Datasets:
Tasks:
Text2Text Generation
Sub-tasks:
text-simplification
Languages:
English
Multilinguality:
unknown
Size Categories:
unknown
Language Creators:
unknown
Annotations Creators:
none
Source Datasets:
original
Tags:
License:
File size: 2,899 Bytes
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import csv
import json
import os
import datasets
_CITATION = """\
@inproceedings{devaraj-etal-2021-paragraph,
title = "Paragraph-level Simplification of Medical Texts",
author = "Devaraj, Ashwin and
Marshall, Iain and
Wallace, Byron and
Li, Junyi Jessy",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.395",
doi = "10.18653/v1/2021.naacl-main.395",
pages = "4972--4984",
}
"""
_DESCRIPTION = """\
This dataset measures the ability for a model to simplify paragraphs of medical text through the omission non-salient information and simplification of medical jargon.
"""
_URLs = {
"train": "train.json",
"validation": "validation.json",
"test": "test.json",
}
class Cochrane(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
DEFAULT_CONFIG_NAME = "cochrane-simplification"
def _info(self):
features = datasets.Features(
{
"gem_id": datasets.Value("string"),
"gem_parent_id": datasets.Value("string"),
"source": datasets.Value("string"),
"target": datasets.Value("string"),
"doi": datasets.Value("string"),
"references": [datasets.Value("string")],
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=datasets.info.SupervisedKeysData(
input="source", output="target"
),
homepage="https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts ",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_URLs)
return [
datasets.SplitGenerator(
name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}
)
for spl in ["train", "validation", "test"]
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
reader = json.load(f)
for id_, example in enumerate(reader):
yield id_, {
"gem_id": f"cochrane-simplification-{split}-{id_}",
"gem_parent_id": f"cochrane-simplification-{split}-{id_}",
"source": example["source"],
"target": example["target"],
"doi": example["doi"],
"references": [example["target"]],
}
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