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import os
import datasets
import pandas as pd
_CITATION = None
_DESCRIPTION = """\
MEDIQA @ NAACL-BioNLP 2021 -- Task 2: Multi-answer summarization
https://sites.google.com/view/mediqa2021
Biomedical Summarization Data
The MEDIQA-AnS Dataset could be used for training.
"""
_HOMEPAGE = "https://github.com/abachaa/MEDIQA2021/tree/main/Task2"
_LICENSE = None
_DATA_URL = "https://huggingface.co/datasets/nbtpj/BioNLP2021/resolve/main/{split_name}.csv"
_SPLIT = ['train_mul', 'train_sig', 'validate', 'test']
class BioNLP2021(datasets.BuilderConfig):
"""BuilderConfig for GLUE."""
def __init__(self, data_url, **kwargs):
"""BuilderConfig for BioNLP2021MAS
Args:
data_url: `string`, url to the dataset (word or raw level)
**kwargs: keyword arguments forwarded to super.
"""
super(BioNLP2021, self).__init__(
version=datasets.Version(
"1.0.0",
),
**kwargs,
)
self.data_url = data_url
class Loader(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.1.0")
BUILDER_CONFIGS = [BioNLP2021(name='BioNLP2021', data_url=_DATA_URL)]
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"question": datasets.Value("string"),
"key": datasets.Value("string"),
"summ_abs": datasets.Value("string"),
"summ_ext": datasets.Value("string"),
# These are the features of your dataset like images, labels ...
# key,question,sum_abs,sum_ext,rating,section,article,text
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
rs = []
for split in _SPLIT:
file= dl_manager.download_and_extract(_DATA_URL.format(split_name=split))
rs.append(
datasets.SplitGenerator(
name=split,
gen_kwargs={"data_file": file, "split": split },
))
return rs
def _generate_examples(self, data_file, split):
"""Yields examples."""
with open(data_file, encoding="utf-8") as f:
f = pd.read_csv(f).to_dict('records')
for idx, row in enumerate(f):
yield idx, row |