Unverified Commit aa2dd2b5 authored by Fengzhe Zhou's avatar Fengzhe Zhou Committed by GitHub
Browse files

[Format] Add config lints (#892)

parent 3dbba119
......@@ -8,7 +8,7 @@ from opencompass.datasets import InfiniteBenchcodedebugDataset
InfiniteBench_codedebug_reader_cfg = dict(
input_columns=['context', 'question', 'option_A', 'option_B', 'option_C', 'option_D'],
output_column='answer',
)
InfiniteBench_codedebug_infer_cfg = dict(
......
......@@ -8,7 +8,7 @@ from opencompass.datasets import InfiniteBenchcoderunDataset
InfiniteBench_coderun_reader_cfg = dict(
input_columns=['context', 'func', 'func_call'],
output_column='answer',
)
InfiniteBench_coderun_infer_cfg = dict(
......
......@@ -6,7 +6,7 @@ from opencompass.datasets import InfiniteBenchendiaDataset, InfiniteBenchendiaEv
InfiniteBench_endia_reader_cfg = dict(
input_columns=['context', 'question'],
output_column='answer',
)
InfiniteBench_endia_infer_cfg = dict(
......
......@@ -8,7 +8,7 @@ from opencompass.datasets import InfiniteBenchenmcDataset
InfiniteBench_enmc_reader_cfg = dict(
input_columns=['context', 'question', 'option_A', 'option_B', 'option_C', 'option_D'],
output_column='answer',
)
InfiniteBench_enmc_infer_cfg = dict(
......
......@@ -6,7 +6,7 @@ from opencompass.datasets import InfiniteBenchenqaDataset, LongBenchF1Evaluator
InfiniteBench_enqa_reader_cfg = dict(
input_columns=['context', 'question'],
output_column='answer',
)
InfiniteBench_enqa_infer_cfg = dict(
......
......@@ -7,7 +7,7 @@ from opencompass.datasets import InfiniteBenchensumDataset
InfiniteBench_ensum_reader_cfg = dict(
input_columns=['context'],
output_column='answer',
)
InfiniteBench_ensum_infer_cfg = dict(
......
......@@ -6,7 +6,7 @@ from opencompass.datasets import InfiniteBenchmathcalcDataset, InfiniteBenchmath
InfiniteBench_mathcalc_reader_cfg = dict(
input_columns=['context'],
output_column='answer',
)
InfiniteBench_mathcalc_infer_cfg = dict(
......
......@@ -8,7 +8,7 @@ from opencompass.datasets.infinitebench.utils import InfiniteBench_first_number_
InfiniteBench_mathfind_reader_cfg = dict(
input_columns=['prefix', 'context', 'question'],
output_column='answer',
)
InfiniteBench_mathfind_infer_cfg = dict(
......
......@@ -6,7 +6,7 @@ from opencompass.datasets import InfiniteBenchretrievekvDataset, InfiniteBenchre
InfiniteBench_retrievekv_reader_cfg = dict(
input_columns=['context', 'input'],
output_column='answer',
)
InfiniteBench_retrievekv_infer_cfg = dict(
......
......@@ -8,7 +8,7 @@ from opencompass.datasets.infinitebench.utils import InfiniteBench_first_number_
InfiniteBench_retrievenumber_reader_cfg = dict(
input_columns=['context', 'input'],
output_column='answer',
)
InfiniteBench_retrievenumber_infer_cfg = dict(
......
......@@ -8,7 +8,7 @@ from opencompass.datasets.infinitebench.utils import InfiniteBench_first_number_
InfiniteBench_retrievepasskey_reader_cfg = dict(
input_columns=['context', 'input'],
output_column='answer',
)
InfiniteBench_retrievepasskey_infer_cfg = dict(
......
......@@ -7,7 +7,7 @@ from opencompass.utils.text_postprocessors import general_cn_postprocess
InfiniteBench_zhqa_reader_cfg = dict(
input_columns=['context', 'question'],
output_column='answer',
)
InfiniteBench_zhqa_infer_cfg = dict(
......
......@@ -12,7 +12,7 @@ iwslt2017_infer_cfg = dict(
ice_template=dict(type='PromptTemplate',
template=dict(
begin=[
dict(role='SYSTEM', fallback_role="HUMAN", prompt='Please translate the following English statements to German:'),
dict(role='SYSTEM', fallback_role='HUMAN', prompt='Please translate the following English statements to German:'),
'</E>',
],
round=[
......
......@@ -19,10 +19,10 @@ jigsawmultilingual_infer_cfg = dict(
type=PromptTemplate,
template=dict(round=[
dict(
role="HUMAN",
prompt="Text: {text}\nQuestion: Does the above text contain "
"rude, hateful, aggressive, disrespectful or unreasonable "
"language?\nAnswer:")
role='HUMAN',
prompt='Text: {text}\nQuestion: Does the above text contain '
'rude, hateful, aggressive, disrespectful or unreasonable '
'language?\nAnswer:')
])),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=CLPInferencer))
......
......@@ -4,19 +4,19 @@ from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
prompts = {
"单选题" : "请你做一道单项选择题\n请你一步一步思考并将思考过程写在【解析】和<eoe>之间。你将从A,B,C,D中选出正确的答案,并写在【答案】和<eoa>之间,答案应只包含最终结果,不要添加额外词语。\n例如:【答案】: A <eoa>\n完整的题目回答的格式如下:\n【解析】 ... <eoe>\n【答案】 ... <eoa>\n请你严格按照上述格式作答。\n题目如下:",
"多选题" : "请你做一道多项选择题\n请你一步一步思考并将思考过程写在【解析】和<eoe>之间。你将从多个选项中选出正确的答案,答案可能是一个到多个选项,奇怪将其写在【答案】和<eoa>之间,答案应只包含最终结果,不要添加额外词语。\n例如:【答案】: A D <eoa>\n完整的题目回答的格式如下:\n【解析】 ... <eoe>\n【答案】 ... <eoa>\n请你严格按照上述格式作答。\n题目如下:",
"填空题" : "请解答下面的填空题\n仔细阅读题目,解答其中的问题,请你一步步思考并将思考过程写在【解析】和<eoe>之间。请把你的答案写在【答案】和<eoa>之间,答案应只包含最终结果,不要添加额外词语。\n完整的题目回答格式如下:\n【解析】 ... <eoe>\n【答案】... <eoa>\n请你严格按照上述格式作答。\n题目如下:",
"完形填空" : "请你做一道英语完形填空题,其中包含二十个小题。\n请你一步一步思考。每一题你将从A,B,C,D中选出正确的答案,并写在【答案】和<eoa>之间。\n例如:(1)【答案】 A <eoa>\n(2)【答案】 B <eoa>\n请你严格按照上述格式作答。\n",
"七选五": "请回答下面的问题,将符合题意的五个选项的字母写在【答案】和<eoa>之间,例如:【答案】 A B C D E <eoa>\n请严格按照上述格式作答。题目如下:\n",
"判断题" : "请回答下面的判断题,将你的判断结果写在【答案】和<eoa>之间,若给定表述正确时回答:\n【答案】正确 <eoa>\n 表述错误时回答:\n【答案】错误 <eoa>\n请严格按照上述格式作答。题目如下:\n",
'单选题' : '请你做一道单项选择题\n请你一步一步思考并将思考过程写在【解析】和<eoe>之间。你将从A,B,C,D中选出正确的答案,并写在【答案】和<eoa>之间,答案应只包含最终结果,不要添加额外词语。\n例如:【答案】: A <eoa>\n完整的题目回答的格式如下:\n【解析】 ... <eoe>\n【答案】 ... <eoa>\n请你严格按照上述格式作答。\n题目如下:',
'多选题' : '请你做一道多项选择题\n请你一步一步思考并将思考过程写在【解析】和<eoe>之间。你将从多个选项中选出正确的答案,答案可能是一个到多个选项,奇怪将其写在【答案】和<eoa>之间,答案应只包含最终结果,不要添加额外词语。\n例如:【答案】: A D <eoa>\n完整的题目回答的格式如下:\n【解析】 ... <eoe>\n【答案】 ... <eoa>\n请你严格按照上述格式作答。\n题目如下:',
'填空题' : '请解答下面的填空题\n仔细阅读题目,解答其中的问题,请你一步步思考并将思考过程写在【解析】和<eoe>之间。请把你的答案写在【答案】和<eoa>之间,答案应只包含最终结果,不要添加额外词语。\n完整的题目回答格式如下:\n【解析】 ... <eoe>\n【答案】... <eoa>\n请你严格按照上述格式作答。\n题目如下:',
'完形填空' : '请你做一道英语完形填空题,其中包含二十个小题。\n请你一步一步思考。每一题你将从A,B,C,D中选出正确的答案,并写在【答案】和<eoa>之间。\n例如:(1)【答案】 A <eoa>\n(2)【答案】 B <eoa>\n请你严格按照上述格式作答。\n',
'七选五': '请回答下面的问题,将符合题意的五个选项的字母写在【答案】和<eoa>之间,例如:【答案】 A B C D E <eoa>\n请严格按照上述格式作答。题目如下:\n',
'判断题' : '请回答下面的判断题,将你的判断结果写在【答案】和<eoa>之间,若给定表述正确时回答:\n【答案】正确 <eoa>\n 表述错误时回答:\n【答案】错误 <eoa>\n请严格按照上述格式作答。题目如下:\n',
}
splits_with_type = {'单选题': ['职业-消防', '职业-测绘', '考研-经济', '职业-安全工程', '考研-政治', '职业-建筑', '考研-英语', '职业-教师资格', '职业-证券', '职业-会计', '职业-公务员', '考研-数学', '职业-高项', '考研-临床医学', '职业-银行', '考研-管理类综合', '职业-基金'],
'多选题': ['职业-消防', '职业-测绘', '考研-政治', '职业-建筑', '职业-证券', '职业-会计', '考研-临床医学', '职业-银行'],
'完形填空': ['考研-英语'],
'七选五': ['考研-英语'],
'判断题': ['职业-证券'],
splits_with_type = {'单选题': ['职业-消防', '职业-测绘', '考研-经济', '职业-安全工程', '考研-政治', '职业-建筑', '考研-英语', '职业-教师资格', '职业-证券', '职业-会计', '职业-公务员', '考研-数学', '职业-高项', '考研-临床医学', '职业-银行', '考研-管理类综合', '职业-基金'],
'多选题': ['职业-消防', '职业-测绘', '考研-政治', '职业-建筑', '职业-证券', '职业-会计', '考研-临床医学', '职业-银行'],
'完形填空': ['考研-英语'],
'七选五': ['考研-英语'],
'判断题': ['职业-证券'],
'填空题': ['考研-数学']}
zh2en = {'单选题': 'single_choice', '多选题': 'multi_choice', '完形填空': 'multi_question_choice', '判断题': 'judgment', '填空题': 'cloze', '七选五': 'five_out_of_seven'}
......@@ -28,44 +28,44 @@ for _type in list(splits_with_type.keys()):
_folder = _split.replace('-' + _type, '')
_p = prompts[_type]
_reader_cfg = {
"input_columns": ['question'],
"output_column": 'answer',
'input_columns': ['question'],
'output_column': 'answer',
}
_infer_cfg = {
"ice_template": {
"type": PromptTemplate,
"template": {
"round": [{
"role": "HUMAN",
"prompt": _p + '{question}'
'ice_template': {
'type': PromptTemplate,
'template': {
'round': [{
'role': 'HUMAN',
'prompt': _p + '{question}'
}]
},
"ice_token": "</E>"
'ice_token': '</E>'
},
"retriever": {
"type": ZeroRetriever
'retriever': {
'type': ZeroRetriever
},
"inferencer": {
"type": GenInferencer,
"max_out_len": 1024,
'inferencer': {
'type': GenInferencer,
'max_out_len': 1024,
}
}
_eval_cfg = {
"evaluator": {
"type": KaoshiEvaluator,
"question_type": zh2en[_type],
'evaluator': {
'type': KaoshiEvaluator,
'question_type': zh2en[_type],
},
"pred_role": "BOT",
'pred_role': 'BOT',
}
_base_path = './data/Kaoshi'
_dataset = {
"type": KaoshiDataset,
"abbr": "Kaoshi" + _split + '-' + _type,
"path": _base_path + '/' + _folder + '/' + _type + ".jsonl",
"name": zh2en[_type],
"reader_cfg": _reader_cfg,
"infer_cfg": _infer_cfg,
"eval_cfg": _eval_cfg,
'type': KaoshiDataset,
'abbr': 'Kaoshi' + _split + '-' + _type,
'path': _base_path + '/' + _folder + '/' + _type + '.jsonl',
'name': zh2en[_type],
'reader_cfg': _reader_cfg,
'infer_cfg': _infer_cfg,
'eval_cfg': _eval_cfg,
}
kaoshi_datasets.append(_dataset)
......
......@@ -4,26 +4,26 @@ from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import LawBenchDataset
names = [
["1-1", "article_recitation"],
["1-2", "knowledge_question_answering"],
["2-1", "document_proofreading"],
["2-2", "dispute_focus_identification"],
["2-3", "marital_disputes_identification"],
["2-4", "issue_topic_identification"],
["2-5", "reading_comprehension"],
["2-6", "named_entity_recognition"],
["2-7", "opinion_summarization"],
["2-8", "argument_mining"],
["2-9", "event_detection"],
["2-10", "trigger_word_extraction"],
["3-1", "fact_based_article_prediction"],
["3-2", "scene_based_article_prediction"],
["3-3", "charge_prediction"],
["3-4", "prison_term_prediction_wo_article"],
["3-5", "prison_term_prediction_w_article"],
["3-6", "case_analysis"],
["3-7", "criminal_damages_calculation"],
["3-8", "consultation"],
['1-1', 'article_recitation'],
['1-2', 'knowledge_question_answering'],
['2-1', 'document_proofreading'],
['2-2', 'dispute_focus_identification'],
['2-3', 'marital_disputes_identification'],
['2-4', 'issue_topic_identification'],
['2-5', 'reading_comprehension'],
['2-6', 'named_entity_recognition'],
['2-7', 'opinion_summarization'],
['2-8', 'argument_mining'],
['2-9', 'event_detection'],
['2-10', 'trigger_word_extraction'],
['3-1', 'fact_based_article_prediction'],
['3-2', 'scene_based_article_prediction'],
['3-3', 'charge_prediction'],
['3-4', 'prison_term_prediction_wo_article'],
['3-5', 'prison_term_prediction_w_article'],
['3-6', 'case_analysis'],
['3-7', 'criminal_damages_calculation'],
['3-8', 'consultation'],
]
lawbench_datasets = []
......@@ -37,7 +37,7 @@ for index, name in names:
type=PromptTemplate,
template=dict(
round=[
dict(role="HUMAN", prompt="{instruction}\n{question}"),
dict(role='HUMAN', prompt='{instruction}\n{question}'),
]
),
),
......
......@@ -4,26 +4,26 @@ from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import LawBenchDataset
names = [
["1-1", "article_recitation"],
["1-2", "knowledge_question_answering"],
["2-1", "document_proofreading"],
["2-2", "dispute_focus_identification"],
["2-3", "marital_disputes_identification"],
["2-4", "issue_topic_identification"],
["2-5", "reading_comprehension"],
["2-6", "named_entity_recognition"],
["2-7", "opinion_summarization"],
["2-8", "argument_mining"],
["2-9", "event_detection"],
["2-10", "trigger_word_extraction"],
["3-1", "fact_based_article_prediction"],
["3-2", "scene_based_article_prediction"],
["3-3", "charge_prediction"],
["3-4", "prison_term_prediction_wo_article"],
["3-5", "prison_term_prediction_w_article"],
["3-6", "case_analysis"],
["3-7", "criminal_damages_calculation"],
["3-8", "consultation"],
['1-1', 'article_recitation'],
['1-2', 'knowledge_question_answering'],
['2-1', 'document_proofreading'],
['2-2', 'dispute_focus_identification'],
['2-3', 'marital_disputes_identification'],
['2-4', 'issue_topic_identification'],
['2-5', 'reading_comprehension'],
['2-6', 'named_entity_recognition'],
['2-7', 'opinion_summarization'],
['2-8', 'argument_mining'],
['2-9', 'event_detection'],
['2-10', 'trigger_word_extraction'],
['3-1', 'fact_based_article_prediction'],
['3-2', 'scene_based_article_prediction'],
['3-3', 'charge_prediction'],
['3-4', 'prison_term_prediction_wo_article'],
['3-5', 'prison_term_prediction_w_article'],
['3-6', 'case_analysis'],
['3-7', 'criminal_damages_calculation'],
['3-8', 'consultation'],
]
lawbench_datasets = []
......@@ -37,7 +37,7 @@ for index, name in names:
type=PromptTemplate,
template=dict(
round=[
dict(role="HUMAN", prompt="{instruction}\n{question}"),
dict(role='HUMAN', prompt='{instruction}\n{question}'),
]
),
),
......
......@@ -19,5 +19,5 @@ with read_base():
from .levaltvshowsumm.leval_tvshow_summ_gen import LEval_tvshow_summ_datasets
from .levalscientificqa.leval_scientificqa_gen import LEval_scientificqa_datasets
from .levalreviewsumm.leval_review_summ_gen import LEval_review_summ_datasets
leval_datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
\ No newline at end of file
leval_datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
......@@ -28,7 +28,7 @@ LEval_coursera_infer_cfg = dict(
)
LEval_coursera_eval_cfg = dict(
evaluator=dict(type=AccEvaluator),
evaluator=dict(type=AccEvaluator),
pred_postprocessor=dict(type=first_capital_postprocess_multi),
pred_role='BOT'
)
......
......@@ -27,7 +27,7 @@ LEval_financialqa_infer_cfg = dict(
)
LEval_financialqa_eval_cfg = dict(
evaluator=dict(type=RougeEvaluator),
evaluator=dict(type=RougeEvaluator),
pred_role='BOT'
)
......
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