Commit c289ecc0 authored by xinghao's avatar xinghao
Browse files

Initial commit

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Pipeline #3004 canceled with stages
import os
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 BBHDataset, BBHEvaluator, bbh_mcq_postprocess, BBHEvaluator_mcq
bbh_reader_cfg = dict(input_columns=['input'], output_column='target')
bbh_multiple_choice_sets = [
'temporal_sequences',
'disambiguation_qa',
'date_understanding',
'tracking_shuffled_objects_three_objects',
'penguins_in_a_table',
'geometric_shapes',
'snarks',
'ruin_names',
'tracking_shuffled_objects_seven_objects',
'tracking_shuffled_objects_five_objects',
'logical_deduction_three_objects',
'hyperbaton',
'logical_deduction_five_objects',
'logical_deduction_seven_objects',
'movie_recommendation',
'salient_translation_error_detection',
'reasoning_about_colored_objects',
]
bbh_free_form_sets = [
'multistep_arithmetic_two',
'navigate',
'dyck_languages',
'word_sorting',
'sports_understanding',
'boolean_expressions',
'object_counting',
'formal_fallacies',
'causal_judgement',
'web_of_lies',
]
bbh_datasets = []
for _name in bbh_multiple_choice_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt=
f"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
)
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
bbh_eval_cfg = dict(
evaluator=dict(type=BBHEvaluator_mcq),
pred_role='BOT',
pred_postprocessor=dict(type=bbh_mcq_postprocess),
dataset_postprocessor=dict(type=bbh_mcq_postprocess))
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
for _name in bbh_free_form_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt=
f"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
)
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
bbh_eval_cfg = dict(evaluator=dict(type=BBHEvaluator), pred_role='BOT')
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
import os
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 BBHDataset, BBHEvaluator, bbh_mcq_postprocess, BBHEvaluator_mcq
bbh_reader_cfg = dict(input_columns=['input'], output_column='target')
bbh_multiple_choice_sets = [
'temporal_sequences',
'disambiguation_qa',
'date_understanding',
'tracking_shuffled_objects_three_objects',
'penguins_in_a_table',
'geometric_shapes',
'snarks',
'ruin_names',
'tracking_shuffled_objects_seven_objects',
'tracking_shuffled_objects_five_objects',
'logical_deduction_three_objects',
'hyperbaton',
'logical_deduction_five_objects',
'logical_deduction_seven_objects',
'movie_recommendation',
'salient_translation_error_detection',
'reasoning_about_colored_objects',
]
bbh_free_form_sets = [
'multistep_arithmetic_two',
'navigate',
'dyck_languages',
'word_sorting',
'sports_understanding',
'boolean_expressions',
'object_counting',
'formal_fallacies',
'causal_judgement',
'web_of_lies',
]
bbh_datasets = []
for _name in bbh_multiple_choice_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt=
f'Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: '
)
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
bbh_eval_cfg = dict(
evaluator=dict(type=BBHEvaluator_mcq),
pred_role='BOT',
pred_postprocessor=dict(type=bbh_mcq_postprocess),
dataset_postprocessor=dict(type=bbh_mcq_postprocess))
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
for _name in bbh_free_form_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt=
f'Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: '
)
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512))
bbh_eval_cfg = dict(evaluator=dict(type=BBHEvaluator), pred_role='BOT')
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
import os
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 BBHDataset, BBHEvaluator, bbh_mcq_postprocess, BBHEvaluator_mcq
bbh_reader_cfg = dict(input_columns=['input'], output_column='target')
bbh_multiple_choice_sets = [
'temporal_sequences',
'disambiguation_qa',
'date_understanding',
'tracking_shuffled_objects_three_objects',
'penguins_in_a_table',
'geometric_shapes',
'snarks',
'ruin_names',
'tracking_shuffled_objects_seven_objects',
'tracking_shuffled_objects_five_objects',
'logical_deduction_three_objects',
'hyperbaton',
'logical_deduction_five_objects',
'logical_deduction_seven_objects',
'movie_recommendation',
'salient_translation_error_detection',
'reasoning_about_colored_objects',
]
bbh_free_form_sets = [
'multistep_arithmetic_two',
'navigate',
'dyck_languages',
'word_sorting',
'sports_understanding',
'boolean_expressions',
'object_counting',
'formal_fallacies',
'causal_judgement',
'web_of_lies',
]
bbh_datasets = []
for _name in bbh_multiple_choice_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=f"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512, stopping_criteria=['Q:']))
bbh_eval_cfg = dict(
evaluator=dict(type=BBHEvaluator_mcq),
pred_role='BOT',
pred_postprocessor=dict(type=bbh_mcq_postprocess),
dataset_postprocessor=dict(type=bbh_mcq_postprocess))
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
for _name in bbh_free_form_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=f"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=512, stopping_criteria=['Q:']))
bbh_eval_cfg = dict(evaluator=dict(type=BBHEvaluator), pred_role='BOT')
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
import os
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 BBHDataset, BBHEvaluator, bbh_mcq_postprocess, BBHEvaluator_mcq
bbh_reader_cfg = dict(input_columns=['input'], output_column='target')
bbh_multiple_choice_sets = [
'temporal_sequences',
'disambiguation_qa',
'date_understanding',
'tracking_shuffled_objects_three_objects',
'penguins_in_a_table',
'geometric_shapes',
'snarks',
'ruin_names',
'tracking_shuffled_objects_seven_objects',
'tracking_shuffled_objects_five_objects',
'logical_deduction_three_objects',
'hyperbaton',
'logical_deduction_five_objects',
'logical_deduction_seven_objects',
'movie_recommendation',
'salient_translation_error_detection',
'reasoning_about_colored_objects',
]
bbh_free_form_sets = [
'multistep_arithmetic_two',
'navigate',
'dyck_languages',
'word_sorting',
'sports_understanding',
'boolean_expressions',
'object_counting',
'formal_fallacies',
'causal_judgement',
'web_of_lies',
]
bbh_datasets = []
for _name in bbh_multiple_choice_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt=
f"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
)
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
bbh_eval_cfg = dict(
evaluator=dict(type=BBHEvaluator_mcq),
pred_role='BOT',
pred_postprocessor=dict(type=bbh_mcq_postprocess),
dataset_postprocessor=dict(type=bbh_mcq_postprocess))
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
for _name in bbh_free_form_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt=
f"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
)
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
bbh_eval_cfg = dict(evaluator=dict(type=BBHEvaluator), pred_role='BOT')
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
from mmengine.config import read_base
with read_base():
from .bbh_llmjudge_gen_b5bdf1 import bbh_datasets # noqa: F401, F403
\ No newline at end of file
# flake8: noqa
import os
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 BBHDataset
from opencompass.datasets.generic import generic_llmjudge_academic_postprocess
bbh_reader_cfg = dict(input_columns=['input'], output_column='target')
bbh_multiple_choice_sets = [
'temporal_sequences',
'disambiguation_qa',
'date_understanding',
'tracking_shuffled_objects_three_objects',
'penguins_in_a_table',
'geometric_shapes',
'snarks',
'ruin_names',
'tracking_shuffled_objects_seven_objects',
'tracking_shuffled_objects_five_objects',
'logical_deduction_three_objects',
'hyperbaton',
'logical_deduction_five_objects',
'logical_deduction_seven_objects',
'movie_recommendation',
'salient_translation_error_detection',
'reasoning_about_colored_objects',
]
bbh_free_form_sets = [
'multistep_arithmetic_two',
'navigate',
'dyck_languages',
'word_sorting',
'sports_understanding',
'boolean_expressions',
'object_counting',
'formal_fallacies',
'causal_judgement',
'web_of_lies',
]
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.
5. If the prediction is given with \\boxed{}, please ignore the \\boxed{} and only judge whether the candidate's answer is consistent with the standard answer.
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>: \n{input}\n<Original Question End>\n\n
<Gold Target Begin>: \n{target}\n<Gold Target End>\n\n
<Predicted Answer Begin>: \n{prediction}\n<Predicted End>\n\n
Judging the correctness of candidates' answers:
""".strip()
bbh_sets = bbh_multiple_choice_sets + bbh_free_form_sets
# For zero shot inference in bbh
bbh_datasets = []
for _name in bbh_sets:
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt=f"Question: {{input}}\n You must give your final answer by starting with 'So the answer is' "
)
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
bbh_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=BBHDataset,
name=_name,
path='opencompass/bbh',
reader_cfg=bbh_reader_cfg,
),
judge_cfg=dict(),
dict_postprocessor=dict(type=generic_llmjudge_academic_postprocess, metric_name='score'),
),
pred_role='BOT',
)
bbh_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy())
)
# For original 3 shot inference in bbh
bbh_3_shot_datasets = []
for _name in bbh_sets:
with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
_hint = f.read()
bbh_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt=f"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
)
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
bbh_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=BBHDataset,
name=_name,
path='opencompass/bbh',
reader_cfg=bbh_reader_cfg,
),
judge_cfg=dict(),
dict_postprocessor=dict(type=generic_llmjudge_academic_postprocess, metric_name='score'),
),
pred_role='BOT',
)
bbh_3_shot_datasets.append(
dict(
type=BBHDataset,
path='opencompass/bbh',
name=_name,
abbr='bbh-' + _name,
reader_cfg=bbh_reader_cfg,
infer_cfg=bbh_infer_cfg.copy(),
eval_cfg=bbh_eval_cfg.copy()))
settings = [
('temporal_sequences', 'mcq'),
('disambiguation_qa', 'mcq'),
('date_understanding', 'mcq'),
('tracking_shuffled_objects_three_objects', 'mcq'),
('penguins_in_a_table', 'mcq'),
('geometric_shapes', 'mcq'),
('snarks', 'mcq'),
('ruin_names', 'mcq'),
('tracking_shuffled_objects_seven_objects', 'mcq'),
('tracking_shuffled_objects_five_objects', 'mcq'),
('logical_deduction_three_objects', 'mcq'),
('hyperbaton', 'mcq'),
('logical_deduction_five_objects', 'mcq'),
('logical_deduction_seven_objects', 'mcq'),
('movie_recommendation', 'mcq'),
('salient_translation_error_detection', 'mcq'),
('reasoning_about_colored_objects', 'mcq'),
('multistep_arithmetic_two', 'free_form'),
('navigate', 'free_form'),
('dyck_languages', 'free_form'),
('word_sorting', 'free_form'),
('sports_understanding', 'free_form'),
('boolean_expressions', 'free_form'),
('object_counting', 'free_form'),
('formal_fallacies', 'free_form'),
('causal_judgement', 'free_form'),
('web_of_lies', 'free_form'),
]
from mmengine.config import read_base
with read_base():
from .bigcodebench_full_complete_gen_faf748 import bigcodebench_full_complete_datasets # noqa: F401, F403
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (BigCodeBenchDataset, BigCodeBenchEvaluator)
bigcodebench_full_reader_cfg = dict(
input_columns=['complete_prompt'],
output_column='test',
)
bigcodebench_full_infer_cfg = dict(prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[dict(role='system', fallback_role='HUMAN', prompt='')],
round=[
dict(role='HUMAN', prompt='{complete_prompt}'),
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer,
max_out_len=1024))
bigcodebench_full_eval_cfg = dict(
evaluator=dict(
type=BigCodeBenchEvaluator,
release_version='v0.1.2',
eval_type='complete',
# remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/',
remote_execute_api=
'https://opencompass-opencompass-bigcodebench-evaluator.hf.space', # noqa: E501
dataset_version='full',
),
pred_role='BOT',
)
bigcodebench_full_complete_datasets = [
dict(abbr='bigcodebench_full_complete',
type=BigCodeBenchDataset,
path='opencompass/bigcodebench',
reader_cfg=bigcodebench_full_reader_cfg,
infer_cfg=bigcodebench_full_infer_cfg,
eval_cfg=bigcodebench_full_eval_cfg,
release_version='v0.1.2')
]
from mmengine.config import read_base
with read_base():
from .bigcodebench_full_instruct_gen_8815eb import bigcodebench_full_instruct_datasets # noqa: F401, F403
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (BigCodeBenchDataset, BigCodeBenchEvaluator)
bigcodebench_full_reader_cfg = dict(
input_columns=['instruct_prompt'],
output_column='test',
)
bigcodebench_full_infer_cfg = dict(prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[dict(role='system', fallback_role='HUMAN', prompt='')],
round=[
dict(role='HUMAN', prompt='{instruct_prompt}'),
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer,
max_out_len=8192))
bigcodebench_full_eval_cfg = dict(
evaluator=dict(
type=BigCodeBenchEvaluator,
release_version='v0.1.2',
eval_type='instruct',
# remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/',
remote_execute_api=
'https://opencompass-opencompass-bigcodebench-evaluator.hf.space', # noqa: E501
dataset_version='full',
),
pred_role='BOT',
)
bigcodebench_full_instruct_datasets = [
dict(abbr='bigcodebench_full_instruct',
type=BigCodeBenchDataset,
path='opencompass/bigcodebench',
reader_cfg=bigcodebench_full_reader_cfg,
infer_cfg=bigcodebench_full_infer_cfg,
eval_cfg=bigcodebench_full_eval_cfg,
release_version='v0.1.2')
]
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (BigCodeBenchDataset, BigCodeBenchEvaluator)
bigcodebench_full_reader_cfg = dict(
input_columns=['instruct_prompt'],
output_column='test',
)
bigcodebench_full_infer_cfg = dict(prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[dict(role='system', fallback_role='HUMAN', prompt='')],
round=[
dict(role='HUMAN', prompt='{instruct_prompt}'),
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
bigcodebench_full_eval_cfg = dict(
evaluator=dict(
type=BigCodeBenchEvaluator,
release_version='v0.1.2',
eval_type='instruct',
# remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/',
remote_execute_api=
'https://opencompass-opencompass-bigcodebench-evaluator.hf.space', # noqa: E501
dataset_version='full',
),
pred_role='BOT',
)
bigcodebench_full_instruct_datasets = [
dict(abbr='bigcodebench_full_instruct',
type=BigCodeBenchDataset,
path='opencompass/bigcodebench',
reader_cfg=bigcodebench_full_reader_cfg,
infer_cfg=bigcodebench_full_infer_cfg,
eval_cfg=bigcodebench_full_eval_cfg,
release_version='v0.1.2',
n=5,
k=3)
]
from mmengine.config import read_base
with read_base():
from .bigcodebench_hard_instruct_gen import bigcodebench_hard_instruct_datasets
from .bigcodebench_hard_complete_gen import bigcodebench_hard_complete_datasets
bigcodebench_hard_datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
from mmengine.config import read_base
with read_base():
from .bigcodebench_hard_complete_gen_faf748 import bigcodebench_hard_complete_datasets # noqa: F401, F403
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (BigCodeBenchDataset, BigCodeBenchEvaluator)
bigcodebench_hard_reader_cfg = dict(
input_columns=['complete_prompt'],
output_column='test',
)
bigcodebench_hard_infer_cfg = dict(prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[dict(role='system', fallback_role='HUMAN', prompt='')],
round=[
dict(role='HUMAN', prompt='{complete_prompt}'),
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer))
bigcodebench_hard_eval_cfg = dict(
evaluator=dict(
type=BigCodeBenchEvaluator,
release_version='v0.1.2',
eval_type='complete',
# remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/',
remote_execute_api=
'https://opencompass-opencompass-bigcodebench-evaluator.hf.space', # noqa: E501
dataset_version='hard',
),
pred_role='BOT',
)
bigcodebench_hard_complete_datasets = [
dict(
abbr='bigcodebench_hard_complete',
type=BigCodeBenchDataset,
path='opencompass/bigcodebench',
reader_cfg=bigcodebench_hard_reader_cfg,
infer_cfg=bigcodebench_hard_infer_cfg,
eval_cfg=bigcodebench_hard_eval_cfg,
release_version='v0.1.2',
dataset_version='hard',
)
]
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (BigCodeBenchDataset, BigCodeBenchEvaluator)
bigcodebench_hard_reader_cfg = dict(
input_columns=['complete_prompt'],
output_column='test',
)
bigcodebench_hard_infer_cfg = dict(prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[dict(role='system', fallback_role='HUMAN', prompt='')],
round=[
dict(role='HUMAN', prompt='{complete_prompt}'),
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer,
max_out_len=1024))
bigcodebench_hard_eval_cfg = dict(
evaluator=dict(
type=BigCodeBenchEvaluator,
release_version='v0.1.2',
eval_type='complete',
# remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/',
remote_execute_api=
'https://opencompass-opencompass-bigcodebench-evaluator.hf.space', # noqa: E501
dataset_version='hard',
),
pred_role='BOT',
)
bigcodebench_hard_complete_datasets = [
dict(
abbr='bigcodebench_hard_complete',
type=BigCodeBenchDataset,
path='opencompass/bigcodebench',
reader_cfg=bigcodebench_hard_reader_cfg,
infer_cfg=bigcodebench_hard_infer_cfg,
eval_cfg=bigcodebench_hard_eval_cfg,
release_version='v0.1.2',
dataset_version='hard',
)
]
from mmengine.config import read_base
with read_base():
from .bigcodebench_hard_instruct_gen_c3d5ad import bigcodebench_hard_instruct_datasets # noqa: F401, F403
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (BigCodeBenchDataset, BigCodeBenchEvaluator)
bigcodebench_hard_reader_cfg = dict(
input_columns=['instruct_prompt'],
output_column='test',
)
bigcodebench_hard_infer_cfg = dict(prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[dict(role='system', fallback_role='HUMAN', prompt='')],
round=[
dict(role='HUMAN', prompt='{instruct_prompt}'),
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer,
max_out_len=8192))
bigcodebench_hard_eval_cfg = dict(
evaluator=dict(
type=BigCodeBenchEvaluator,
release_version='v0.1.2',
eval_type='instruct',
# remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/',
remote_execute_api=
'https://opencompass-opencompass-bigcodebench-evaluator.hf.space', # noqa: E501
dataset_version='hard',
),
pred_role='BOT',
)
bigcodebench_hard_instruct_datasets = [
dict(
abbr='bigcodebench_hard_instruct',
type=BigCodeBenchDataset,
path='opencompass/bigcodebench',
reader_cfg=bigcodebench_hard_reader_cfg,
infer_cfg=bigcodebench_hard_infer_cfg,
eval_cfg=bigcodebench_hard_eval_cfg,
release_version='v0.1.2',
dataset_version='hard',
)
]
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (BigCodeBenchDataset, BigCodeBenchEvaluator)
bigcodebench_hard_reader_cfg = dict(
input_columns=['instruct_prompt'],
output_column='test',
)
bigcodebench_hard_infer_cfg = dict(prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[dict(role='system', fallback_role='HUMAN', prompt='')],
round=[
dict(role='HUMAN', prompt='{instruct_prompt}'),
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer)
)
bigcodebench_hard_eval_cfg = dict(
evaluator=dict(
type=BigCodeBenchEvaluator,
release_version='v0.1.2',
eval_type='instruct',
# remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/',
remote_execute_api=
'https://opencompass-opencompass-bigcodebench-evaluator.hf.space', # noqa: E501
dataset_version='hard',
),
pred_role='BOT',
)
bigcodebench_hard_instruct_datasets = [
dict(
abbr='bigcodebench_hard_instruct',
type=BigCodeBenchDataset,
path='opencompass/bigcodebench',
reader_cfg=bigcodebench_hard_reader_cfg,
infer_cfg=bigcodebench_hard_infer_cfg,
eval_cfg=bigcodebench_hard_eval_cfg,
release_version='v0.1.2',
dataset_version='hard',
)
]
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (BigCodeBenchDataset, BigCodeBenchEvaluator)
bigcodebench_hard_reader_cfg = dict(
input_columns=['instruct_prompt'],
output_column='test',
)
bigcodebench_hard_infer_cfg = dict(prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[dict(role='system', fallback_role='HUMAN', prompt='')],
round=[
dict(role='HUMAN', prompt='{instruct_prompt}'),
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer)
)
bigcodebench_hard_eval_cfg = dict(
evaluator=dict(
type=BigCodeBenchEvaluator,
release_version='v0.1.2',
eval_type='instruct',
# remote_execute_api='https://bigcode-bigcodebench-evaluator.hf.space/',
remote_execute_api=
'https://opencompass-opencompass-bigcodebench-evaluator.hf.space', # noqa: E501
dataset_version='hard',
),
pred_role='BOT',
)
bigcodebench_hard_instruct_datasets = [
dict(
abbr='bigcodebench_hard_instruct',
type=BigCodeBenchDataset,
path='opencompass/bigcodebench',
reader_cfg=bigcodebench_hard_reader_cfg,
infer_cfg=bigcodebench_hard_infer_cfg,
eval_cfg=bigcodebench_hard_eval_cfg,
release_version='v0.1.2',
dataset_version='hard',
n=5,
k=3
)
]
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