Datasets:

allenai
/

scitldr

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""Dataset for TLDR: Extreme Summarization of Scientific Documents"""


	import json
	import os

	import datasets


	_SOURCE = "source"
	_TARGET = "target"

	_CITATION = """\
	@article{cachola2020tldr,
	title={{TLDR}: Extreme Summarization of Scientific Documents},
	author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
	journal={arXiv:2004.15011},
	year={2020},
	}
	"""

	_DESCRIPTION = """\
	A new multi-target dataset of 5.4K TLDRs over 3.2K papers.
	SCITLDR contains both author-written and expert-derived TLDRs,
	where the latter are collected using a novel annotation protocol
	that produces high-quality summaries while minimizing annotation burden.
	"""


	_LICENSE = "Apache License 2.0"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLs = {
	"Abstract": "https://raw.githubusercontent.com/allenai/scitldr/master/SciTLDR-Data/SciTLDR-A/",
	"AIC": "https://raw.githubusercontent.com/allenai/scitldr/master/SciTLDR-Data/SciTLDR-AIC/",
	"FullText": "https://raw.githubusercontent.com/allenai/scitldr/master/SciTLDR-Data/SciTLDR-FullText/",
	}

	_TRAIN_DATA = "train.jsonl"
	_TEST_DATA = "test.jsonl"
	_VALID_DATA = "dev.jsonl"


	# There are several preprocessing scripts given in the original SciTLDR GitHub repository to preprocess this data.
	class Scitldr(datasets.GeneratorBasedBuilder):
	"""Dataset for TLDR: Extreme Summarization of Scientific Documents."""

	VERSION = datasets.Version("1.1.0")

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('scitldr', 'Abstract')
	# data = datasets.load_dataset('scitldr', 'AIC')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="Abstract", description="This part contains only abstracts of the paper"),
	datasets.BuilderConfig(
	name="AIC",
	description="This part contains Abstracts, Introduction and Conclusion (AIC) sections of the paper",
	),
	datasets.BuilderConfig(name="FullText", description="This part contains the full text of the paper"),
	]

	DEFAULT_CONFIG_NAME = (
	"Abstract" # It's not mandatory to have a default configuration. Just use one if it make sense.
	)

	def _info(self):
	if self.config.name == "AIC": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"source": datasets.Sequence(datasets.Value("string")),
	"source_labels": datasets.Sequence(datasets.ClassLabel(num_classes=2, names=[0, 1])),
	"rouge_scores": datasets.Sequence(datasets.Value("float32")),
	"paper_id": datasets.Value("string"),
	"ic": datasets.Value("bool_"),
	"target": datasets.features.Sequence(datasets.Value("string"))
	# These are the features of your dataset like images, labels ...
	}
	)
	else:
	features = datasets.Features(
	{
	"source": datasets.Sequence(datasets.Value("string")),
	"source_labels": datasets.Sequence(
	datasets.ClassLabel(num_classes=2, names=["non-oracle", "oracle"])
	),
	"rouge_scores": datasets.Sequence(datasets.Value("float32")),
	"paper_id": datasets.Value("string"),
	"target": datasets.Sequence(datasets.Value("string"))
	# These are the features of your dataset like images, labels ...
	}
	)
	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,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=(_SOURCE, _TARGET),
	# Homepage of the dataset for documentation
	homepage="https://github.com/allenai/scitldr",
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	urls = {
	"train": _URLs[self.config.name] + _TRAIN_DATA,
	"valid": _URLs[self.config.name] + _VALID_DATA,
	"test": _URLs[self.config.name] + _TEST_DATA,
	}
	data_dir = dl_manager.download(urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": os.path.join(data_dir["train"])},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": os.path.join(data_dir["test"])},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": os.path.join(data_dir["valid"])},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	if self.config.name == "AIC":
	yield id_, {
	"source": data["source"],
	"source_labels": data["source_labels"],
	"rouge_scores": data["rouge_scores"],
	"paper_id": data["paper_id"],
	"ic": True if data["ic"] else False,
	"target": data["target"],
	}
	else:
	yield id_, {
	"source": data["source"],
	"source_labels": data["source_labels"],
	"rouge_scores": data["rouge_scores"],
	"paper_id": data["paper_id"],
	"target": data["target"],
	}