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Unverified Commit aa2dd2b5 authored by Fengzhe Zhou's avatar Fengzhe Zhou Committed by GitHub
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[Format] Add config lints (#892)

parent 3dbba119
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configs/datasets/iwslt2017/iwslt2017_gen_b4a814.py
View file @ aa2dd2b5
...@@ -12,7 +12,7 @@ iwslt2017_infer_cfg = dict( ...@@ -12,7 +12,7 @@ iwslt2017_infer_cfg = dict(
ice_template=dict(type='PromptTemplate', ice_template=dict(type='PromptTemplate',
template=dict( template=dict(
begin=[ 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>', '</E>',
], ],
round=[ round=[
......
configs/datasets/jigsawmultilingual/jigsawmultilingual_clp_fe50d8.py
View file @ aa2dd2b5
...@@ -19,10 +19,10 @@ jigsawmultilingual_infer_cfg = dict( ...@@ -19,10 +19,10 @@ jigsawmultilingual_infer_cfg = dict(
type=PromptTemplate, type=PromptTemplate,
template=dict(round=[ template=dict(round=[
dict( dict(
role="HUMAN", role='HUMAN',
prompt="Text: {text}\nQuestion: Does the above text contain " prompt='Text: {text}\nQuestion: Does the above text contain '
"rude, hateful, aggressive, disrespectful or unreasonable " 'rude, hateful, aggressive, disrespectful or unreasonable '
"language?\nAnswer:") 'language?\nAnswer:')
])), ])),
retriever=dict(type=ZeroRetriever), retriever=dict(type=ZeroRetriever),
inferencer=dict(type=CLPInferencer)) inferencer=dict(type=CLPInferencer))
......
configs/datasets/kaoshi/kaoshi_gen_86aca2.py
View file @ aa2dd2b5
...@@ -4,12 +4,12 @@ from opencompass.openicl.icl_prompt_template import PromptTemplate ...@@ -4,12 +4,12 @@ from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever from opencompass.openicl.icl_retriever import ZeroRetriever
prompts = { 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：", '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：", '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:", '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", '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", '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", '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'], 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'],
...@@ -28,44 +28,44 @@ for _type in list(splits_with_type.keys()): ...@@ -28,44 +28,44 @@ for _type in list(splits_with_type.keys()):
_folder = _split.replace('-' + _type, '') _folder = _split.replace('-' + _type, '')
_p = prompts[_type] _p = prompts[_type]
_reader_cfg = { _reader_cfg = {
"input_columns": ['question'], 'input_columns': ['question'],
"output_column": 'answer', 'output_column': 'answer',
} }
_infer_cfg = { _infer_cfg = {
"ice_template": { 'ice_template': {
"type": PromptTemplate, 'type': PromptTemplate,
"template": { 'template': {
"round": [{ 'round': [{
"role": "HUMAN", 'role': 'HUMAN',
"prompt": _p + '{question}' 'prompt': _p + '{question}'
}] }]
}, },
"ice_token": "</E>" 'ice_token': '</E>'
}, },
"retriever": { 'retriever': {
"type": ZeroRetriever 'type': ZeroRetriever
}, },
"inferencer": { 'inferencer': {
"type": GenInferencer, 'type': GenInferencer,
"max_out_len": 1024, 'max_out_len': 1024,
} }
} }
_eval_cfg = { _eval_cfg = {
"evaluator": { 'evaluator': {
"type": KaoshiEvaluator, 'type': KaoshiEvaluator,
"question_type": zh2en[_type], 'question_type': zh2en[_type],
}, },
"pred_role": "BOT", 'pred_role': 'BOT',
} }
_base_path = './data/Kaoshi' _base_path = './data/Kaoshi'
_dataset = { _dataset = {
"type": KaoshiDataset, 'type': KaoshiDataset,
"abbr": "Kaoshi" + _split + '-' + _type, 'abbr': 'Kaoshi' + _split + '-' + _type,
"path": _base_path + '/' + _folder + '/' + _type + ".jsonl", 'path': _base_path + '/' + _folder + '/' + _type + '.jsonl',
"name": zh2en[_type], 'name': zh2en[_type],
"reader_cfg": _reader_cfg, 'reader_cfg': _reader_cfg,
"infer_cfg": _infer_cfg, 'infer_cfg': _infer_cfg,
"eval_cfg": _eval_cfg, 'eval_cfg': _eval_cfg,
} }
kaoshi_datasets.append(_dataset) kaoshi_datasets.append(_dataset)
......
configs/datasets/lawbench/lawbench_one_shot_gen_002588.py
View file @ aa2dd2b5
...@@ -4,26 +4,26 @@ from opencompass.openicl.icl_inferencer import GenInferencer ...@@ -4,26 +4,26 @@ from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import LawBenchDataset from opencompass.datasets import LawBenchDataset
names = [ names = [
["1-1", "article_recitation"], ['1-1', 'article_recitation'],
["1-2", "knowledge_question_answering"], ['1-2', 'knowledge_question_answering'],
["2-1", "document_proofreading"], ['2-1', 'document_proofreading'],
["2-2", "dispute_focus_identification"], ['2-2', 'dispute_focus_identification'],
["2-3", "marital_disputes_identification"], ['2-3', 'marital_disputes_identification'],
["2-4", "issue_topic_identification"], ['2-4', 'issue_topic_identification'],
["2-5", "reading_comprehension"], ['2-5', 'reading_comprehension'],
["2-6", "named_entity_recognition"], ['2-6', 'named_entity_recognition'],
["2-7", "opinion_summarization"], ['2-7', 'opinion_summarization'],
["2-8", "argument_mining"], ['2-8', 'argument_mining'],
["2-9", "event_detection"], ['2-9', 'event_detection'],
["2-10", "trigger_word_extraction"], ['2-10', 'trigger_word_extraction'],
["3-1", "fact_based_article_prediction"], ['3-1', 'fact_based_article_prediction'],
["3-2", "scene_based_article_prediction"], ['3-2', 'scene_based_article_prediction'],
["3-3", "charge_prediction"], ['3-3', 'charge_prediction'],
["3-4", "prison_term_prediction_wo_article"], ['3-4', 'prison_term_prediction_wo_article'],
["3-5", "prison_term_prediction_w_article"], ['3-5', 'prison_term_prediction_w_article'],
["3-6", "case_analysis"], ['3-6', 'case_analysis'],
["3-7", "criminal_damages_calculation"], ['3-7', 'criminal_damages_calculation'],
["3-8", "consultation"], ['3-8', 'consultation'],
] ]
lawbench_datasets = [] lawbench_datasets = []
...@@ -37,7 +37,7 @@ for index, name in names: ...@@ -37,7 +37,7 @@ for index, name in names:
type=PromptTemplate, type=PromptTemplate,
template=dict( template=dict(
round=[ round=[
dict(role="HUMAN", prompt="{instruction}\n{question}"), dict(role='HUMAN', prompt='{instruction}\n{question}'),
] ]
), ),
), ),
......
configs/datasets/lawbench/lawbench_zero_shot_gen_002588.py
View file @ aa2dd2b5
...@@ -4,26 +4,26 @@ from opencompass.openicl.icl_inferencer import GenInferencer ...@@ -4,26 +4,26 @@ from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import LawBenchDataset from opencompass.datasets import LawBenchDataset
names = [ names = [
["1-1", "article_recitation"], ['1-1', 'article_recitation'],
["1-2", "knowledge_question_answering"], ['1-2', 'knowledge_question_answering'],
["2-1", "document_proofreading"], ['2-1', 'document_proofreading'],
["2-2", "dispute_focus_identification"], ['2-2', 'dispute_focus_identification'],
["2-3", "marital_disputes_identification"], ['2-3', 'marital_disputes_identification'],
["2-4", "issue_topic_identification"], ['2-4', 'issue_topic_identification'],
["2-5", "reading_comprehension"], ['2-5', 'reading_comprehension'],
["2-6", "named_entity_recognition"], ['2-6', 'named_entity_recognition'],
["2-7", "opinion_summarization"], ['2-7', 'opinion_summarization'],
["2-8", "argument_mining"], ['2-8', 'argument_mining'],
["2-9", "event_detection"], ['2-9', 'event_detection'],
["2-10", "trigger_word_extraction"], ['2-10', 'trigger_word_extraction'],
["3-1", "fact_based_article_prediction"], ['3-1', 'fact_based_article_prediction'],
["3-2", "scene_based_article_prediction"], ['3-2', 'scene_based_article_prediction'],
["3-3", "charge_prediction"], ['3-3', 'charge_prediction'],
["3-4", "prison_term_prediction_wo_article"], ['3-4', 'prison_term_prediction_wo_article'],
["3-5", "prison_term_prediction_w_article"], ['3-5', 'prison_term_prediction_w_article'],
["3-6", "case_analysis"], ['3-6', 'case_analysis'],
["3-7", "criminal_damages_calculation"], ['3-7', 'criminal_damages_calculation'],
["3-8", "consultation"], ['3-8', 'consultation'],
] ]
lawbench_datasets = [] lawbench_datasets = []
...@@ -37,7 +37,7 @@ for index, name in names: ...@@ -37,7 +37,7 @@ for index, name in names:
type=PromptTemplate, type=PromptTemplate,
template=dict( template=dict(
round=[ round=[
dict(role="HUMAN", prompt="{instruction}\n{question}"), dict(role='HUMAN', prompt='{instruction}\n{question}'),
] ]
), ),
), ),
......
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