mirror of
https://github.com/open-compass/opencompass.git
synced 2025-05-30 16:03:24 +08:00
Merge branch 'main' into SciKnowEval
This commit is contained in:
Linchen Xiao
GitHub
commit
3119903840
@ -122,6 +122,18 @@
|
||||
paper: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10778138
|
||||
configpath: opencompass/configs/datasets/MedBench/medbench_gen.py
|
||||
configpath_llmjudge: ''
|
||||
- MedCalc_Bench:
|
||||
name: MedCalc_Bench
|
||||
category: Knowledge / Medicine
|
||||
paper: https://arxiv.org/abs/2406.12036
|
||||
configpath: opencompass/configs/datasets/MedCalc_Bench/MedCalcBench_official_gen_a5155f.py
|
||||
configpath_llmjudge: ''
|
||||
- MedXpertQA:
|
||||
name: MedQA
|
||||
category: Knowledge / Medicine
|
||||
paper: https://arxiv.org/abs/2009.13081
|
||||
configpath: opencompass/configs/datasets/MedQA/MedQA_gen.py
|
||||
configpath_llmjudge: opencompass/configs/datasets/MedQA/MedQA_llmjudge_gen.py
|
||||
- MedXpertQA:
|
||||
name: MedXpertQA
|
||||
category: Knowledge / Medicine
|
||||
@ -695,12 +707,6 @@
|
||||
paper: https://arxiv.org/pdf/2009.03300
|
||||
configpath: opencompass/configs/datasets/mmlu/mmlu_gen.py
|
||||
configpath_llmjudge: opencompass/configs/datasets/mmlu/mmlu_llm_judge_gen.py
|
||||
- SciEval:
|
||||
name: SciEval
|
||||
category: Understanding
|
||||
paper: https://arxiv.org/pdf/2308.13149
|
||||
configpath: opencompass/configs/datasets/SciEval_lifscience/SciEval_lifscience_gen.py
|
||||
configpath_llmjudge: opencompass/configs/datasets/SciEval_lifscience/SciEval_lifscience_llm_judge_gen.py
|
||||
- mmlu_cf:
|
||||
name: MMLU-CF
|
||||
category: Understanding
|
||||
@ -769,6 +775,12 @@
|
||||
paper: https://arxiv.org/pdf/1911.11641v1
|
||||
configpath: opencompass/configs/datasets/piqa/piqa_gen.py
|
||||
configpath_llmjudge: ''
|
||||
- ProteinLMBench:
|
||||
name: ProteinLMBench
|
||||
category: Knowledge / Biology (Protein)
|
||||
paper: https://arxiv.org/abs/2406.05540
|
||||
configpath: opencompass/configs/datasets/ProteinLMBench/ProteinLMBench_gen.py
|
||||
configpath_llmjudge: opencompass/configs/datasets/ProteinLMBench/ProteinLMBench_llmjudge_gen.py
|
||||
- py150:
|
||||
name: py150
|
||||
category: Code
|
||||
|
||||
@ -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]
|
||||
63
opencompass/configs/datasets/MedQA/MedQA_gen_3bf756.py
Normal file
63
opencompass/configs/datasets/MedQA/MedQA_gen_3bf756.py
Normal file
@ -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,
|
||||
)
|
||||
)
|
||||
108
opencompass/configs/datasets/MedQA/MedQA_llmjudge_gen_3bf756.py
Normal file
108
opencompass/configs/datasets/MedQA/MedQA_llmjudge_gen_3bf756.py
Normal file
@ -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.
|
||||
|
||||
<Original Question Begin>: {question}\n {choices} \n<Original Question End>\n\n
|
||||
<Gold Target Begin>: \n{label}\n<Gold Target End>\n\n
|
||||
<Predicted Answer Begin>: \n{prediction}\n<Predicted End>\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,
|
||||
)
|
||||
)
|
||||
@ -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]
|
||||
@ -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.
|
||||
|
||||
<Original Question Begin>: {question}\n<Original Question End>\n\n
|
||||
<Gold Target Begin>: \n{label}\n<Gold Target End>\n\n
|
||||
<Predicted Answer Begin>: \n{prediction}\n<Predicted End>\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]
|
||||
@ -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='</E>',
|
||||
round=[
|
||||
dict(
|
||||
role='HUMAN',
|
||||
prompt=f'{_hint}\nQuestion: {{input}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nAnswer: '
|
||||
),
|
||||
],
|
||||
),
|
||||
ice_token='</E>',
|
||||
),
|
||||
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,
|
||||
)
|
||||
]
|
||||
@ -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.
|
||||
|
||||
<Original Question Begin>: {input}
|
||||
A) {A}
|
||||
B) {B}
|
||||
C) {C}
|
||||
D) {D}
|
||||
<Original Question End>
|
||||
|
||||
<Gold Target Begin>:
|
||||
{target}
|
||||
<Gold Target End>
|
||||
|
||||
<Predicted Answer Begin>:
|
||||
{prediction}
|
||||
<Predicted End>
|
||||
|
||||
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',
|
||||
)
|
||||
)
|
||||
@ -1,3 +0,0 @@
|
||||
SciEval_lifescience_subsets = [
|
||||
'biology', # 大学生物学
|
||||
]
|
||||
@ -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,
|
||||
))
|
||||
@ -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.
|
||||
<Original Question Begin>: {question}\n {options_str} \n<Original Question End>\n\n
|
||||
<Gold Target Begin>: \n{answer}\n<Gold Target End>\n\n
|
||||
<Predicted Answer Begin>: \n{prediction}\n<Predicted End>\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,
|
||||
)
|
||||
)
|
||||
@ -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),
|
||||
)
|
||||
]
|
||||
@ -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),
|
||||
)
|
||||
]
|
||||
17
opencompass/configs/models/huatuogpt/hf_huatuogpt2_13b.py
Normal file
17
opencompass/configs/models/huatuogpt/hf_huatuogpt2_13b.py
Normal file
@ -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),
|
||||
)
|
||||
]
|
||||
13
opencompass/configs/models/huatuogpt/hf_huatuogpt2_7b.py
Normal file
13
opencompass/configs/models/huatuogpt/hf_huatuogpt2_7b.py
Normal file
@ -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),
|
||||
)
|
||||
]
|
||||
15
opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_7b.py
Normal file
15
opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_7b.py
Normal file
@ -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'),
|
||||
)
|
||||
]
|
||||
15
opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_8b.py
Normal file
15
opencompass/configs/models/huatuogpt/hf_huatuogpt_o1_8b.py
Normal file
@ -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'),
|
||||
)
|
||||
]
|
||||
323
opencompass/datasets/MedCalc_Bench.py
Normal file
323
opencompass/datasets/MedCalc_Bench.py
Normal file
@ -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"(?<!-)\d+", extracted_answer)
|
||||
match = re.findall(r'(-?\d+(\.\d+)?)', extracted_answer)
|
||||
# match = re.findall(r"-?\d+", extracted_answer)
|
||||
if len(match) > 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
|
||||
29
opencompass/datasets/MedQA.py
Normal file
29
opencompass/datasets/MedQA.py
Normal file
@ -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
|
||||
58
opencompass/datasets/ProteinLMBench.py
Normal file
58
opencompass/datasets/ProteinLMBench.py
Normal file
@ -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
|
||||
@ -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<stem>.*?)' # 题干
|
||||
r'(?:A\.)\s*(?P<A>.*?)\s*' # 选项 A
|
||||
r'B\.\s*(?P<B>.*?)\s*' # 选项 B
|
||||
r'C\.\s*(?P<C>.*?)\s*' # 选项 C
|
||||
r'D\.\s*(?P<D>.*?)' # 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
|
||||
@ -97,6 +97,9 @@ from .math_intern import * # noqa: F401, F403
|
||||
from .mathbench import * # noqa: F401, F403
|
||||
from .mbpp import * # noqa: F401, F403
|
||||
from .medbench import * # noqa: F401, F403
|
||||
from .MedCalc_Bench import MedCalc_BenchDataset # noqa: F401
|
||||
from .MedCalc_Bench import MedCalcOfficial_Evaluator # noqa: F401
|
||||
from .MedQA import * # noqa: F401, F403
|
||||
from .MedXpertQA import * # noqa: F401, F403
|
||||
from .mgsm import * # noqa: F401, F403
|
||||
from .mmlu import * # noqa: F401, F403
|
||||
@ -118,6 +121,7 @@ from .OlympiadBench import * # noqa: F401, F403
|
||||
from .OpenFinData import * # noqa: F401, F403
|
||||
from .physics import * # noqa: F401, F403
|
||||
from .piqa import * # noqa: F401, F403
|
||||
from .ProteinLMBench import * # noqa: F401, F403
|
||||
from .py150 import * # noqa: F401, F403
|
||||
from .qasper import * # noqa: F401, F403
|
||||
from .qaspercut import * # noqa: F401, F403
|
||||
@ -130,9 +134,7 @@ from .ruler import * # noqa: F401, F403
|
||||
from .safety import * # noqa: F401, F403
|
||||
from .scibench import ScibenchDataset, scibench_postprocess # noqa: F401, F403
|
||||
from .scicode import * # noqa: F401, F403
|
||||
from .SciKnowEval import SciKnowEvalDataset # noqa: F401, F403
|
||||
from .SciKnowEval import SciKnowEvalEvaluator # noqa: F401, F403
|
||||
from .SciEval_lifescience import SciEvalDataset # noqa: F401
|
||||
from .SciKnowEval import * # noqa: F401, F403
|
||||
from .simpleqa import * # noqa: F401, F403
|
||||
from .siqa import * # noqa: F401, F403
|
||||
from .smolinstruct import * # noqa: F401, F403
|
||||
|
||||
@ -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)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user