/dataset-index.yml @@ -361,6 +361,12 @@ paper: https://arxiv.org/pdf/2004.05986 configpath: opencompass/configs/datasets/CLUE_C3/CLUE_C3_gen.py configpath_llmjudge: '' +- CARDBiomedBench: + name: CARDBiomedBench + category: Knowledge / Medicine + paper: https://www.biorxiv.org/content/10.1101/2025.01.15.633272v1 + configpath: opencompass/configs/datasets/CARDBiomedBench + configpath_llmjudge: 'opencompass/configs/datasets/CARDBiomedBench/CARDBiomedBench_llmjudge_gen_99a231.py' - cb: name: SuperGLUE / CB category: Reasoning diff --git a/opencompass/configs/datasets/CARDBiomedBench/CARDBiomedBench_llmjudge_gen_99a231.py b/opencompass/configs/datasets/CARDBiomedBench/CARDBiomedBench_llmjudge_gen_99a231.py new file mode 100644 index 00000000..c6acb71e --- /dev/null +++ b/opencompass/configs/datasets/CARDBiomedBench/CARDBiomedBench_llmjudge_gen_99a231.py @@ -0,0 +1,101 @@ +from opencompass.datasets import CARDBiomedBenchDataset +from opencompass.datasets import generic_llmjudge_postprocess +from opencompass.openicl.icl_inferencer import GenInferencer +from opencompass.openicl.icl_prompt_template import PromptTemplate +from opencompass.openicl.icl_retriever import ZeroRetriever +from opencompass.evaluator import GenericLLMEvaluator +ZERO_SHOT_PROMPT = 'You are an expert in {expert}.\n{question}\n' + +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. + + : Q: You are an expert in {expert}.\n{question}\n\n\n + : \n{answer}\n\n\n + : \n{prediction}\n\n\n + Judging the correctness of candidates' answers: +""".strip() + + +# Reader configuration +reader_cfg = dict( + input_columns=[ + 'question', + 'answer', + 'Bio_Category', + 'SQL_Category', + 'uuid', + 'template uuid', + 'expert', + ], + output_column='answer', +) +# Inference configuration +infer_cfg = dict( + prompt_template=dict( + type=PromptTemplate, + template=dict( + round=[ + dict( + + role='HUMAN', + prompt=ZERO_SHOT_PROMPT, # prompt mode: zero-shot + ), + ], + ), + ), + retriever=dict(type=ZeroRetriever), + inferencer=dict(type=GenInferencer), +) + +# Evaluation configuration +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=CARDBiomedBenchDataset, + path='NIH-CARD/CARDBiomedBench', + prompt_mode='zero-shot', + reader_cfg=reader_cfg, + ), + judge_cfg=dict(), + dict_postprocessor=dict(type=generic_llmjudge_postprocess), + ), +) +cardbiomedbench_dataset = dict( + type=CARDBiomedBenchDataset, + abbr='cardbiomedbench', + path='NIH-CARD/CARDBiomedBench', + prompt_mode='zero-shot', + reader_cfg=reader_cfg, + infer_cfg=infer_cfg, + eval_cfg=eval_cfg, +) +cardbiomedbench_datasets = [cardbiomedbench_dataset] diff --git a/opencompass/datasets/CARDBiomedBench.py b/opencompass/datasets/CARDBiomedBench.py new file mode 100644 index 00000000..77ff9ee6 --- /dev/null +++ b/opencompass/datasets/CARDBiomedBench.py @@ -0,0 +1,30 @@ +from datasets import load_dataset + +from opencompass.registry import LOAD_DATASET + +from .base import BaseDataset + + +def _parse(item, prompt_mode): + item['expert'] = item['Bio_Category'] + item['start'] = chr(65) + item['end'] = chr(65 + len(item.get('choices', {'label': []})['label']) - + 1) + item['prompt_mode'] = prompt_mode + return item + + +@LOAD_DATASET.register_module() +class CARDBiomedBenchDataset(BaseDataset): + + @staticmethod + def load(path: str, prompt_mode: str, **kwargs): + data_files = {'test': 'data/CARDBiomedBench.csv'} + dataset = load_dataset(path, data_files=data_files, split='test') + # dataset = dataset.select(range(200)) + 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 diff --git a/opencompass/datasets/__init__.py b/opencompass/datasets/__init__.py index 220ce030..03e7d228 100644 --- a/opencompass/datasets/__init__.py +++ b/opencompass/datasets/__init__.py @@ -16,6 +16,7 @@ from .boolq import * # noqa: F401, F403 from .bustum import * # noqa: F401, F403 from .c3 import * # noqa: F401, F403 from .calm import * # noqa: F401, F403 +from .CARDBiomedBench import CARDBiomedBenchDataset # noqa: F401 from .cb import * # noqa: F401, F403 from .ceval import * # noqa: F401, F403 from .charm import * # noqa: F401, F403 From c5048bfec7890ad1d1a3efa77963e3eba0c97730 Mon Sep 17 00:00:00 2001 From: tcheng Date: Fri, 9 May 2025 14:31:12 +0800 Subject: [PATCH 05/10] [Dataset] Add Lifescience Sub-set Support for SciEval (#2059) * style: pass all formatting hooks (yapf & quote fixer) * revise name:Add Lifescience Sub-set Support for MMLU & SciEval (datasets + configs + loader) * revise name:Add Lifescience SciEval (datasets + configs + loader+dataset-index.yml) * Add Lifescience SciEval (datasets + configs + loader+dataset-index.yml) --------- Co-authored-by: root --- dataset-index.yml | 6 + .../SciEval_lifescience_0shot_gen_4043d4.py | 61 +++++++++ ...l_lifescience_0shot_llmjudge_gen_012dd1.py | 125 ++++++++++++++++++ .../SciEval_lifescience_sets.py | 3 + opencompass/datasets/SciEval_lifescience.py | 62 +++++++++ opencompass/datasets/__init__.py | 1 + 6 files changed, 258 insertions(+) create mode 100644 opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_0shot_gen_4043d4.py create mode 100644 opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_0shot_llmjudge_gen_012dd1.py create mode 100644 opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_sets.py create mode 100644 opencompass/datasets/SciEval_lifescience.py diff --git a/dataset-index.yml b/dataset-index.yml index f0960740..fcf34dcb 100644 --- a/dataset-index.yml +++ b/dataset-index.yml @@ -695,6 +695,12 @@ 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 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 new file mode 100644 index 00000000..5381abcf --- /dev/null +++ b/opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_0shot_gen_4043d4.py @@ -0,0 +1,61 @@ +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 new file mode 100644 index 00000000..26af5cd3 --- /dev/null +++ b/opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_0shot_llmjudge_gen_012dd1.py @@ -0,0 +1,125 @@ +# 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 new file mode 100644 index 00000000..8d0a0a83 --- /dev/null +++ b/opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_sets.py @@ -0,0 +1,3 @@ +SciEval_lifescience_subsets = [ + 'biology', # 大学生物学 +] diff --git a/opencompass/datasets/SciEval_lifescience.py b/opencompass/datasets/SciEval_lifescience.py new file mode 100644 index 00000000..af93e496 --- /dev/null +++ b/opencompass/datasets/SciEval_lifescience.py @@ -0,0 +1,62 @@ +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 03e7d228..a70b27d5 100644 --- a/opencompass/datasets/__init__.py +++ b/opencompass/datasets/__init__.py @@ -130,6 +130,7 @@ 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 .SciEval_lifescience import SciEvalDataset # noqa: F401 from .simpleqa import * # noqa: F401, F403 from .siqa import * # noqa: F401, F403 from .smolinstruct import * # noqa: F401, F403 From d72df59363ef1e4e67c6f7a3873268badf16c205 Mon Sep 17 00:00:00 2001 From: Linchen Xiao Date: Fri, 9 May 2025 14:46:27 +0800 Subject: [PATCH 06/10] [Revert] Add Lifescience Sub-set Support for SciEval (#2059) (#2087) This reverts commit c5048bfec7890ad1d1a3efa77963e3eba0c97730. --- dataset-index.yml | 6 - .../SciEval_lifescience_0shot_gen_4043d4.py | 61 --------- ...l_lifescience_0shot_llmjudge_gen_012dd1.py | 125 ------------------ .../SciEval_lifescience_sets.py | 3 - opencompass/datasets/SciEval_lifescience.py | 62 --------- opencompass/datasets/__init__.py | 1 - 6 files changed, 258 deletions(-) delete mode 100644 opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_0shot_gen_4043d4.py delete mode 100644 opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_0shot_llmjudge_gen_012dd1.py delete mode 100644 opencompass/configs/datasets/SciEval_lifscience/SciEval_lifescience_sets.py delete mode 100644 opencompass/datasets/SciEval_lifescience.py diff --git a/dataset-index.yml b/dataset-index.yml index fcf34dcb..f0960740 100644 --- a/dataset-index.yml +++ b/dataset-index.yml @@ -695,12 +695,6 @@ 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 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/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 a70b27d5..03e7d228 100644 --- a/opencompass/datasets/__init__.py +++ b/opencompass/datasets/__init__.py @@ -130,7 +130,6 @@ 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 .SciEval_lifescience import SciEvalDataset # noqa: F401 from .simpleqa import * # noqa: F401, F403 from .siqa import * # noqa: F401, F403 from .smolinstruct import * # noqa: F401, F403 From 6097186a95e8bbaa38d817dd990065aa83552fe6 Mon Sep 17 00:00:00 2001 From: Jin Ye Date: Fri, 9 May 2025 16:47:44 +1000 Subject: [PATCH 07/10] [Datasets] MedQA, ProteinLMBench; Add Models: huatuogpt, baichuanM1 (#2064) * Add Datasets: MedQA, ProteinLMBench; Add Models: huatuogpt, baichuanM1 * Fix bugs for MedQA. Add info in dataset-index * Add version code for MedQA and ProteinLMBench * Add version code for MedQA and ProteinLMBench --- dataset-index.yml | 12 ++ .../datasets/MedQA/MedQA_gen_3bf756.py | 63 ++++++++++ .../MedQA/MedQA_llmjudge_gen_3bf756.py | 108 ++++++++++++++++++ .../ProteinLMBench_gen_a67965.py | 46 ++++++++ .../ProteinLMBench_llmjudge_gen_a67965.py | 89 +++++++++++++++ .../baichuan/hf_baichuan_m1_14b_base.py | 14 +++ .../baichuan/hf_baichuan_m1_14b_instruct.py | 14 +++ .../models/huatuogpt/hf_huatuogpt2_13b.py | 17 +++ .../models/huatuogpt/hf_huatuogpt2_7b.py | 13 +++ .../models/huatuogpt/hf_huatuogpt_o1_7b.py | 15 +++ .../models/huatuogpt/hf_huatuogpt_o1_8b.py | 15 +++ opencompass/datasets/MedQA.py | 29 +++++ opencompass/datasets/ProteinLMBench.py | 58 ++++++++++ opencompass/datasets/__init__.py | 2 + 14 files changed, 495 insertions(+) create mode 100644 opencompass/configs/datasets/MedQA/MedQA_gen_3bf756.py create mode 100644 opencompass/configs/datasets/MedQA/MedQA_llmjudge_gen_3bf756.py create mode 100644 opencompass/configs/datasets/ProteinLMBench/ProteinLMBench_gen_a67965.py create mode 100644 opencompass/configs/datasets/ProteinLMBench/ProteinLMBench_llmjudge_gen_a67965.py create mode 100644 opencompass/configs/models/baichuan/hf_baichuan_m1_14b_base.py create mode 100644 opencompass/configs/models/baichuan/hf_baichuan_m1_14b_instruct.py create mode 100644 opencompass/configs/models/huatuogpt/hf_huatuogpt2_13b.py create mode 100644 opencompass/configs/models/huatuogpt/hf_huatuogpt2_7b.py create mode 100644 opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_7b.py create mode 100644 opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_8b.py create mode 100644 opencompass/datasets/MedQA.py create mode 100644 opencompass/datasets/ProteinLMBench.py diff --git a/dataset-index.yml b/dataset-index.yml index f0960740..abd0878a 100644 --- a/dataset-index.yml +++ b/dataset-index.yml @@ -122,6 +122,12 @@ paper: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10778138 configpath: opencompass/configs/datasets/MedBench/medbench_gen.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 @@ -763,6 +769,12 @@ 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/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/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/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/__init__.py b/opencompass/datasets/__init__.py index 03e7d228..5a98a942 100644 --- a/opencompass/datasets/__init__.py +++ b/opencompass/datasets/__init__.py @@ -97,6 +97,7 @@ 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 .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 +119,7 @@ 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 From 7bdd3c190451f83fc4fa8f250b77f0bf35c8d628 Mon Sep 17 00:00:00 2001 From: Kun Yuan <31314392+Flaick@users.noreply.github.com> Date: Fri, 9 May 2025 09:07:26 +0200 Subject: [PATCH 08/10] [Dataset] MMLU_Pro Biomedical Version Support (#2081) --- .../mmlu_pro_biomed_0shot_cot_gen_057927.py | 60 +++++++++++ ...d_0shot_nocot_genericllmeval_gen_057927.py | 101 ++++++++++++++++++ 2 files changed, 161 insertions(+) create mode 100644 opencompass/configs/datasets/mmlu_pro/mmlu_pro_biomed_0shot_cot_gen_057927.py create mode 100644 opencompass/configs/datasets/mmlu_pro/mmlu_pro_biomed_0shot_nocot_genericllmeval_gen_057927.py 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 From 508e2b0cb252ce6adb2a12b92ef4bfa38f13d4d7 Mon Sep 17 00:00:00 2001 From: Linchen Xiao Date: Fri, 9 May 2025 15:21:47 +0800 Subject: [PATCH 09/10] [Update] Set load_from_cache_file to False (#2085) --- opencompass/datasets/base.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) 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) From 44a7024ed556917e158c41852fd7d0e23719e884 Mon Sep 17 00:00:00 2001 From: huihui1999 <107675879+bio-mlhui@users.noreply.github.com> Date: Fri, 9 May 2025 16:58:55 +0800 Subject: [PATCH 10/10] [Dataset] MedCalc_Bench (#2072) * MedCalc_Bench * MedCal_Bench * add hash * fix hash * fix comments &dataset-index yml * fix lint * fix lint * fix lint * fix lint * fix lint --------- Co-authored-by: Linchen Xiao --- dataset-index.yml | 6 + .../MedCalcBench_official_gen_a5155f.py | 57 ++++ opencompass/datasets/MedCalc_Bench.py | 323 ++++++++++++++++++ opencompass/datasets/__init__.py | 2 + 4 files changed, 388 insertions(+) create mode 100644 opencompass/configs/datasets/MedCalc_Bench/MedCalcBench_official_gen_a5155f.py create mode 100644 opencompass/datasets/MedCalc_Bench.py diff --git a/dataset-index.yml b/dataset-index.yml index abd0878a..a2179b92 100644 --- a/dataset-index.yml +++ b/dataset-index.yml @@ -122,6 +122,12 @@ 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 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/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/__init__.py b/opencompass/datasets/__init__.py index 5a98a942..babdcef2 100644 --- a/opencompass/datasets/__init__.py +++ b/opencompass/datasets/__init__.py @@ -97,6 +97,8 @@ 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