Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
json
Size:
100K - 1M
Tags:
text-generation
License:
# -*- coding: utf-8 -*- | |
""" | |
@author:XuMing(xuming624@qq.com) | |
@description: | |
Natural Language Generation Chinese Corpus.(medical) | |
""" | |
import os | |
import json | |
import datasets | |
_DESCRIPTION = """纯文本数据,中文医疗数据集,包含预训练数据的百科数据,指令微调数据和奖励模型数据。""" | |
_HOMEPAGE = "https://github.com/shibing624/MedicalGPT" | |
_CITATION = "" | |
_LICENSE = "" | |
_BASE_URL = "https://huggingface.co/datasets/shibing624/medical/resolve/main/" | |
# file url: https://huggingface.co/datasets/shibing624/medical/resolve/main/finetune/test_zh_0.json | |
class NewDataset(datasets.GeneratorBasedBuilder): | |
"""Medical Chinese Version""" | |
VERSION = datasets.Version("1.0.1") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig(name="pretrain", version=VERSION, description="pretrain data"), | |
datasets.BuilderConfig(name="finetune", version=VERSION, description="finetune data"), | |
datasets.BuilderConfig(name="reward", version=VERSION, description="reward data"), | |
] | |
def _info(self): | |
if self.config.name == "pretrain": | |
features = datasets.Features( | |
{ | |
"text": datasets.Value("string") | |
} | |
) | |
elif self.config.name == 'finetune': | |
features = datasets.Features( | |
{ | |
"instruction": datasets.Value("string"), | |
"input": datasets.Value("string"), | |
"output": datasets.Value("string") | |
} | |
) | |
elif self.config.name == 'reward': | |
features = datasets.Features( | |
{ | |
"question": datasets.Value("string"), | |
"response_chosen": datasets.Value("string"), | |
"response_rejected": datasets.Value("string") | |
} | |
) | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# This defines the different columns of the dataset and their types | |
features=features, # Here we define them above because they are different between the two configurations | |
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and | |
# specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
# supervised_keys=("sentence", "label"), | |
# Homepage of the dataset for documentation | |
homepage=_HOMEPAGE, | |
# License for the dataset if available | |
license=_LICENSE, | |
# Citation for the dataset | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
data_url = _BASE_URL + self.config.name | |
if self.config.name == 'pretrain': | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepath": dl_manager.download_and_extract(f"{data_url}/train_encyclopedia.json"), | |
"split": "train" | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"filepath": dl_manager.download_and_extract(f"{data_url}/valid_encyclopedia.json"), | |
"split": "dev" | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"filepath": dl_manager.download_and_extract(f"{data_url}/test_encyclopedia.json"), | |
"split": "test" | |
}, | |
), | |
] | |
elif self.config.name == 'finetune': | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepath": dl_manager.download_and_extract([f"{data_url}/train_zh_0.json", f"{data_url}/train_en_1.json"]), | |
"split": "train" | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"filepath": dl_manager.download_and_extract([f"{data_url}/valid_zh_0.json", f"{data_url}/valid_en_1.json"]), | |
"split": "dev" | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"filepath": dl_manager.download_and_extract([f"{data_url}/test_zh_0.json", f"{data_url}/test_en_1.json"]), | |
"split": "test" | |
}, | |
), | |
] | |
elif self.config.name == 'reward': | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepath": dl_manager.download_and_extract(f"{data_url}/train.json"), | |
"split": "train" | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"filepath": dl_manager.download_and_extract(f"{data_url}/valid.json"), | |
"split": "dev" | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"filepath": dl_manager.download_and_extract(f"{data_url}/test.json"), | |
"split": "test" | |
}, | |
), | |
] | |
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
def _generate_examples(self, filepath, split): | |
id = 0 | |
if isinstance(filepath, str): | |
filepath = [filepath] | |
for file in filepath: | |
with open(file, encoding="utf-8") as f: | |
for key, row in enumerate(f): | |
data = json.loads(row) | |
if self.config.name == "pretrain": | |
yield id, { | |
"text": data["text"] | |
} | |
elif self.config.name == 'finetune': | |
yield id, { | |
"instruction": data["instruction"], | |
"input": data["input"], | |
"output": data["output"] | |
} | |
elif self.config.name == 'reward': | |
yield id, { | |
"question": data["question"], | |
"response_chosen": data["response_chosen"], | |
"response_rejected": data["response_rejected"] | |
} | |
id += 1 | |