Datasets:

fever
/

feverous

	"""FEVEROUS dataset."""

	import json
	import textwrap

	import datasets


	class FeverousConfig(datasets.BuilderConfig):
	"""BuilderConfig for FEVER."""

	def __init__(self, homepage: str = None, citation: str = None, base_url: str = None, urls: dict = None, **kwargs):
	"""BuilderConfig for FEVEROUS.

	Args:
	homepage (`str`): Homepage.
	citation (`str`): Citation reference.
	base_url (`str`): Data base URL that precedes all data URLs.
	urls (`dict`): Data URLs (each URL will pe preceded by `base_url`).
	**kwargs: keyword arguments forwarded to super.
	"""
	super().__init__(**kwargs)
	self.homepage = homepage
	self.citation = citation
	self.base_url = base_url
	self.urls = {key: f"{base_url}/{url}" for key, url in urls.items()}


	class FeverOUS(datasets.GeneratorBasedBuilder):
	"""FEVEROUS dataset."""

	BUILDER_CONFIGS = [
	FeverousConfig(
	version=datasets.Version("1.0.0"),
	description=textwrap.dedent(
	"FEVEROUS:\n"
	"FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) is a fact "
	"verification dataset which consists of 87,026 verified claims. Each claim is annotated with evidence "
	"in the form of sentences and/or cells from tables in Wikipedia, as well as a label indicating whether "
	"this evidence supports, refutes, or does not provide enough information to reach a verdict. The "
	"dataset also contains annotation metadata such as annotator actions (query keywords, clicks on page, "
	"time signatures), and the type of challenge each claim poses."
	),
	homepage="https://fever.ai/dataset/feverous.html",
	citation=textwrap.dedent(
	"""\
	@inproceedings{Aly21Feverous,
	author = {Aly, Rami and Guo, Zhijiang and Schlichtkrull, Michael Sejr and Thorne, James and Vlachos, Andreas and Christodoulopoulos, Christos and Cocarascu, Oana and Mittal, Arpit},
	title = {{FEVEROUS}: Fact Extraction and {VERification} Over Unstructured and Structured information},
	eprint={2106.05707},
	archivePrefix={arXiv},
	primaryClass={cs.CL},
	year = {2021}
	}"""
	),
	base_url="https://fever.ai/download/feverous",
	urls={
	datasets.Split.TRAIN: "feverous_train_challenges.jsonl",
	datasets.Split.VALIDATION: "feverous_dev_challenges.jsonl",
	datasets.Split.TEST: "feverous_test_unlabeled.jsonl",
	},
	),
	]

	def _info(self):
	features = {
	"id": datasets.Value("int32"),
	"label": datasets.ClassLabel(names=["SUPPORTS", "REFUTES", "NOT ENOUGH INFO"]),
	"claim": datasets.Value("string"),
	"evidence": [
	{
	"content": [datasets.Value("string")],
	"context": [[datasets.Value("string")]],
	}
	],
	"annotator_operations": [
	{
	"operation": datasets.Value("string"),
	"value": datasets.Value("string"),
	"time": datasets.Value("float"),
	}
	],
	"expected_challenge": datasets.Value("string"),
	"challenge": datasets.Value("string"),
	}
	return datasets.DatasetInfo(
	description=self.config.description,
	features=datasets.Features(features),
	homepage=self.config.homepage,
	citation=self.config.citation,
	)

	def _split_generators(self, dl_manager):
	dl_paths = dl_manager.download_and_extract(self.config.urls)
	return [
	datasets.SplitGenerator(
	name=split,
	gen_kwargs={
	"filepath": dl_paths[split],
	},
	)
	for split in dl_paths.keys()
	]

	def _generate_examples(self, filepath):
	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	# First item in "train" has all values equal to empty strings
	if [value for value in data.values() if value]:
	evidence = data.get("evidence", [])
	if evidence:
	for evidence_set in evidence:
	# Transform "context" from dict to list (analogue to "content")
	evidence_set["context"] = [
	evidence_set["context"][element_id] for element_id in evidence_set["content"]
	]
	yield id_, {
	"id": data.get("id"),
	"label": data.get("label", -1),
	"claim": data.get("claim", ""),
	"evidence": evidence,
	"annotator_operations": data.get("annotator_operations", []),
	"expected_challenge": data.get("expected_challenge", ""),
	"challenge": data.get("challenge", ""),
	}