Datasets:

allenai
/

scifact

	"""Scientific fact-checking dataset. Verifies claims based on citation sentences
	using evidence from the cited abstracts."""


	import json

	import datasets


	_CITATION = """\
	@inproceedings{Wadden2020FactOF,
	title={Fact or Fiction: Verifying Scientific Claims},
	author={David Wadden and Shanchuan Lin and Kyle Lo and Lucy Lu Wang and Madeleine van Zuylen and Arman Cohan and Hannaneh Hajishirzi},
	booktitle={EMNLP},
	year={2020},
	}
	"""

	_DESCRIPTION = """\
	SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts, and annotated with labels and rationales.
	"""

	_URL = "https://scifact.s3-us-west-2.amazonaws.com/release/latest/data.tar.gz"


	class ScifactConfig(datasets.BuilderConfig):
	"""BuilderConfig for Scifact"""

	def __init__(self, **kwargs):
	"""

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(ScifactConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


	class Scifact(datasets.GeneratorBasedBuilder):
	"""TODO(scifact): Short description of my dataset."""

	# TODO(scifact): Set up version.
	VERSION = datasets.Version("0.1.0")
	BUILDER_CONFIGS = [
	ScifactConfig(name="corpus", description=" The corpus of evidence documents"),
	ScifactConfig(name="claims", description=" The claims are split into train, test, dev"),
	]

	def _info(self):
	# TODO(scifact): Specifies the datasets.DatasetInfo object
	if self.config.name == "corpus":
	features = {
	"doc_id": datasets.Value("int32"), # The document's S2ORC ID.
	"title": datasets.Value("string"), # The title.
	"abstract": datasets.features.Sequence(
	datasets.Value("string")
	), # The abstract, written as a list of sentences.
	"structured": datasets.Value("bool"), # Indicator for whether this is a structured abstract.
	}
	else:
	features = {
	"id": datasets.Value("int32"), # An integer claim ID.
	"claim": datasets.Value("string"), # The text of the claim.
	"evidence_doc_id": datasets.Value("string"),
	"evidence_label": datasets.Value("string"), # Label for the rationale.
	"evidence_sentences": datasets.features.Sequence(datasets.Value("int32")), # Rationale sentences.
	"cited_doc_ids": datasets.features.Sequence(datasets.Value("int32")), # The claim's "cited documents".
	}

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# datasets.features.FeatureConnectors
	features=datasets.Features(
	features
	# These are the features of your dataset like images, labels ...
	),
	# 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 of the dataset for documentation
	homepage="https://scifact.apps.allenai.org/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(scifact): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	archive = dl_manager.download(_URL)

	if self.config.name == "corpus":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": "data/corpus.jsonl",
	"split": "train",
	"files": dl_manager.iter_archive(archive),
	},
	),
	]
	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": "data/claims_train.jsonl",
	"split": "train",
	"files": dl_manager.iter_archive(archive),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": "data/claims_test.jsonl",
	"split": "test",
	"files": dl_manager.iter_archive(archive),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": "data/claims_dev.jsonl",
	"split": "dev",
	"files": dl_manager.iter_archive(archive),
	},
	),
	]

	def _generate_examples(self, filepath, split, files):
	"""Yields examples."""
	# TODO(scifact): Yields (key, example) tuples from the dataset
	for path, f in files:
	if path == filepath:
	for id_, row in enumerate(f):
	data = json.loads(row.decode("utf-8"))
	if self.config.name == "corpus":
	yield id_, {
	"doc_id": int(data["doc_id"]),
	"title": data["title"],
	"abstract": data["abstract"],
	"structured": data["structured"],
	}
	else:
	if split == "test":
	yield id_, {
	"id": data["id"],
	"claim": data["claim"],
	"evidence_doc_id": "",
	"evidence_label": "",
	"evidence_sentences": [],
	"cited_doc_ids": [],
	}
	else:
	evidences = data["evidence"]
	if evidences:
	for id1, doc_id in enumerate(evidences):
	for id2, evidence in enumerate(evidences[doc_id]):
	yield str(id_) + "_" + str(id1) + "_" + str(id2), {
	"id": data["id"],
	"claim": data["claim"],
	"evidence_doc_id": doc_id,
	"evidence_label": evidence["label"],
	"evidence_sentences": evidence["sentences"],
	"cited_doc_ids": data.get("cited_doc_ids", []),
	}
	else:
	yield id_, {
	"id": data["id"],
	"claim": data["claim"],
	"evidence_doc_id": "",
	"evidence_label": "",
	"evidence_sentences": [],
	"cited_doc_ids": data.get("cited_doc_ids", []),
	}
	break