Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
json
Size:
100K - 1M
Tags:
text-generation
License:
File size: 7,114 Bytes
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# -*- 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
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