Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
json
Languages:
Chinese
Size:
10M - 100M
ArXiv:
Tags:
medical
License:
from datasets import DatasetInfo, Features, Split, SplitGenerator, GeneratorBasedBuilder, Value, Sequence | |
import json | |
class MyDataset(GeneratorBasedBuilder): | |
def _info(self): | |
return DatasetInfo( | |
features=Features({ | |
"questions": Sequence(Value("string")), | |
"answers": Sequence(Value("string")) | |
}), | |
supervised_keys=("questions", "answers"), | |
homepage="https://github.com/FreedomIntelligence/Huatuo-26M", | |
citation=''' | |
@misc{li2023huatuo26m, | |
title={Huatuo-26M, a Large-scale Chinese Medical QA Dataset}, | |
author={Jianquan Li and Xidong Wang and Xiangbo Wu and Zhiyi Zhang and Xiaolong Xu and Jie Fu and Prayag Tiwari and Xiang Wan and Benyou Wang}, | |
year={2023}, | |
eprint={2305.01526}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.CL} | |
} | |
''', | |
) | |
def _split_generators(self, dl_manager): | |
train_path = "train_datasets.jsonl" | |
validation_path = "validation_datasets.jsonl" | |
test_path = "test_datasets.jsonl" | |
return [ | |
SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": train_path}), | |
SplitGenerator(name=Split.VALIDATION, gen_kwargs={"filepath": validation_path}), | |
SplitGenerator(name=Split.TEST, gen_kwargs={"filepath": test_path}), | |
] | |
def _generate_examples(self, filepath): | |
with open(filepath, encoding="utf-8") as f: | |
for id_, row in enumerate(f): | |
# Process your data here and create a dictionary with the features. | |
# For example, if your data is in JSON format: | |
data = json.loads(row) | |
yield id_, { | |
"questions": data["questions"], | |
"answers": data["answers"], | |
} | |
if __name__ == '__main__': | |
from datasets import load_dataset | |
dataset = load_dataset("my_dataset.py") | |
print() |