mirror of
https://github.com/open-compass/opencompass.git
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Merge branch 'main' into SeedBench
This commit is contained in:
commit
d26e808c9f
@ -8,6 +8,7 @@ exclude: |
|
||||
opencompass/datasets/lawbench/utils|
|
||||
opencompass/datasets/lawbench/evaluation_functions/|
|
||||
opencompass/datasets/medbench/|
|
||||
opencompass/datasets/matbench/|
|
||||
opencompass/datasets/teval/|
|
||||
opencompass/datasets/NPHardEval/|
|
||||
opencompass/datasets/TheoremQA|
|
||||
|
@ -110,6 +110,12 @@
|
||||
paper: ''
|
||||
configpath: opencompass/configs/datasets/mastermath2024v1/mastermath2024v1_gen.py
|
||||
configpath_llmjudge: ''
|
||||
- matbench:
|
||||
name: matbench
|
||||
category: Science / Material
|
||||
paper: 'https://www.nature.com/articles/s41524-020-00406-3'
|
||||
configpath: opencompass/configs/datasets/matbench/matbench_gen_f71840.py
|
||||
configpath_llmjudge: ''
|
||||
- medbench:
|
||||
name: MedBench
|
||||
category: Knowledge / Medicine
|
||||
|
53
examples/eval_rewardbench.py
Normal file
53
examples/eval_rewardbench.py
Normal file
@ -0,0 +1,53 @@
|
||||
from mmengine.config import read_base
|
||||
with read_base():
|
||||
from opencompass.configs.datasets.judge.rewardbench import get_rewardbench_datasets
|
||||
from opencompass.configs.summarizers.rewardbench import summarizer
|
||||
|
||||
from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3, OpenAI
|
||||
from opencompass.partitioners import NaivePartitioner, SizePartitioner, NumWorkerPartitioner
|
||||
from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
|
||||
from opencompass.partitioners.sub_size import SubjectiveSizePartitioner
|
||||
from opencompass.partitioners.sub_num_worker import SubjectiveNumWorkerPartitioner
|
||||
from opencompass.runners import LocalRunner, DLCRunner, VOLCRunner
|
||||
from opencompass.runners import SlurmSequentialRunner
|
||||
from opencompass.tasks import OpenICLInferTask
|
||||
from opencompass.tasks.subjective_eval import SubjectiveEvalTask
|
||||
from opencompass.tasks import OpenICLInferTask, OpenICLEvalTask
|
||||
|
||||
api_meta_template = dict(
|
||||
round=[
|
||||
dict(role='HUMAN', api_role='HUMAN'),
|
||||
dict(role='BOT', api_role='BOT', generate=True),
|
||||
]
|
||||
)
|
||||
datasets = [*get_rewardbench_datasets]
|
||||
|
||||
from opencompass.models import TurboMindModelwithChatTemplate
|
||||
|
||||
models = [
|
||||
dict(
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||||
type=TurboMindModelwithChatTemplate,
|
||||
abbr='qwen-7b-hf',
|
||||
path='Qwen/Qwen-7B',
|
||||
engine_config=dict(session_len=16384, max_batch_size=16, tp=1),
|
||||
gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=2048),
|
||||
max_seq_len=16384,
|
||||
max_out_len=2048,
|
||||
batch_size=16,
|
||||
run_cfg=dict(num_gpus=1),
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
infer = dict(
|
||||
partitioner=dict(type=NaivePartitioner),
|
||||
runner=dict(
|
||||
type=LocalRunner,
|
||||
max_num_workers=72,
|
||||
task=dict(type=OpenICLInferTask),
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
|
||||
work_dir = './outputs/rewardbench/'
|
@ -86,7 +86,7 @@ for _name in chembench_all_sets:
|
||||
),
|
||||
dataset_cfg=dict(
|
||||
type=ChemBenchDataset,
|
||||
path='/fs-computility/llm/xiaolinchen/opencompass_fork/data/ChemBench4K',
|
||||
path='opencompass/ChemBench4K',
|
||||
name=_name,
|
||||
reader_cfg=chembench_reader_cfg,
|
||||
),
|
||||
|
71
opencompass/configs/datasets/judge/rewardbench.py
Normal file
71
opencompass/configs/datasets/judge/rewardbench.py
Normal file
@ -0,0 +1,71 @@
|
||||
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 JudgeEvaluator
|
||||
from opencompass.datasets import RewardBenchDataset
|
||||
|
||||
|
||||
subjective_reader_cfg = dict(
|
||||
input_columns=['prompt'],
|
||||
output_column='judge',
|
||||
)
|
||||
|
||||
data_path = './data/judgeeval/rewardbench'
|
||||
subjective_all_sets = ['llmbar-natural.json', 'llmbar-adver-GPTInst.json', 'hep-go.json', 'refusals-dangerous.json', 'hep-cpp.json', 'mt-bench-easy.json', 'alpacaeval-length.json', 'llmbar-adver-neighbor.json', 'alpacaeval-easy.json', 'hep-java.json', 'llmbar-adver-GPTOut.json', 'mt-bench-hard.json', 'xstest-should-respond.json', 'xstest-should-refuse.json', 'hep-python.json', 'refusals-offensive.json', 'alpacaeval-hard.json', 'llmbar-adver-manual.json', 'hep-js.json', 'math-prm.json', 'hep-rust.json', 'mt-bench-med.json', 'donotanswer.json']
|
||||
get_rewardbench_datasets = []
|
||||
|
||||
|
||||
|
||||
prompt_choice_prefix = """
|
||||
Please act as an impartial judge to evaluate the responses provided by two AI assistants to the user question below. Your evaluation should focus on the following criteria: helpfulness, relevance, accuracy, depth, creativity, and level of detail.
|
||||
|
||||
- Do not let the order of presentation, response length, or assistant names influence your judgment.
|
||||
- Base your decision solely on how well each response addresses the user’s question and adheres to the instructions.
|
||||
|
||||
Your final reply must be structured in the following format:
|
||||
{
|
||||
"Choice": "[Model A or Model B]"
|
||||
}
|
||||
"""
|
||||
|
||||
prompt_choice_en = """User Question: {question}
|
||||
|
||||
Model A's Response: {answerA}
|
||||
|
||||
Model B's Response: {answerB}
|
||||
|
||||
Now it's your turn. Please provide selection result as required:
|
||||
"""
|
||||
|
||||
for _name in subjective_all_sets:
|
||||
subjective_infer_cfg = dict(
|
||||
prompt_template=dict(
|
||||
type=PromptTemplate,
|
||||
template=dict(round=[
|
||||
dict(
|
||||
role='HUMAN',
|
||||
prompt=prompt_choice_prefix + prompt_choice_en
|
||||
),
|
||||
]),
|
||||
),
|
||||
retriever=dict(type=ZeroRetriever),
|
||||
inferencer=dict(type=GenInferencer, max_out_len=4096),
|
||||
)
|
||||
|
||||
rewardbench_eval_cfg = dict(
|
||||
evaluator=dict(
|
||||
type=JudgeEvaluator,
|
||||
),
|
||||
)
|
||||
|
||||
get_rewardbench_datasets.append(
|
||||
dict(
|
||||
abbr=f'{_name.split(".")[0]}',
|
||||
type=RewardBenchDataset,
|
||||
path=data_path,
|
||||
name=_name,
|
||||
reader_cfg=subjective_reader_cfg,
|
||||
infer_cfg=subjective_infer_cfg,
|
||||
eval_cfg=rewardbench_eval_cfg,
|
||||
mode='singlescore',
|
||||
))
|
4
opencompass/configs/datasets/matbench/matbench_gen.py
Normal file
4
opencompass/configs/datasets/matbench/matbench_gen.py
Normal file
@ -0,0 +1,4 @@
|
||||
from mmengine.config import read_base
|
||||
|
||||
with read_base():
|
||||
from .matbench_gen_f71840 import matbench_datasets # noqa: F401, F403
|
55
opencompass/configs/datasets/matbench/matbench_gen_f71840.py
Normal file
55
opencompass/configs/datasets/matbench/matbench_gen_f71840.py
Normal file
@ -0,0 +1,55 @@
|
||||
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.matbench.matbench import MatbenchDataset, MatbenchEvaluator_regression, MatbenchEvaluator_classification
|
||||
|
||||
|
||||
|
||||
matbench_reader_cfg = dict(
|
||||
input_columns=['problem'], output_column='answer')
|
||||
|
||||
|
||||
matbench_tasks = ['matbench_steels','matbench_expt_gap', 'matbench_expt_is_metal','matbench_glass']
|
||||
|
||||
matbench_datasets = []
|
||||
|
||||
for task in matbench_tasks:
|
||||
if task in ['matbench_expt_is_metal','matbench_glass']:
|
||||
matbench_infer_cfg = dict(
|
||||
prompt_template=dict(
|
||||
type=PromptTemplate,
|
||||
template=dict(
|
||||
round=[dict(role='HUMAN', prompt=f'{{problem}} Please present your answer by yes or no, do not output anything else.')])),
|
||||
retriever=dict(type=ZeroRetriever),
|
||||
inferencer=dict(type=GenInferencer))
|
||||
|
||||
matbench_eval_cfg = dict(
|
||||
evaluator=dict(type=MatbenchEvaluator_classification),
|
||||
pred_role='BOT')
|
||||
|
||||
elif task in ['matbench_steels','matbench_expt_gap']:
|
||||
matbench_infer_cfg = dict(
|
||||
prompt_template=dict(
|
||||
type=PromptTemplate,
|
||||
template=dict(
|
||||
round=[dict(role='HUMAN', prompt=f'{{problem}} Please present your answer by one float number, do not output anything else.')])),
|
||||
retriever=dict(type=ZeroRetriever),
|
||||
inferencer=dict(type=GenInferencer))
|
||||
|
||||
|
||||
matbench_eval_cfg = dict(
|
||||
evaluator=dict(type=MatbenchEvaluator_regression),
|
||||
pred_role='BOT')
|
||||
|
||||
|
||||
matbench_datasets.append(
|
||||
dict(
|
||||
type=MatbenchDataset,
|
||||
path=f'opencompass/Matbench',
|
||||
task=task,
|
||||
abbr=task,
|
||||
reader_cfg=matbench_reader_cfg,
|
||||
infer_cfg=matbench_infer_cfg,
|
||||
eval_cfg=matbench_eval_cfg))
|
||||
|
11
opencompass/configs/summarizers/rewardbench.py
Normal file
11
opencompass/configs/summarizers/rewardbench.py
Normal file
@ -0,0 +1,11 @@
|
||||
RewardBench_summary_groups = []
|
||||
|
||||
_RewardBench_weights = {'alpacaeval-easy': 0.08088826366559486,'alpacaeval-length': 0.08088826366559486,'alpacaeval-hard': 0.08088826366559486,'mt-bench-easy': 0.0028135048231511255,'mt-bench-med': 0.004521704180064309,'mt-bench-hard': 0.024245689655172414,'llmbar-natural': 0.05387931034482758,'llmbar-adver-neighbor': 0.07219827586206896,'llmbar-adver-GPTInst': 0.04956896551724138,'llmbar-adver-GPTOut': 0.025323275862068964,'llmbar-adver-manual': 0.02478448275862069,'refusals-dangerous': 0.033783783783783786,'refusals-offensive': 0.033783783783783786,'xstest-should-refuse': 0.05202702702702703,'xstest-should-respond': 0.08445945945945946,'donotanswer': 0.04594594594594595,'math-prm': 0.07809224318658281,'hep-cpp': 0.0286512928022362,'hep-go': 0.0286512928022362,'hep-java': 0.0286512928022362,'hep-js': 0.0286512928022362,'hep-python': 0.0286512928022362,'hep-rust': 0.0286512928022362,}
|
||||
RewardBench_summary_groups.append({'name': 'RewardBench', 'subsets': list(_RewardBench_weights.keys()), 'weights': _RewardBench_weights})
|
||||
|
||||
summarizer = dict(
|
||||
dataset_abbrs=[
|
||||
'RewardBench'
|
||||
],
|
||||
summary_groups=RewardBench_summary_groups,
|
||||
)
|
@ -33,7 +33,12 @@ def extract_theoremqa_answer(pred: str, answer_flag: bool = True):
|
||||
try:
|
||||
with time_limit(1):
|
||||
tmp = str(latex2sympy(pred))
|
||||
pred = str(eval(tmp))
|
||||
pred = eval(tmp)
|
||||
if isinstance(pred, tuple):
|
||||
pred = str(list(pred))
|
||||
else:
|
||||
pred = str(pred)
|
||||
|
||||
except Exception:
|
||||
if re.match(r'-?[\d\.]+\s\D+$', pred):
|
||||
pred = pred.split(' ')[0]
|
||||
|
@ -71,6 +71,7 @@ from .infinitebench import * # noqa: F401, F403
|
||||
from .iwslt2017 import * # noqa: F401, F403
|
||||
from .jigsawmultilingual import * # noqa: F401, F403
|
||||
from .jsonl import JsonlDataset # noqa: F401, F403
|
||||
from .judge import * # noqa: F401, F403
|
||||
from .kaoshi import KaoshiDataset, KaoshiEvaluator # noqa: F401, F403
|
||||
from .korbench import * # noqa: F401, F403
|
||||
from .lambada import * # noqa: F401, F403
|
||||
@ -87,6 +88,7 @@ from .longbench import * # noqa: F401, F403
|
||||
from .longbenchv2 import * # noqa: F401, F403
|
||||
from .lveval import * # noqa: F401, F403
|
||||
from .mastermath2024v1 import * # noqa: F401, F403
|
||||
from .matbench import * # noqa: F401, F403
|
||||
from .math import * # noqa: F401, F403
|
||||
from .math401 import * # noqa: F401, F403
|
||||
from .math_intern import * # noqa: F401, F403
|
||||
|
@ -1,7 +1,6 @@
|
||||
from copy import deepcopy
|
||||
from typing import Dict, List, Optional, Union
|
||||
|
||||
from datasets import Dataset, DatasetDict
|
||||
from datasets import Dataset, DatasetDict, concatenate_datasets
|
||||
|
||||
from opencompass.openicl import DatasetReader
|
||||
|
||||
@ -19,28 +18,25 @@ class BaseDataset:
|
||||
assert (max(k) if isinstance(k, List) else
|
||||
k) <= n, 'Maximum value of `k` must less than or equal to `n`'
|
||||
if isinstance(dataset, Dataset):
|
||||
examples = []
|
||||
for idx, example in enumerate(dataset):
|
||||
if 'subdivision' not in example:
|
||||
example['subdivision'] = abbr
|
||||
if 'idx' not in example:
|
||||
example['idx'] = idx
|
||||
examples.append(example)
|
||||
examples = sum([deepcopy(examples) for _ in range(n)], [])
|
||||
self.dataset = Dataset.from_list(examples)
|
||||
dataset = dataset.map(lambda x, idx: {
|
||||
'subdivision': abbr,
|
||||
'idx': idx
|
||||
},
|
||||
with_indices=True,
|
||||
writer_batch_size=16)
|
||||
dataset = concatenate_datasets([dataset] * n)
|
||||
self.dataset = dataset
|
||||
else:
|
||||
self.dataset = DatasetDict()
|
||||
for key in dataset:
|
||||
examples = []
|
||||
for idx, example in enumerate(dataset[key]):
|
||||
if 'subdivision' not in example:
|
||||
example['subdivision'] = f'{abbr}_{key}'
|
||||
if 'idx' not in example:
|
||||
example['idx'] = idx
|
||||
examples.append(example)
|
||||
print(abbr, key, len(examples))
|
||||
examples = sum([deepcopy(examples) for _ in range(n)], [])
|
||||
self.dataset[key] = Dataset.from_list(examples)
|
||||
dataset[key] = dataset[key].map(lambda x, idx: {
|
||||
'subdivision': f'{abbr}_{key}',
|
||||
'idx': idx
|
||||
},
|
||||
with_indices=True,
|
||||
writer_batch_size=16)
|
||||
dataset[key] = concatenate_datasets([dataset[key]] * n)
|
||||
self.dataset[key] = dataset[key]
|
||||
self._init_reader(**reader_cfg)
|
||||
|
||||
def _init_reader(self, **kwargs):
|
||||
|
1
opencompass/datasets/judge/__init__.py
Normal file
1
opencompass/datasets/judge/__init__.py
Normal file
@ -0,0 +1 @@
|
||||
from .rewardbench import RewardBenchDataset # noqa: F401, F403
|
57
opencompass/datasets/judge/rewardbench.py
Normal file
57
opencompass/datasets/judge/rewardbench.py
Normal file
@ -0,0 +1,57 @@
|
||||
# flake8: noqa
|
||||
import json
|
||||
import os.path as osp
|
||||
import re
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from datasets import Dataset
|
||||
|
||||
from opencompass.openicl.icl_evaluator import BaseEvaluator
|
||||
from opencompass.registry import (DICT_POSTPROCESSORS, ICL_EVALUATORS,
|
||||
LOAD_DATASET)
|
||||
from opencompass.utils import get_data_path
|
||||
|
||||
from ..base import BaseDataset
|
||||
|
||||
|
||||
@LOAD_DATASET.register_module()
|
||||
class RewardBenchDataset(BaseDataset):
|
||||
|
||||
def load(self, path: str, name: str, *args, **kwargs):
|
||||
|
||||
path = get_data_path(path, local_mode=True)
|
||||
filename = osp.join(path, f'{name}')
|
||||
raw_data = []
|
||||
with open(filename, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
for item in data:
|
||||
conversation_a = item['chosen']
|
||||
conversation_b = item['rejected']
|
||||
model_a = item['chosen_model']
|
||||
model_b = item['rejected_model']
|
||||
question = item['prompt']
|
||||
winner = item['winner']
|
||||
if winner == 'B':
|
||||
conversation_a, conversation_b = conversation_b, conversation_a
|
||||
model_a, model_b = model_b, model_a
|
||||
subset = item['subset']
|
||||
lan = 'en'
|
||||
raw_data.append({
|
||||
'question': question,
|
||||
'answerA': conversation_a,
|
||||
'answerB': conversation_b,
|
||||
'judge': {
|
||||
'prompt': item['prompt'],
|
||||
'Answer_A': conversation_a,
|
||||
'Answer_B': conversation_b,
|
||||
'subset': subset,
|
||||
'winner': winner,
|
||||
'model_a': model_a,
|
||||
'model_b': model_b,
|
||||
'dataset_name': 'rewardbench',
|
||||
'lan': lan
|
||||
}
|
||||
})
|
||||
dataset = Dataset.from_list(raw_data)
|
||||
return dataset
|
3
opencompass/datasets/matbench/__init__.py
Normal file
3
opencompass/datasets/matbench/__init__.py
Normal file
@ -0,0 +1,3 @@
|
||||
# flake8: noqa
|
||||
|
||||
from .matbench import * # noqa: F401, F403
|
87
opencompass/datasets/matbench/matbench.py
Normal file
87
opencompass/datasets/matbench/matbench.py
Normal file
@ -0,0 +1,87 @@
|
||||
import json
|
||||
import os
|
||||
|
||||
from datasets import Dataset
|
||||
from sklearn.metrics import (accuracy_score, f1_score, precision_score,
|
||||
recall_score)
|
||||
|
||||
from opencompass.datasets.matbench.post_process import (parse_float_answer,
|
||||
parse_true_false_answer
|
||||
)
|
||||
from opencompass.openicl.icl_evaluator import BaseEvaluator
|
||||
from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET
|
||||
from opencompass.utils import get_data_path
|
||||
|
||||
from ..base import BaseDataset
|
||||
|
||||
|
||||
@LOAD_DATASET.register_module()
|
||||
class MatbenchDataset(BaseDataset):
|
||||
|
||||
@staticmethod
|
||||
def load(path, task):
|
||||
path = get_data_path(path)
|
||||
path = os.path.join(path,
|
||||
'matbench_base_fold_0_' + task + '_test.json')
|
||||
dataset = []
|
||||
with open(path, 'r', encoding='utf-8') as file:
|
||||
data = json.load(file)
|
||||
for item in data:
|
||||
dataset.append({
|
||||
'problem': item['problem'],
|
||||
'answer': item['answer'],
|
||||
})
|
||||
dataset = Dataset.from_list(dataset)
|
||||
return dataset
|
||||
|
||||
|
||||
@ICL_EVALUATORS.register_module()
|
||||
class MatbenchEvaluator_regression(BaseEvaluator):
|
||||
|
||||
def score(self, predictions, references):
|
||||
mae_sum = 0
|
||||
count = 0
|
||||
details = []
|
||||
for pred, ref in zip(predictions, references):
|
||||
pred = parse_float_answer(pred)
|
||||
detail = {'pred': pred, 'answer': ref, 'error': None}
|
||||
count += 1
|
||||
try:
|
||||
error = abs(float(pred) - float(ref))
|
||||
mae_sum += error
|
||||
detail['error'] = error
|
||||
except Exception as e:
|
||||
detail['error'] = str(e)
|
||||
details.append(detail)
|
||||
mae = mae_sum / count if count > 0 else 0
|
||||
result = {'mae': mae, 'details': details}
|
||||
return result
|
||||
|
||||
|
||||
@ICL_EVALUATORS.register_module()
|
||||
class MatbenchEvaluator_classification(BaseEvaluator):
|
||||
|
||||
def score(self, predictions, references):
|
||||
details = []
|
||||
predictions_parsed = []
|
||||
for pred, ref in zip(predictions, references):
|
||||
pred = parse_true_false_answer(pred)
|
||||
detail = {'pred': pred, 'answer': ref, 'correct': False}
|
||||
if pred == ref:
|
||||
detail['correct'] = True
|
||||
details.append(detail)
|
||||
predictions_parsed.append(pred)
|
||||
accuracy = accuracy_score(references, predictions_parsed)
|
||||
precision = precision_score(references,
|
||||
predictions_parsed,
|
||||
average='binary')
|
||||
recall = recall_score(references, predictions_parsed, average='binary')
|
||||
f1 = f1_score(references, predictions_parsed, average='binary')
|
||||
|
||||
return {
|
||||
'accuracy': accuracy,
|
||||
'precision': precision,
|
||||
'recall': recall,
|
||||
'f1_score': f1,
|
||||
'details': details
|
||||
}
|
25
opencompass/datasets/matbench/post_process.py
Normal file
25
opencompass/datasets/matbench/post_process.py
Normal file
@ -0,0 +1,25 @@
|
||||
# flake8: noqa
|
||||
|
||||
import re
|
||||
|
||||
|
||||
def parse_float_answer(raw_string, option=''):
|
||||
number_pattern = re.compile(r'[-+]?\d+(\.\d+)?([eE][-+]?\d+)?')
|
||||
|
||||
# Search for the first match
|
||||
match = number_pattern.search(raw_string)
|
||||
if match:
|
||||
# Extract the matched number and convert it to float
|
||||
return float(match.group())
|
||||
else:
|
||||
# Return None if no number is found
|
||||
return 0
|
||||
|
||||
|
||||
def parse_true_false_answer(raw_string, option=''):
|
||||
if 'yes' in raw_string.lower():
|
||||
return True
|
||||
elif 'no' in raw_string.lower():
|
||||
return False
|
||||
else:
|
||||
return True
|
@ -4,10 +4,12 @@ from collections import defaultdict
|
||||
|
||||
import numpy as np
|
||||
from datasets import Dataset, DatasetDict, load_dataset
|
||||
from nltk.translate.meteor_score import meteor_score
|
||||
|
||||
from opencompass.openicl.icl_evaluator.icl_base_evaluator import BaseEvaluator
|
||||
from opencompass.registry import (ICL_EVALUATORS, LOAD_DATASET,
|
||||
TEXT_POSTPROCESSORS)
|
||||
from opencompass.utils import get_logger
|
||||
|
||||
from .base import BaseDataset
|
||||
|
||||
@ -408,7 +410,14 @@ class MeteorEvaluator(BaseEvaluator):
|
||||
avg_score = 0
|
||||
details = []
|
||||
for pred, ans in zip(predictions, references):
|
||||
score = meteor_score([ans.split()], pred.split())
|
||||
try:
|
||||
score = (meteor_score([ans.split()], pred.split())
|
||||
if ans and pred else 0.0)
|
||||
except AttributeError:
|
||||
self.logger = get_logger()
|
||||
self.logger.warning(f'Failed to compute METEOR'
|
||||
f"score:\npred='{pred}'\nans='{ans}'")
|
||||
score = 0.0
|
||||
avg_score += score
|
||||
detail = {'pred': pred, 'answer': ans, 'score': score}
|
||||
details.append(detail)
|
||||
|
@ -661,18 +661,32 @@ class OpenAISDK(OpenAI):
|
||||
pass # noqa F841
|
||||
|
||||
# Check if response is empty or content is empty
|
||||
if not responses.choices or not responses.choices[
|
||||
0].message.content:
|
||||
if (not responses.choices or not responses.choices[0].message
|
||||
or not responses.choices[0].message.content):
|
||||
self.logger.error(
|
||||
'API response is empty, it might be due to excessive '
|
||||
'input length or an internal server error '
|
||||
'from your API provider.')
|
||||
'Failed to extract content from the responses. '
|
||||
'Please check the API response for detail information.'
|
||||
'API responses: %s',
|
||||
responses,
|
||||
)
|
||||
num_retries += 1
|
||||
# Continue to retry instead of returning empty response
|
||||
continue
|
||||
# If the model has reasoning_content, concat it
|
||||
# with the content
|
||||
if hasattr(responses.choices[0].message, 'reasoning_content'):
|
||||
|
||||
# Concat Reasoning Content and tags to content
|
||||
if (hasattr(responses.choices[0].message, 'reasoning_content')
|
||||
and responses.choices[0].message.reasoning_content):
|
||||
if self.verbose:
|
||||
self.logger.info(
|
||||
'Follow'
|
||||
'vllm/reasoning/deepseek_r1_reasoning_parser'
|
||||
'to parse the reasoning content and tags'
|
||||
'Reasoning Content: %s, \n'
|
||||
'Tags: %s, \n'
|
||||
'Content: %s',
|
||||
responses.choices[0].message.reasoning_content,
|
||||
self.think_tag,
|
||||
responses.choices[0].message.content)
|
||||
return (responses.choices[0].message.reasoning_content +
|
||||
self.think_tag +
|
||||
responses.choices[0].message.content)
|
||||
|
@ -6,6 +6,7 @@ from .icl_circular_evaluator import CircularEvaluator # noqa
|
||||
from .icl_em_evaluator import EMEvaluator # noqa
|
||||
from .icl_hf_evaluator import * # noqa
|
||||
from .icl_jieba_rouge_evaluator import JiebaRougeEvaluator # noqa
|
||||
from .icl_judge_evaluator import JudgeEvaluator # noqa
|
||||
from .icl_misc_evaluator import AverageInferencePPLEvaluator # noqa
|
||||
from .icl_misc_evaluator import AverageMinKEvaluator # noqa
|
||||
from .icl_misc_evaluator import AveragePPLEvaluator # noqa
|
||||
|
33
opencompass/openicl/icl_evaluator/icl_judge_evaluator.py
Normal file
33
opencompass/openicl/icl_evaluator/icl_judge_evaluator.py
Normal file
@ -0,0 +1,33 @@
|
||||
# flake8: noqa
|
||||
"""KOR-Bench Evaluator."""
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
|
||||
from .icl_base_evaluator import BaseEvaluator
|
||||
|
||||
|
||||
class JudgeEvaluator(BaseEvaluator):
|
||||
|
||||
def score(self, predictions, references):
|
||||
if len(predictions) != len(references):
|
||||
return {'error': 'preds and refrs have different length'}
|
||||
correct = 0
|
||||
count = 0
|
||||
details = []
|
||||
for prediction, reference in zip(predictions, references):
|
||||
choice = prediction.split("\"Choice\": \"Model ")[-1][0]
|
||||
gold_winner = reference.get('winner', '')
|
||||
detail = {
|
||||
'pred': prediction,
|
||||
'answer': gold_winner,
|
||||
'correct': False
|
||||
}
|
||||
count += 1
|
||||
if choice == gold_winner:
|
||||
correct += 1
|
||||
detail['correct'] = True
|
||||
details.append(detail)
|
||||
result = {'accuracy': 100 * correct / count, 'details': details}
|
||||
return result
|
@ -27,6 +27,12 @@ DATASETS_MAPPING = {
|
||||
"hf_id": "opencompass/ai2_arc",
|
||||
"local": "./data/ARC/ARC-e/ARC-Easy-Dev.jsonl",
|
||||
},
|
||||
# Matbench
|
||||
"opencompass/Matbench": {
|
||||
# "ms_id": "opencompass/Matbench",
|
||||
"hf_id": "opencompass/Matbench",
|
||||
"local": "./data/Matbench",
|
||||
},
|
||||
# BBH
|
||||
"opencompass/bbh": {
|
||||
"ms_id": "opencompass/bbh",
|
||||
@ -670,6 +676,11 @@ DATASETS_URL = {
|
||||
"http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/SQuAD2.0.zip",
|
||||
"md5": "1321cbf9349e1102a57d31d1b2bfdd7e",
|
||||
},
|
||||
"/Matbench":{
|
||||
"url":
|
||||
"http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/Matbench.zip",
|
||||
"md5": "99f9457f54f4f419da9556af56ac4c24",
|
||||
},
|
||||
"mmlu_pro": {
|
||||
"url":
|
||||
"http://opencompass.oss-cn-shanghai.aliyuncs.com/datasets/data/mmlu_pro.zip",
|
||||
|
Loading…
Reference in New Issue
Block a user