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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() |